Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 11, pp.717-724.

* Clifton, P. et al. 2010. A comprehensive survey of data mining-based fraud detection research. International Conference on Intelligent Computation Technology and Automation, Vol. 1, pp.50-53.

* H.Cai et al. China's land market auction evidence of corruption. NBER working paper 15067, June 2009. http://www.nber.org/papers/w15067

## Quiz

1. Please analyze following case studies, assess the risks of financial security and suggest suitable technological solutions.

### Fraud detection on GST collection

Agents: Business analyst, Auditor, Buying agents, Selling agents;

Objectives: Fraud detection on tax (GST) collection;

* Life science / pharmaceuticals

* Retail

* Consumer goods

Constraints:

* GST collection and implementation mechanism,

* Availability of information and communication infrastructure;

System: Analytics, Web enabled ERP & SCM system;

Input: Transactional data (e.g. sales, invoice), inventory data, price;

Procedure:

* Collect sales transactions data;

* Audit sales invoices or bills: Price, GST, Discount;

* Verify authentication, authorization, correct identification, fairness, correctness, transparency and accountability in sales transactions;

* Collect feedback of the buying agents on quality of products.

Output:

* Loss in tax collection;

* Detect fraudulent transactions;

* Detect sales and distribution of fake drugs / poor quality medicines;

### Problem 1: Analytics on fraud detection against GST collection in life-science / pharmaceutical industry;

Research methodology: Primary data analysis; data collected through interviewing a patient;

Observations: Malicious Business models are promoting innovation, production, sales and distribution of fake drugs (with vague compositions) causing serious side effects and flawed medical devices today: is it not medical ragging or bioterrorism? Following observations, feedback and experience are furnished regarding possible side effects and ineffectiveness of following few medicines prescribed by an authorized doctor for the treatment of diabetic problem and pain in swollen
scrotum as a result of side effects. Is it really medical ragging or bio-terrorism or sybil attack in sales and distribution of healthcare sector?

<table><caption>Table 8.1 : Primary data used for fraud detection</caption><thead><tr><td>SL No.</td><td>Medicine</td><td>GST Details</td><td>Remarks</td></tr></thead><tbody><tr><td>1</td><td>Tenelife-M</td><td>Bill given without giving GST details</td><td>Discount : 10%; bill given, no GST mentioned in the bill from medicine shop 1; no clarity in prescription</td></tr><tr><td>2</td><td>Icobal-Forte</td><td>No bill given</td><td>Discount : 5%; no bill given from medicine shop 2</td></tr><tr><td>3</td><td>Dolowin</td><td>Not known</td><td>Given at free of cost at polyclinic; no idea of GST</td></tr><tr><td>4</td><td>Clavimox 625</td><td>No bill given</td><td>Discount : 5%; no bill given from medicine shop 2; no clarity in prescription due to bad hand writing</td></tr><tr><td>5</td><td>DOTKLOR</td><td>Not applicable</td><td>Free sample given at free of cost at polyclinic</td></tr></tbody></table>

GST collection details against medicines prescribed :

* Tenelife-M (Teneligipin 20 mg and Metformin Hydrochloride 500 mg.); Discount : 10%; bill given, no GST mentioned in the bill from medicine shop 1;

* Icobal-Forte : Discount : 5%; no bill given from medicine shop 2;

* Dolowin : Given at free of cost at polyclinic; no idea of GST

* Clavimox 625 : Discount : 5%; no bill given from medicine shop 2;

* DOTKLOR : Free sample given at free of cost at polyclinic.

Note : The name of doctor, polyclinic and medicine retail outlets, location and details of bills are private data.

Feedback after consuming aforesaid medicines:

* No relief of pain

* No reduction in blood sugar level

* Possible infection in urinary bladder and natural activities

* Diabetic gangrene (as diagnosed)

* Physical weakness and deterioration of health conditions of the patient.

Is it not essential in taking necessary corrective actions and precautions in drug discovery and irrational mechanical innovation from the perspectives of sustainable life-science, biotechnology and pharmacy in future ignoring economic pressure in healthcare sector. It is a critical issue of business modeling and system dynamics and Corporate Social Responsibilities (CSR) in formulation of stressless public policy.
## Recommended Mechanism Of GST Collection

* Adoption of information system for correct billing with detailed computation of GST and discounts;

* Use computers: desktops / laptops

* Use tablets

* Use smart phones

* Production of valid prescriptions of doctors essential with clarity (e.g. fairness, correctness, completeness)

* Mobile messages

* Exception:

* Emergency cases (e.g. strokes) : Self declaration by the patients or their relatives through letters;

* Critical drugs (e.g. sleeping pills, pain killers) : Self declaration by the patients or their relatives through letters;

* Audit of fraudulent transactions and corruptions, use of fake drugs

* Real time Inventory control through CPFR (Collaborative planning, forecasting and replenishment), S&OP

## Quiz

* What is the scope of financial security technology?

* What is the dominant design of these technologies?

* What are the basic elements of the system architecture?

* What do you mean by technology security? How can You verify the security intelligence?

* What are the strategic moves of technology innovation, adoption and diffusion? What is the outcome of technology life-cycle analysis and SWOT analysis of online and offline technologies?

* How to manage resources for innovation project of these technologies?

* What should be the talent management strategy? What are the skills, leadership style and support demanded by the technological innovation?

* How do You manage financial security technology innovation project efficiently? What should be the shared vision, common goals and communication protocols? How can you ensure a perfect fit among '7-S' elements?
SESSION 9: SOCIAL SECURITY - SOCIAL ENGINEERING, CANCER OF MIND, INFORMATION, MEDIA AND ENTERTAINMENT TECHNOLOGIES

Event: Technology for humanity and global security summit

Venue: Social security hall, Technology park : Sanada

Time Schedule: 3 p.m. – 6 p.m., 17.8.2020

Agents: Representatives of various global organizations (Global nations, global childcare organization, global peace organization, global health organization, global financial organization, global economic forum), Technology management experts from science and technology forums, Social engineers, Social scientists, representatives and ministers from the departments of child, women and family welfare of developed, developing and underdeveloped countries, CEOs of social media corporations, business development consultants of information, media and entertainments sectors, representatives from NGOs.

Topic of discussion and key focus areas: social security, social networking, social engineering, cancer of mind.

Keynote speakers: Prof. S. Nelson, Prof. Nancy Robson, Prof. R. Subramanium, Prof. P. Ronaldinho, Dr. Gremy Smith.

1. SCOPE

Scope Analytics

Agents: Social scientists, system analysts, business analysts;

Moves: Critical success factors analysis, Requirements management;

Social security parameters: Define a set of sustainable development goals for social security.

religious and cultural security, gender equality, child security, women's empowerment, safety of senior citizen, peace, justice, partnership, job security (decent work) and regulatory compliance through strong institutions.

Poverty control of refugees and migrants /* scope analytics, chapter 1*/

Safety against war, act of terrorism, crime, bioterrorism and natural disaster;

Healthcare, ageing population, technological transition, higher public expectations, employment of young workforce, labour markets and digital economy, protection of migrant workforce, inequalities and discrimination, new risks, shocks and extreme events;

Prof. Nancy Robson has started the session highlighting the aforesaid scope analytics. She is giving a multidimensional view of social security in terms of religious and cultural security, gender equality, child security, women's empowerment, safety of senior citizen, peace, justice, partnership, job security (decent work) and regulatory compliance through strong institutions. The key focus areas of this session is the emerging digital technologies such as social networking,
social engineering and stress management and also evolution of social security technologies through deep analytics. The expert panel have planned to analyze a case study on cancer of mind at the end of the session. Social security is deeply related to human psychological problem, mental stress and depression of the kids, boys, girls, young people, men and women and senior citizen.

## 2. SYSTEM

### System Analytics

**Agents**: System analysts, business analysts, Social scientists;

**Moves**: Requirements engineering, system design, coding, prototype testing, erection, installation, testing, commissioning

**Emerging technologies**: Innovate a set of emerging technologies based on social security. /* Refer to scope analytics, section 1 */

* Social networking (secure access control, private encrypted message communication, private publishing of image, video, data streaming)

* Social security :

* Child security: Technologies related to sports and games

* Indoor games (toys, caroms, ludos, table tennis, chess)

* Outdoor games (football, cricket, hockey, volleyball, basketball, rugby, tennis, badminton, baseball, cycling, athletics)

* Yoga and physical exercises (Tools used in gymnasiums and health fitness clubs)

* Meditation

* Social media, webmail, e-chat;

* Women's empowerment : social media;

* Gender equality : social media;

* Safety of senior citizen : mechatronics, mechanical, old age home;

* Religious and cultural security : Broadcast communication of divine religious and cultural rituals and activities at temples, mosques, churches etc. using virtual reality technology;

* Peace : Defense, CCTVs, Webcams, Drones for surveillance operation by police force, army, fire brigade and disaster management workforce;

* Justice : AI and logic based automated legal system, case based reasoning, digital governance;

* Partnership : strategic alliance, collaborative intelligence;

* Job security : decent work, workplace safety, HR information system;

* regulatory compliance through strong institutions : Regtech analytics

* Poverty control of refugees and migrants /* Refer chapter 1*/

* Safety against war, act of terrorism, crime, bioterrorism and natural disaster /* Refer chapter 2.

* Technologies for education and financial security
* Healthcare, ageing population, higher public expectations: Health insurance system, retirement planning & pension system,

* Technological transition, inequalities and discrimination, new risks, shocks and extreme events: Strategic risk analytics;

* Employment of young workforce, labour markets and digital economy, protection of migrant workforce: Human resource management system;

Prof. Nelson, Prof. Ronaldinho and Dr. Parker are exploring the system associated with the innovation of emerging social security technologies. Social networking is an emerging technology; popular social networking sites have several billion monthly and daily users. It is an online platform which is used to build social networks or social relationships based on similar personal or career interests, activities, backgrounds or real-life connections. Social networking services may have various types of format and number of features such as digital photo and video sharing, posting comments and online blogging. The technology is accessible through various types of popular information and communication tools, desktops, laptops, tablets and smart phones.

Social engineering is the psychological manipulation of people into performing actions or divulging confidential information through various ways such as vishing, phishing, smishing, impersonation, water holing, baiting and quid pro quo. It is essential to develop information security culture. Employee behavior can have a big impact on information security in organizations. Cultural concepts can help different segments of the organization work effectively or work against effectiveness towards information security within an organization. It is an interesting agenda to explore the relationship between organizational culture and information security culture. It is possible to develop information security culture through pre-evaluation, strategic planning, operative planning, implementation and post-evaluation.

## 3. STRUCTURE

### *Structure Analytics*

Agents: System analysts, business analysts;

Moves: Design and configure

* Organization structure

* Technology forums

* National level: Government (E-governance model), NGOs, research organizations;

* International level: strategic alliance among global organizations (nations, heath, child, peace);

* System architecture (topology, modules, nodes, connectivity, layers);

* Emerging technologies: Innovate a set of emerging technologies as per the goals of social security;

* Level 1: digital technology, information technology, computer science, electrical, electronics, telecommunication, chemical, mechanical, civil engineering;
* Level 2: Identify fundamental building blocks of information technology:

* computing

* data (data warehouses, big data analytics, performance scorecard)

* networking (web mail)

* security schema

* application (social networking)

* Social networking services (friendship, dating, job search, career growth planning, employment, research, policy making, search for resource

Prof. Nelson is presenting the basic structure of social security technologies. Social networking services may be structured into various categories: social network services for socializing with existing friends (e.g. Facebook, Twitter) through online distributed computer networks and internet services, networking services used or non-social interpersonal communication on job search, career growth planning and employment, social navigation for helping users to find specific information or resources (e.g. products, services, books, medicines, healthcare, travel, hospitality) and services for researchers and policymakers. Social networking services may be categorized based on age, occupation and religion supported by trusted recommendation system. A social network service usually provides an individual centered service whereas online community services are group centered to maintain and develop new social and professional relationships and to offer opportunities within professional education, curriculum education, and learning, to make new business contacts or keep in touch with previous alumni, co-workers, affiliates and clients. Social networking services provide a virtual space for learners through participation, collaboration, distribution, dispersion of expertise and relatedness. Registered users share and search for knowledge which contributes to informal learning.

Excessive use of social networking may affect the young community such as wasting time, cyber bullying, invasions of privacy, social anxiety, abuses resulting conflicts in relationship management and depression. Blocking and banning of social networking services for the student community may not be a rational solution. The community should be alert of addiction of social media and cyberbullying. Social networking services often share personal data of the users which may be a window into privacy theft.

# 4. SECURITY

## Security Analytics

Agents: kids, children, youth, men, women, senior people;

Organization: Security council of global organizations;

Social security goals: healthcare, ageing population, technological transition, higher public expectations, employment of young workforce, labour markets and digital economy, protection of migrant workforce, inequalities and discrimination, new risks, shocks and extreme events;
**Verification mechanism:** audit security intelligence through intelligent surveillance technologies (e.g. drones, CCTVs, webcams, smartphones).

* **multi-party corruption:** do surveillance through security council of global organization, police, army, detectives, journalists;

* **Social crimes:** Ensure safety from (relationship management problems, superstition, narrow religious outlook, sex, domestic violence, drug and alcohol addiction, addiction of pornographic films and video games, smoking, mental stress, panic, financial crime);

* **Safety from natural disaster/** /*refer chapter 2*/

* **Safety from war, bioterrorism and acts of terrorisms

* **access control:** verify authentication, authorization, correct identification, privacy, audit confidentiality, data integrity and non-repudiation;

* **social security policy:** verify rationality, fairness, correctness, transparency, accountability, trust and commitment;

* **system performance:** verify reliability, consistency, scalability, resiliency, liveness, deadlock freeness, reachability, synchronization, safety;

* **malicious attacks:** verify the risk of Sybil, false data injection, shilling: push and pull, denial of service (DoS), fault injection attack;

* **web security:** session hijack, phishing, hacking, cross site request forgery, cross site script, broken authentication, improper error handling;

* **Social engineering:** vishing, pretexting, vishing, phishing, spear phishing, smishing, impersonation, water holing, baiting, quid pro quo;

call threat analytics and assess risks of emerging digital technologies (e.g. social networking sites, social media, broadcast communication):

* **what is corrupted or compromised (agents, computing schema, communication schema, data schema, application schema)?** detect type of threat.

* **time:** what occurred? what is occuring? what will occur? assess probability of occurrence and impact.

* **insights:** how and why did it occur? do cause-effect analysis on performance, sensitivity, trends, exception and alerts.

* **recommend:** what is the next best action?

* **predict:** what is the best or worst that can happen?

**Output:** security intelligence

Prof. Roberts and Dr. Gremy Smith are exploring the security of social networking services through a case analysis on cancer of mind, depression and stress management (section 8). There are various methods of social engineering based on specific attributes of human decision making, cognitive biases or bugs in human hardware. Social engineering can steal confidential data of the users through phones, session hijack, criminal posing or stealing of company secrets. A malicious hacker may contact the target through a social networking site; gains the trust of the target and tries to access sensitive private data. Social engineering relies heavily on various principles of influence such as reciprocity, commitment and consistency, social proof, authority, liking and scarcity. Reciprocity forces people to return a favor. Commitment and consistency forces people to disclose private data.
Authority may force a user to reveal critical strategic information. People are easily persuaded by other people whom they like. Perceived scarcity may generate demand. Vishing or voice phishing is the criminal practice of using social engineering over telephone system to gain access to private personal and financial information from the public for the purpose of financial reward. Phishing is a technique of fraudulently obtaining private information through e-mail. Smishing is the use of SMS to lure victims into a specific course of action. Like phishing, it can be clicking on a malicious link or divulging information. Impersonation is pretending to be another person with the goal of gaining access physically to a system. The life-cycle of social engineering goes through information gathering, engaging with victim, attacking and closing interaction.

Pretexting is the act of creating and using an invented scenario to engage a targeted victim in a manner that increases the chance the victim will divulge information or perform actions that would be unlikely in ordinary circumstances. Vishing uses a rogue interactive voice response (IVR) system. Spear phishing fraudulently obtains private information by sending highly custom emails to few end users. Water holing is a targeted social engineering strategy that capitalizes on the trust users have in websites they regularly visit. The victim feels safe to do things they would not do in a different situation. Baiting is like the real-world Trojan horse that uses physical media and relies on the curiosity or greed of the victim. Quid pro quo means something for something. In case of tailgating, an attacker, may seek entry to a restricted area secured by unattended, electronic access control, e.g. by RFID card, simply walks in behind a person who has legitimate access.

The security intelligence of regulatory compliance is a multi-dimensional parameter which should be verified at various levels. The regulatory clauses should be defined and audited by a group of authorized agents correctly and rationally. The scope of social security technologies should be correctly identified; relevant data should be sourced through authenticated channels. The social networking system should preserve confidentiality, privacy and integrity of data. For any critical analysis, the system should ask the identity and authentication of the users. After correct identification and authentication, the system should address the issue of authorization. The system should be configured in such a way that an unauthorized agent cannot perform any task out of scope. The system should ask the credentials of the requester; validate the credentials and authorize the agents to perform a specific task. The agents should be assigned an explicit set of access rights according to role. Privacy is another important issue; the analysts can view only the information according to authorized access rights.

It is also crucial to verify and evaluate various rules and regulatory clauses for social security in terms of fairness, correctness, rationality, transparency, accountability, commitment and trust. It is essential to evaluate the performance of the system in terms of reliability, consistency, and stability. The performance of the system is expected to be consistent and reliable. Liveness ensures that under certain conditions an event will ultimately occur. Deadlock freeness indicates that the system should never be in a state in which no progress is possible. The system should be protected from various types of internal and external malicious attacks such as false data injection, Sybil, shilling and denial of service (DoS) attack. The
auditors must assess the threats of such types malicious attacks by adversaries. It is
also important to assess the risk of multi-party corruptions on the social security
technologies in terms of agents, policy, procedure and protocol. An efficient
knowledge based system is expected to monitor the gaps and violations in regulatory
compliance in real-time and diagnose any fault just like supervisory control and
data acquisition system.

5. STRATEGY

Strategy Analytics

Agents: System analysts, business analysts, technology management consultants;

Strategic moves :

Call deep analytics ‘7-S’ model; explore how to ensure a perfect fit among 7- S elements – scope, system, structure, security, strategy, staff-resources, skill- style-support;

Define a set of social security goals and emerging technologies accordingly.

* Do SWOT analysis: strength, weakness, opportunities and threats of existing technologies related to e-governance, digital technologies, social networking technologies, police, army, detectives.

* Fair and rational business model innovation associated with entertainment, culture and religious rituals

- Who are the consumers?

- What should be the offering of products and services?

- What do the consumers value?

- What is the rational revenue stream?

- How to deliver values to the consumers at rational cost?

* Do technology life-cycle analysis on ‘S’ curve : presently at growth phase of ‘S’ curve.

* Explore technology innovation-adoption-diffusion strategy for real-time surveillance system.

* Explore innovation model and knowledge management system for creation, storage, sharing and application of knowledge.

* Adopt ‘4E’ approach for the implementation of social security globally : Envision, Explore, Exercise and Extend.

Case: Poverty, Intrusion and Enterprise Resource Planning

Prof. Subramanian is exploring the strategy for innovation of emerging technologies to ensure social security Let us look at the problem of poverty of a group of neighboring countries: I, B, P, N, S and C. The ministries of finance and economic affairs, home, defense and foreign affairs of country I are brainstorming how to control poverty, create more job opportunities through business model innovation and countermeasures against environmental pollution. The points of discussion are as follows:
Is poverty really the outcome of luxurious passion and fashion of the economists and public policy makers globally today?

Is it rational to copy the concept of chemical equilibrium into economic equilibrium to maintain the stability of the global economy?

Should the government of country I be indifferent to regulate intrusion, migration and infiltration of the refugees from neighbors (e.g. B, P, N) to control poverty and unemployment being provoked by the wise sayings like tolerance, unity in diversity, collaborative intelligence, humanity, actions against promotion of hate crimes and vote bank politics? What are the risks and countermeasures to avoid economic stress in job market and optimal fair resource distribution in country I today? Is it not essential to adopt a strict immigration policy, citizen amendment bill (CAB), national registry of citizens (NRC) and ERP system to curb the problem of intrusion and poverty?

If more resources are allocated and distributed to the poor people of the society by Govt. of I, there will be more intrusion and infiltration from the neighbors to country I and there will be spiraling effects in poverty statistics. The poor people from the neighboring countries will be attracted to the facilities and opportunities adopted by Govt. of country I. The local people of country I are facing the stress in job market and deprived of fair resource allocation and distribution due to the pressure from the refugees / migrants / infiltrators. A large portion of the resources (e.g. space, land, food, energy, utilities) are captured by the refugees, infiltrators and migrants who are involved in setting up their local colony and culture through strategic alliance. Look at the local people of state W of country I. They are forced to migrate to the other states of country I but ill-treated and forced to come back to W. The recent killing of several laborers in state K of country may be a good example of the problem. The migrants are coming to W. They are setting up their own colony, culture and educational system and running parallel government. The migrants and refugees are involved in bad culture ('apasanskriti') and malicious antisocial activities silently through fake broadcast and false data injection attack; the real contributions are big zero; hate crimes (jatibiddesh) are getting originated through a natural process. The resources are getting consumed by the intruders / migrants / refugees in unregulated manner which will surely create problems of resource planning, allocation and distribution in future. It is the time to think scientifically. The local people of I and state W are at war, helpless, victimized and burdened.

So what should be the solution for the burden of intruders/ infiltrators / migrants for poverty control? The conflict between ERP (Enterprise Resource Planning) and emotional economics is inevitable. An interesting solution may be 'Uniform growth and development for all' through collaboration in neighboring countries and proper rehabilitation of the refugees, intruders and infiltrators in the neighboring country / state through back-propagation mechanism. If more resources are distributed to all the poor people of all neighboring countries (e.g. minimum income) and there is uniform economic development, there will be less intrusion of the refugees towards country I and state W. It may be then easier to tackle the rising problem of poverty and unemployment. Border security force of country I may not be able to tackle the problem of migration and infiltration alone. Country I should help the neighbors
following the policy of collaborative intelligence in various domains such as
technology management, public policy for poverty control measures and
infrastructure development. The ministry of defense, home, foreign and economic
affairs of country I are expected to work with governments of neighboring countries
closely and jointly. It is a critical issue of fairness, correctness, transparency,
accountability and rationality in enterprise resource planning and supply chain
management; it is rational to think from the resource based view of a nation,
country and state.

The ministers of the aforesaid ministries are also considering several other issues:

- Is the broadcast communication system in country I (e.g. newspapers, TV, radio channels) captured and compromised by the intruders, infiltrators, migrants and refugees from the neighbors?

- Is it rational to focus on traditional old models of Economics such as Keynes and David Ricardo for poverty control?

- Are the books on poor economics in hard times really written by the great economists or the outcome of the perception based nonfactual readymade immatured confused thoughts of the students / academic community at the college canteens of the academic institutes? Are all data mentioned in the books on poor economics in hard times authentic or readymade?

Is the resource based view of a firm similar to the resource based view of a nation / state / country? What are the negative impacts of unregulated resource consumption by the intruders, refugees, infiltrators and migrants in a country or a state ? Will not there be shortage of resources in future in country I? If there is unlimited increase in consumption by the intruders and refugees; there may be increase in demand for a firm in short run but there will be shortage of resources and economic stress in job market for the local people of a country in long run! The local folks may be forced to struggle for existence. Did the great economists think from the perspective of optimal enterprise resource planning (ERP) and supply chain management (SCM) in their old time? Does country I need critical thinking and deep analytics on HR and business model innovation today; the problem of poverty is not so trivial at all; traditional old models may be dead and obsolete in modern times! An apparently popular solution may be a disaster due to biased, perception based nonfactual readymade immatured confused thoughts of the so called intellectuals! It is a question of the security of a country : be it social security or defense. The ministries have decided to focus on technology management i.e. proper innovation, adoption and diffusion of a set of path breaking technologies to fight against poverty, environmental pollution and creating new job opportunities through business model innovation.

## 6. STAFF-RESOURCES

### Staff-resources Analytics

do estimation, planning, capacity utilization, allocation and distribution of '5M' resources.
★ Man (human capital management [social scientists, business analysts, system analysts, project managers], talent acquisition, talent retention, training, reward and recognition);

★ Machine (computer hardware, software, internet);

★ Material (data at social networking sites, toys, tools for sports and games);

★ Method (process innovation);

★ Money (optimal fund allocation, project management, resource allocation, resource distribution).

Prof. Nancy Robson is analyzing the need of staff-resources in terms of 5M (man, machine, material, method and money) for the innovation of social security technologies. In this connection, human capital should be considered as a strategic asset and a sustainable resource of technological innovation. Talent management demands the skills of human resources for the innovation of social security technologies. ‘Man’ explores various aspects of human capital management of technological innovations such as talent acquisition and retention strategy, training and performance evaluation. ‘Machine’ indicates the basic need of computer hardware, software and internet. ‘Material’ is related to toys, equipments and tools used for various types of sports and games. ‘Method’ explores various aspects of process innovation, intelligent mechanism and procedure for the innovation of social security technologies. Finally, ‘money’ explores the scope of optimal fund allocation for innovation and diffusion of technologies.

It is crucial to analyze the dynamics of this technological innovation in terms of sources of innovation and roles of individuals and organizations, government and collaborative networks; various resources required for effective technological evolution and diffusion. Innovation demands the commitment of creative people. Creativity is the underlying process for technological innovation which promotes new ideas through intellectual abilities, thinking style, knowledge, personality, motivation and commitment. Innovation demands the motivation and commitment of creative people. The innovation needs useful and novel support of creative, skilled, experienced and knowledgeable talent. Creative talent can look at the problems in unconventional ways; can generate new ideas and articulate shared vision through their intellectual abilities, knowledge, novel thinking style, personality, motivation, confidence, commitment and group dynamics. A cooperative and collaborative environment must recognize and reward creative talent in time. Organizational creativity is associated with several critical factors such as human capital management, talent acquisition and retention policy, complex and tacit knowledge management strategy and organization structure.

# 7. SKILL-STYLE-SUPPORT

## Skill-style-support Analytics

★ Skill: technical, system admin, management, legal, governance, surveillance, relationship management;
* Style: leadership, shared vision, goal setting, intelligent communication protocol, risk assessment and mitigation;

* Support : proactive, preventive and reactive support.

The expert panel have explored skill-style-support necessary for the innovation of social security technologies. The workforce involved in innovation are expected to develop different types of skills in social networking, social media, social engineering and digital governance. They should be creative in development of social security technologies. The diffusion of new technological innovation depends on the skills and capabilities of the human talent. The system administrators must have leadership skills in smart thinking, communication, coordination and change management. The workforce should develop skills through effective knowledge management programmes. An effective knowledge management system should support creation, storage, sharing and application of knowledge in a transparent, collaborative and innovative way. The diffusion of social security demands the support of smart leadership style; The style is basically the quality of leadership; great leaders must have passion, motivation, commitment, support, coordination, integration and excellent communication skill. What should be the innovation model for effective diffusion of social security technologies? Is it possible to adopt K-A-B-C-D-E-T-F model?

It is essential to develop information security culture at organization level through pre-evaluation, strategic planning, operative planning, implementation and post-evaluation. Pre-evaluation identifies the awareness of information security within employees through analysis of current security policy. Strategic planning comes up with a better awareness-program with clear targets. Operative planning sets a good security culture based on internal communication, management buy-in, security awareness and training program. The final stage is implementation of information security culture through the commitment of management and employees of the organization.

8. CASE: CANCER OF MIND & SOCIAL SECURITY

The expert panel are analyzing a case on cancer of mind which affects social security seriously. . They have defined and identified major causes of cancer of mind. Cancer of mind is the inability to cope with a perceived or real threat to one's physical, mental, emotional and spiritual well being which results in a series of physiological responses and adaptation. It is human body's physical, mental and classical reaction to circumstances that frightens, confuses, endangers or irritates human agents. It is basically mental tension. If controlled, it may be a friend that strengthens human beings for the next encounter. But, is it really easy to control cancer of mind? It is basically mental tension; it is a feeling of strain and pressure. It is a type of physiological pain.

What are the symptoms and characteristics of cancer of mind: depression, anxiety, anger, irritabilities, restlessness, feeling overwhelmed, unmotivated or unfocused, trouble in sleeping or sleeping too much, racing thoughts and constant worry, problems with memory or concentration and making bad decisions. Cancer of mind
may have negative effects on health: high blood pressure, heart disease,, diabetes, obesity, depression, anxiety, skin problem ( acme or eczema) and menstrual problems. It is debatable whether cancer of mind has any positive effects such as boosting brainpower, increase of short term immunity, increase of strength, creativity, motivation of success and developments of the children. Cognitive symptoms may be memory problems, inability to concentrate, poor judgement, seeing negative attitude and constant worrying. The emotional symptoms may be depression and general unhappiness, anxiety, agitation, moodiness, anger or irritability, feeling overwhelmed, lonliness and isolation, internal and external emotional problems. Physical symptoms may be aches and pain, diahorrea, constipation, nausea, dizziness, chest pain, rapid heart rate, loss of sex drive, frequent cold or flue. Behavioral symptoms may be eating more or less, sleeping too much or too little, withdraw from others, drug addiction, smoking, alcoholic addiction, nervous habits, procrastinating or neglecting responsibilities.

The next critical issue is how to identify cancer of mind? There are several behavioral signs : no time for relaxation or pleasant activities, prone to accidents and forgetfulness, increased reliance on alcohol, smoking, caffeine, recreational or illegal drugs, becoming a workaholic, poor time management, poor standards of works, absenteeism, self negligence, change in appearance and social withdrawal. What are various types of cancer of mind? It is a physical, mental or emotional factor that causes physical or mental tension. It may be internal due to illness or medical surgery or external due to environment, phycological or social factors. It may be anticipatory, situational or encounter. It may be cognitive (e.g. information overloading, guilt, panic, anxiety), acute (single, repeated) or chronic cancer of mind (if acute cancer of mind is not resolved and lasts for long period) or emotional cancer of mind (mental strain, fear, frastration, sadness, anger, grief). What are the sources of cancer of mind : money or financial troubles, job issues, relationship conflicts, life changes; loss of loved one (spouses, grief, relatives, long daily journey, abuse, insult and physical torture. The expert panel are outlining a mechanism for restricting cancer of mind.

Agents: Defender (e.g. human agent, doctor), Attacker (e.g. malicious agent or adversary);

Model: Human mind;

Objectives: cancer prevention at optimal cost;

Constraints: budget or financial constraint, resources, time, knowledge;

Input: Perception of human agent, performance measures of biological system or test data;

Strategic moves: intelligent reasoning, optimal mix of proactive and reactive approaches, rational payment function and budget plan;

Revelation principle: The agents preserve privacy of strategic data;

* Defender : The defenders share critical information collaboratively.

* Attacker : The adversaries do not reveal the plan of malicious attack, information of targets and weak links in advance.

Cancer Prevention Approaches:

<figure><img src="image_89.png" /></figure>

Proactive approach:
* Identify targets :

* application schema – human mind;

* networking schema – brain and nervous system;

* computing schema – nerve impulse and release of neurotransmitter;

* data schema – symptoms of abnormal, selfish behavior, narrow outlook, jealousy, negative mechanical robotic thinking, devil's thought, fear of death, anxiety, impatience, restlessness, hyperactive behavior, depression;

* Security schema – auto immunity, hormones, vitamins, minerals, genetic traits;

* Threat modeling

* Call threat analytics and assess miscellaneous risk elements :

* change in behavior, physical appearance and personality;

* psycho-oncology disoder;

* Schizophrenia : multiple personalities, severe mental disorder in thinking, perception, emotion, sense of self and violent behavior;

* Estimate demand plan;

* Explore risk mitigation plan : accept / transfer / remove / mitigate risks.

* Vaccination (option to be explored);

* Optimal diet intake to fight against malnutrition;

* Life-style : Avoid smoking, alcohols and drug addiction;

* Stress control through yoga and meditation, deep sleep;

* Listen soft relaxation music during idle time in subconscious mind.

<figure><img src="image_90.png" /></figure>

* Reactive approach:

* adopt sense-and-respond strategy.

* assess risks of single or multiple attacks on the mind; analyze performance, sensitivity, trends, exception and alerts.

* what is corrupted or compromised?

* time series analysis : what occurred? what is occuring? what will occur?

* insights : how and why did it occur? do cause-effect analysis.

* recommend : what is the next best action?

* predict: what is the best or worst that can happen?

* verify security intelligence of application, computing, networking, security and data schema of human mind.

* Level1: correctness, fairness, accountability, transparency, rationality, trust, commitment;
* Level 2: authentication, authorization, correct identification, privacy, audit;

* Level3: safety, reliability, consistency, liveness, deadlock-freeness, reachability, resiliency;

* Level4: stability, system dynamics, quality of application integration.

* Explore risk mitigation plan.

* Do medical testing → Data visualization of brain scan;

* Integrated medicine

* Psychiatric oncology

* Behavioral and cognitive therapy

<figure><img src="image_91.png" /></figure>

* Fight against bad luck: Identify critical risk elements.

* Genetic disorder

* Reproductive disorder (personal, hormonal and family history)

* Occupational exposure

* Injuries from accidents, war and crime

* Environmental pollution (e.g. air, sound)

* Hostile climate, weather and other locational disadvantages, exposure to sunshine

* Develop risk mitigation plan in terms of deaddiction and rehabilitation.

### Payment function:

* Select dominant strategy of healthcare investment from the options of reinforcement on the weakest link, experimental treatment, process re-engineering, transformational and renewal.

* Estimate aspiration point, reservation point, strong, weak, indifference and veto thresholds in the security requirements.

* Trade-off proactive vs. reactive security: assign weights to each approach.

* Allocate healthcare budget in the ratio x:y:z where x: fund for proactive approach, y : fund for reactive approach and z: health insurance premium;

### Output: Prevention plan of cancer of mind

Why cancer of mind is so critical to the student community of the world? The student community faces cancer of mind due to various reasons such as academic pressure, vast courses, different types of courses, information overloading, pressure of activity based learning, completion of assignment based projects within deadline, mental depression due to poor performance in examinations, abuses from teachers, parents, friends and relatives, relationship management problem, financial pressure of education loans, exhaustive effects of long journeys for going to school, pressure of intensive competition, rivalry, panic, jealousy, examination, deadline, pressure of combating paid work and study, difficulty in organizing works, pressure of long hour study, poor time management and out of control debts.. The students may have some common symptoms of cancer of mind : difficulty in sleeping, weight gain
or weight loss, stomach pain, irritability, teeth grinding, panic attacks, headaches and difficulty in concentration. There are some common signs of cancer of mind for the students such as personality change, agitated, withdrawal syndrome, poor self-care, hopelessness. How can the student community manage cancer of mind? There are various strategies for reducing cancer of mind: physical (yoga, meditation, exercises), morning walk, get more sleep, adopt relaxation techniques such as listening soft instrumental music, talk to near and dear ones, physical activities, avoiding drugs, alcohol, caffeine and smoking, time management, disciplined lifestyle, intake of nutritious food (e.g vitamins B1, B2, B3, B6, B9, B12, milk, sufficient water, spinach, fruits, vegetables).

Drug addiction is a major cause of cancer of mind. There is slight difference between cancer of mind and madness. A human agent suffering from cancer of mind may act selfishly with narrow outlook and malicious business intelligence. But, a mad man generally acts irrationally. Any chemical substance other than food used for the prevention, diagnosis, alleviation, treatment or cure of a disease of human agents or animals is called a *drug* or medicine or therapeutic agent. Drug addiction is the habitual, physiological and psychological dependence on a substance or a practice which is beyond voluntary control of an addict. Addictive drug modifies the biological, psychological and social behavior of the addicted person by stimulating, depressing or distorting the function of their body and mind. Use is basically taking a drug for medical treatment like disorder or injury. Drug abuse is the wrong, improper, injurious and misuse of drugs for non-medical purposes which affects physical and mental health of the drug abuser. They use drugs without the prescription of the doctors secretly; taken frequently and regularly; habituating substances; may affect brain and nervous system and changes behavior; gives temporary pleasure or relief from stress. A doctor prescribes drugs for the treatment of diseases or for the improvement of physical and mental health and the drugs are withdrawn as soon as the desired effect is achieved. Repeated use of some drugs on a periodic or continuous basis may make the body dependent on those drugs. It is *drug dependence*. The drug action is affected by a set of factors such as the form, type, dose, mode of use, period of consumption and susceptibility of the addicted person. The addicted person becomes drug dependent through various stages such as experimental use for curiosity, recreational use, situational use, compulsive use and dependence. The addicted person shows some abnormal symptoms such as poor academic performance, indifference in the duties and responsibilities, change in behavior (e.g. telling lies, violence, unrest), change of physical appearance (e.g. loss of weight, vigor and appetite) and change in personality. There are two types of drug dependence - psychological and physical or neuroadaptation. In case of physical dependence, intake of drugs is essential to maintain physiological equilibrium. In case of psychological dependence, a person believes that the normal state can only be achieved with the action of the drugs.

There are several critical *causal factors* of drug addiction such as curiosity, the pressure from friends and relatives, high stress, pleasure, temporary relief from mental stress, frustration and depression, poor academic performance, problems in relationship management, job loss, unemployment, desire for more work, looking for a different world, relief from pain, family history, easy availability of drugs and
money and excitement and adventure. Some students take drugs to keep awake the
whole night for the preparation of their examinations or to manage high work load
or backlog. It is a malpractice and bad habit.

Drugs act on brain and central nervous system. The structural and functional units of nerve cells are neurons; the message passes from one neuron to the other through synapses. Arrival of the nerve impulse causes the release of a chemical neurotransmitter. The drugs act at the synapses. The depressant drugs (e.g. alcohol, narcotics) inhibit the production of neurotransmitter or inactivation of the neurotransmitter more quickly or modify postsynaptic membrane. The stimulants increase the production of neurotransmitter and increase stimulation of the neurons. The general symptoms of drug addiction include excitement, violent nature, exhausted and drowsy appearance, poor concentration, memory loss, loss of interests in works, studies and social life, reduced appetite, vigor and weight and disorder of sleep. Ultimately, it results the cancer of mind of drug addicted people.

The addicts often suffer from the problems of central nervous system, psychosis, Hepatitis-B, AIDS, impotency, chromosal abnormalities and genetic disorder. Many of them have a dull unhappy life. They create problems for their families, neglect duties and may lose jobs. It may deprive a family of basic needs and may result frustration and insecurity of the children. The family members may suffer from physical and psychiatric problems such as headache, anxiety, insomnia and depression. The drug users get drugs from illegal sources encouraging smuggling, criminal activities, bio-terrorism and accidents. The drug addicts are less efficient and unreliable as workers and often lose their job or may not get employment anywhere.

Life-science supply chain is a soft target of bio-terrorism. The drugs and medicines sold through popular distribution channels may be tainted, compromised and mislabeled. It needs strong support of drug quality and security act. The life-science supply chain has developed and produced breakthrough drugs and medicines that enhance the average life span in the world. Unfortunately, when bad things happen in life-science supply chain, the public get hurt. Today's life science supply chain requires an effective ‘Drug Quality and Security Act and Standards’ which is able to clarify with transparency the authority, roles and responsibilities of food and drugs administration and consumer protection ministry, regulate pricing of drugs, develop a national track-and-trace system to audit the functions of the life-science supply chain and minimize the risks of contamination, adulteration, diversion or counterfeiting.

It is essential to adopt a set of *good habits* by the students and youth as proactive approach through a value based education system at school and colleges to mitigate the risks of drug abuse.

* Intelligent reasoning through common sense, logical and analytical mind set;

* Be proactive and take responsibility of your life. Avoid bad habits and negative thinking; adopt good habits;

* Be dedicated, motivated and committed in learning;

* Define vision, mission and goals in life rationally and innovatively;

* Control physical and mental stress through yoga, meditation, relaxation music and extracurricular activities;
* Be conscious of physical, mental and social health;

* Prioritize multiple tasks through proper planning and time management and do the most important things first;

* Think win-win; have an everyone-can-win attitude with confidence, patience and perseverance;

* Listen to the other people carefully and sincerely. First try to understand and then to be understood;

* Promote synergy and collaborative intelligence, work together to achieve more through group dynamics;

* Sharpen the saw - renew yourself regularly. Analyze as-is state; find out gap and innovate to-be-state;

* Contribute to the society and environment through activities, thoughts and plans.

There are various strategies to mitigate the risk of drug abuse and drug addiction for reactive approach: deaddiction, childcare, drugs as social stigma, legal punitive action, strict regulatory compliance through effective corporate governance, corporate social responsibilities and good habit development through an effective education system. The physicians should prescribe drugs with responsibility and the pharmacists should not sell drugs without the valid prescriptions of the doctors. The parents should keep a watch and monitor the activities, attitude and behavior of their children. The social workers and policemen should be alert and inform the parents or deaddiction centers in time. In fact, law and the public should take joint responsibility against drug abuse.

*Deaddiction* is basically treatment of drug addiction or withdrawal symptoms of drugs. The major steps of deaddiction include master health check up (e.g. blood test, brain scanning), pharmacotherapy, psychosocial therapy, health restoration, psychological treatment and prevention of relapse. If a drug dependent person fails to get drugs, feels severe physical and psychological disturbances depending on the type and dosage of drugs. The general treatment of *withdrawal symptoms* of a drug is to replace the drug with a less reinforcing and legally available drug that can be gradually eliminated with decreasing doses. It is Pharmacotherapy. For the drug combination addiction, it is required to withdraw one drug at a time and maintain the others. After the withdrawal symptom subsides, psychological treatment persists and cause craving for the drugs. At this stage, the drug addicts need the moral support of their parents, relatives and friends. They may need the treatment at rehabilitation centers; it is a long term treatment requiring behavioral training of the patients. *Rehabilitation* involves the psychological and social therapy in the form of counseling by relatives, friends and physicians in a sympathetic manner. The patients should learn the ill effects of drug addiction through Psychosocial therapy. The patient also needs supportive measures such as administration of vitamins, proper nutrition, restoration of electrolytic balance and proper hydration. Vitamin C checks the rise of the level of cAMP in human brain. The patient may also need Psychological treatment. Finally, *readdiction* may occur; many addicts restart taking drugs after deaddiction. They should be watched by their near and dear ones.
It is an interesting agenda to identify various stages and models of cancer of mind with the support of human mind, physiology, stimulus, response, transaction, interaction, pressure-environment fit concept. There are various stages of cancer of mind such as alarm, resistance, exhaustion, fight or flight, damage control, recovery, adaptation and burnout. It is possible to diagnose cancer of mind through a set of common symptoms such as anxiety, panic, workload, pressure, hassle and hurry, irritability and moodiness, physical symptoms (e.g. chest pain, headache, stomach upset) and sleeping disorder. Cancer of mind is a biological response to environmental condition, body's method of reacting to threat, challenge, physical or psychological barriers. It may be a stimulus and demands response, adjustment or adaptation. It is often experienced when a human agent perceives that demand exceeds personal and social resources the individual is able to mobilize. It is a collection of physiological changes that occur when one forces a perceived threat. Individuals behavior is determined by the interaction between individuals in two directions. It is the degree of fit or match between human agent and his / her workplace environment and other members of an organization.

What are the causes and symptoms of cancer of mind : personal, organizational and environmental. Personal causes of cancer of mind may be personal life style (smoking, alcohol addiction, drug addiction), indisciplined life style / Bohemian life style), negative attitude, violent behavior, loss of control (fear, anger, panic, grief), desperate narrow outlook, superstition, lack of human feelings (love, affection, respect), mechanical, robotic and materialistic outlook, too much ambitious, lack of focus and concentration, lack of confidence, hesitation to take challenges, no goal in life, random aspiration and reservation point and preferential thresholds, intense competition and comparison with others. Work stress is a very important factor - unhappy in job, no job satisfaction, having a heavy workload, too much responsibilities, working long hours, poor management, unclear expectation of work, no management of objectives, no target, no goal, working under dangerous conditions, insecurity of career growth, risk of termination, dull corporate communication, failure in project management, discrimination or harassment at work all these factors may result cancer of mind in working professionals. There are other causal factors of cancer of mind : death of spouse, near and dear ones, divorce, loss of job, increase in financial obligation, getting married, moving to new house, chronic illness or injury, emotional problems (depression, anxiety, anger, grief, guilt, low self esteem, care of senior or sick family members and traumatic event (natural disaster, theft, rape, domestic violence). The environmental factors may be fear and uncertainty, bad attitude and perception, unrealistic expectation, change in life, major changes in life (work or school), relationship difficulties, financial problems, being too easy, children and family. The internal environmental factors may be pessimism, inability to accept uncertainty, rigid thinking, lack of flexibility, negative self talk, unrealistic expectation, perfectionism, all-or-nothing attitude. Major events have negative impact on mind such as death of a spouse, divorce, marriage separation, imprisonment, death of near and dear ones and close family members, injury or illness, marriage, job loss, marriage reconciliation and retirement. The exper panel is not sure whether this problem can help one to accomplish tasks more efficiently; can boost memory.. It is a vital warning system
producing fight-or-fight response when the brain perceives mental stress, it starts flooding the body with cortisol.

What are the consequences of cancer of mind? It may have effects on behavior and personality (e.g. anger, anxiety, panic, bad temper, fear, tired, boring, frustration, hopelessness, sadness, grief, lack of self confidence, lack of self respect), high blood pressure, heart diseases, diabetes, obesity, depression, anxiety, skinn problem, menstruation problem. It may have negative effects on performance such as bad performance, low productivity, increased mistakes in works, increased rejection level, loss of focus, no objectives, no target, no goal, loss of goal, random walk in life, increased time and cost, quality problems, quality of services and making bad decisions. It may affect the performance of an organization in terms of reduced revenue, reduced profit, increased cost, low productivity, poor performance, quality problems and increased cost.

What are the strategies to mitigate the risk of cancer of mind? The healthy strategies include yoga, meditation, physical exercises, get more sleep, relaxation, listening soft instruemental music, watching TV, chat and talk to near and dear ones, intake of water and nutritious food such as balanced food (milk), fruits, vegetables, vitamins (A,B,C,D,E,K), protein (e.g. fish, meat, egg). Unhealthy strategies include avoiding drug addiction, smoking, alcohol, stress free life-style, be happy and calm. A sick person should follow regular physical exercises, connect to the others, building stronger and more satisfying connections, engaging senses (eye, ear, nose, tongue and skin) for sight, sound, taste, smell, touch and movement, learn to relax, eat a healthy diet; get rest, feeling tired can increase stress by causing one to think irrationally; should get recovery from job loss and unemployment.

The factors that influence cancer of mind include tolerance levels, support network, your sense of control, individual attitude and outlook, ability to deal with emotion, knowledge and preparation. Peer group and social support from near and dear ones, friends, relatives. A strong network of supportive friends and family members free of competition, jealousy, rivalry, hate, frank and free exchange of information, experience, chat and gossip, trust, commitment, motivation, spiritual, be happy – all are important to counter cancer of mind. Another important move is happiness and well being (be happy with positive attitude, job satisfaction, contributing to society, spiritual outlook, do not be too materialistic, donation, religious and cultural outlook, rational setting of individual goal in terms of aspiration point, reservation point and preferential thresholds). Another important factor is workplace environment; excessive workload can interfere individual productivity and performance, may have bad impact on physical and emotional health, affecting personal relationships and home life. Job satisfaction with personal job profile, adaptation, adjustment, ambition, career planning, setting goal of life, positive attitude and mindset – all are important factors to fight against cancer of mind. Each organization needs the support of an effective human resources management system. Is it possible to innovate technologies (e.g. AI techniques, digital, chemical, antidepressants drugs having no side effects) to mitigate the risk of cancer of mind?
# FURTHER READING

1. C.E. Alchourrón, Logic of norms and logic of normative propositions, Log. Anal. 12 (1969) 242-268.

2. V. Aleven, Teaching case-based argumentation through a model and examples, PhD dissertation, University of Pittsburgh, 1997.

3. International Social Security Association.

4. S.K.Chaturvedi. 2012. Psychiatric oncology: cancer in mind. Indian Journal Psychiatry. Apr-Jun; 54(2): 111–118.

## Quiz

* What is the scope of social security technology?

* What is the dominant design of these technologies?

* What are the basic elements of the system architecture?

* What do you mean by technology security? How can You verify the security intelligence?

* What are the strategic moves of technology innovation, adoption and diffusion? What is the outcome of technology life-cycle analysis and SWOT analysis of online and offline technologies?

* How to manage resources for innovation project of these technologies

* What should be the talent management strategy? What are the skills, leadership style and support demanded by the technological innovation?

* How do You manage social security technology innovation project efficiently? What should be the shared vision, common goals and communication protocols? How can you ensure a perfect fit among ‘7-S’ elements?

* What is cancer of mind ? How is it associated with social security? What are the symptoms and effects of cancer of mind? What are the countermeasures? Can you innovate any AI technologies and HRS to mitigate the risk of cancer of mind?
SESSION 10: EMERGING DIGITAL TECHNOLOGIES for HUMANITY – INFORMATION & COMMUNICATION SECURITY

Abstract:

Event : Technology for humanity and global security summit

Venue: Information and communication security hall, Technology park : Sanada

Time Schedule : 1 p.m. – 6 p.m. , 17.8.2020

Agents : Representatives of various global organizations, Technology management experts from science and technology forums, engineers, scientists, representatives and ministers from the departments of information and communication technologies of developed, developing and underdeveloped countries, CEOs of digital technology corporations, business development consultants.

Topic of discussion and key focus areas : Digital technologies, information, media and entertainment

Keynote speakers : Prof. Parker Smith, Prof. Pearson, Prof. Kamal Kumar, Prof. Simon Watson, Dr. Aziz, Dr. Henry Plank

1. SCOPE

Scope Analytics

Explore a set of digital technologies for sustainable development goals.

*   **Poverty control** : Food security (zero hunger), Home security (disaster proof nano-housing schema), Garments and consumer goods security, Education security, Healthcare security (good health, well being, family planning, population control), Financial security (banking, financial services, tax, insurance, retirement planning, stock and derivative trading, economic growth), Energy security (clean and affordable renewable energy), Utilities security (clean water and sanitation, gas, computing, internet, telecom), Communication security (internet, broadcast, satellite communication), Logistics security (travel, hospitalities, surface, water, rail, water, EVs and hybrid vehicles), Information, media and entertainment security;

*   Social security (HR security, decent work, religious and cultural security, gender equality, child security, women's empowerment, peace, justice, partnership, regulatory compliance, strong institutions)

*   Natural disaster security (climate change, flood, drought, storm, cyclone, earthquake, volcano, snowfall, rainfall, fire, bushfire, global warming, heat wave, epidemic, astronomical hazards) (attack of wild animals, insects, paste); artificial disaster security (defense, war, act of terrorism, bioterrorism)

* Responsible consumption and production (Enterprise Resource Planning, Supply Chain Management)

* Industry, innovation and infrastructure (smart cities, smart villages)

* Life on land (environmental pollution, conservation of resources and forest, population control)
* Life below water (marine life, water pollution, global warming, oil leakage, nuclear explosion)

*Emerging innovative digital technologies:*

* Communication technology: Satellite communication, mobile communication, broadcast communication, Sensor, global positioning system (GPS), Secure adaptive filter, RFID, IoT, IIoT, cloud computing, cloud streaming, edge computing;

* Information technology: Information security intelligence analytics, adaptive security, dynamic data protection, cyber security, crash proof code, self healing smart grid; applied AI and machine learning: Deep learning, Robotics, Soft computing; Analytics – Deep analytics, Predictive analytics, Collaborative analytics; virtual and augmented reality, Digital twins, solar computing, Quantum computing, pervasive computing, wearable computing, Ray tracing.

Prof. Parker Smith and Prof. Pearson have started the session by exploring the scope of emerging digital technologies. With the significant advancement of information and communication technology, computing is perceived to be used as the next utility after water, electricity, gas and telecommunication. This session explores the classification of emerging digital technology through scope analytics. Digital technology can be classified into communication and information technologies. The scope of emerging communication technology is explored in terms of adaptive secure filters in adversarial environment, cloud computing, cloud streaming, cloud analytics, Internet of Things (IoT), Industrial IoT, Edge computing, next generation wireless and mobile communication (e.g.4G,5G,6G,7G,8G), broadcast and satellite communication, RFID and sensor networks. The scope of information technology is explored in terms of adaptive security, dynamic data protection, cyber security, crash proof code; applied AI and machine learning, soft computing, deep learning, robotics; deep analytics, predictive analytics, collaborative analytics, virtual and augmented reality, digital twins, solar computing, pervasive computing, wearable computing, secure multi-party quantum computing and ray tracing. This work also evaluates emerging digital technology in terms of system, structure, security, strategy, staff-resources and skill-style-support. Specifically, the technologies of adaptive security, dynamic data protection, solar computing, secure adaptive filters in adversarial environment and secure multi-party quantum computation have been evaluated in depth. The scope of deep analytics and deep learning have been outlined in sessions 1. Emerging digital technologies are useful in various application domains such as education, healthcare, information, media and entertainment, e-governance, manufacturing, retail, logistics, insurance, banking and financial services and development of smart cities and smart villages. Is it possible to apply digital technologies rationally and intelligently for the education of Dyslexia students by reducing their mental stress, information overloading and improving motivation, trust and commitment of the academic communities?

Figure 8.1 shows the classification of emerging digital technology. This is an interesting example of technology association. Level 1 shows the classification of digital technology. Level 2 shows the technology association of various types of
communication technologies such as cloud computing, cloud streaming, cloud analytics, IoT and IIoT. It is possible to explore the scope of digital technology through Business Process Reengineering (BPR) approach (analyze as-is process and related IS, identify gaps and risks of as-is processes and IS and design to-be processes and system); top-down approach, critical success factor (CSF) analysis based on business objectives, constraints and requirements engineering, value chain analysis, bottom-up approach and inside-outside approach. One of the contributions of this session is scope analytics of emerging digital technology in figure 8.1. The scope analytics shows a set of information and communication technologies; among which the concepts of deep analytics, solar computing and adaptive security are unique.

<figure><img src="image_92.png" /><figcaption>Figure 10.1 : Emerging Digital Technology Classification</figcaption></figure>

**Communication Technology** : The scope of emerging communication technology may be explored in terms of cloud computing, cloud streaming, cloud analytics, Internet of Things (IoT), Industrial IoT and Edge computing next generation wireless and mobile communication, broadcast communication, satellite communication, RFID and sensor networks. The wireless technology is going through an evolution of a set of generations (1G→2G→3G→4G→5G→G→7G→8G). This session analyzes the technology of 5G, 6G, 7G, cloud computing, streaming and analytics and edge computing.

This section performs *SWOT analysis* on the emerging 5G-6G-7G-8G wireless technologies. 5G is the fifth generation wireless technology for digital cellular
networks with wide deployment in 2019. The frequency spectrum of 5G is classified
as millimeter waves, mid band and low band. Low band uses a similar frequency
range as 4G. 5G millimeter wave is the fastest having actual speeds 1–2 Gbit/s
down. Frequencies are above 24 GHz reaching up to 72 GHz, above lower boundary
of extremely high frequency band. 5G mid-band is the most widely deployed in over
20 networks; speed in a 100 MHz wide band is 100–400 Mbit/s down. 5G low-band
offers similar capacity to advanced 4G; latencies between 25 -35 milliseconds. 5G
networks are digital cellular networks in which the covered service area covered is
divided into cells. Analog signals (e.g. sounds, images) are digitized by an analog to
digital converter and transmitted as a stream of bits. All 5G wireless devices in a cell
communicate by radio waves with a local antenna array and low power
automated transceiver in the cell, The local antennas are connected with telephone
network and Internet through a high bandwidth optical fiber or wireless backhaul
connection. A mobile device crossing from one cell to another is automatically hands
off seamlessly to the new cell.

6G (sixth generation) is the successor to 5G cellular technology; 6G networks are expected to use higher frequencies, higher capacity and much lower latency than 5G networks. 6G is a wireless technology that is beyond 5G. China has officially launched R&D works for 6G mobile networks. It would be about a decade before 6G comes along, NTTDoCoMo has presented the evolution of wireless technology from 3G in 2000s, 4G in 2010, 5G in 2020 and it is reasonable to expect 6G in 2030. It is not exactly known how fast 6G will be yet; it may be governed by the standards of International Telecommunication Union (ITU). If everything connects together using 5G, 6G with higher data speeds and lower latency makes instant device-to-device connection possible in various application such as autonomous cars, drones and smart cities, integration of our brains with computers and greatly improved touch control systems. 7G is the next generation communication technology. It is being adopted in Norway, China, Japan and other developed countries of the world. In Norway, Internet speed is fastest. Utilizing superior design and technology, 7G Network is expected to deliver millions of calls reliably every day,

Let us exercise *technology analysis* of 5G. The air interface defined by 3GPP for 5G is known as New Radio (NR), and the specification is subdivided into two frequency bands - FR1 (below 6 GHz) and FR2 (mmWave) each with different capabilities. The next issue is frequency range 1 (< 6 GHz ); maximum channel bandwidth defined for FR1 is 100 MHz, the most widely band is around 3.5 GHz. For frequency range 2 (> 24 GHz), minimum channel bandwidth defined for FR2 is 50 MHz and the maximum is 400 MHz, From the perspective of *performance analysis*; 5G speed is expected to range from ~50Mbit/s to over 2Gbit/s even 100Gbit/s, 100x faster than 4G. The fastest 5G, known as mmWave, delivers speeds up to and over 2Gbit/s. The latency of 5G is 8 - 12 milliseconds. It is governed by International Telecommunication Union's IMT-2020 standards. Next, let us consider the deployment of 5G, nine companies sell 5G radio hardware and 5G systems for carriers - Altiostar, Cisco Systems, Datang Telecom, Ericsson, Huawei, Nokia, Qualcomm, Samsung and ZTE Large quantities of new radio spectrum (5G NR frequency bands) have been allocated to 5G. 5G devices include Samsung Galaxy S10 5G. The technology is getting adopted and
diffused in Australia, Argentina, Bulgaria, Canada, China, Finland, Germany, India, Monaco, Netherlands, New Zealand, Norway, Pakistan, Philippines, Romania, Russian Federation, San Marino, South Africa, South Korea, Taiwan, Thailand, Uruguay, Vietnam, Qatar, Mexico, USA, Sweden and Panama.

Let us exercise *scope and usage scenario analysis* of 5G. ITU-R has defined three main uses for 5G as faster and reliable connection - Enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). Only eMBB is deployed in 2019; URLLC and mMTC are several years away in most locations. eMBB uses 5G as a progression from 4G with faster connections, higher throughput, and more capacity; mMTC is expected to connect a large number of low power and low cost devices which have high scalability and increased battery lifetime in a wide area. 5G technology may connect some of 50 billion connected IoT devices. The other interesting uses are drones in disaster management through real-time data communication, smart cities for monitoring air and water quality through sensors, Vehicle-to-Vehicle (V2V) communication, public safety (e.g. Mission-critical push-to-talk (MCPTT) and mission-critical video and data), healthcare (Ultra-Reliable Low Latency Communications [URLLC] may improve telehealth, remote patient monitoring, remote surgery and wearable computing applications), fixed wireless connections, smart home (automated home equipped with lighting, heating, or other electronic devices that can be controlled remotely by smartphone or computer).

Most cars are expected to have a 4G or 5G cellular connection for many services. 5G Automotive Association have been promoting the C-V2X communication technology. It provides for communication between vehicles and communication between vehicles and infrastructures, leading to increase in autonomous self-driving cars and IOT (Internet of Things). The speed of 5G technology in upcoming self-driving cars may be vital in helping the capabilities of autonomous cars realize their full potential (Llanasas, 2019). Current 4G network doesn't possess the required speed needed to provide self-driving vehicles that could prevent catastrophic accidents or collision (Llanasas, 2019. 5G is expected to be the basic building block of anti collision system of next generation vehicles.

So far, we have discussed the strength of the emerging wireless technologies. But, there are various constraints such as *interference, security, surveillance and health concerns*. The spectrum used by remote sensing, weather and Earth observation satellites will be significant without effective controls. The technology has health concerns; the radiation could have adverse health effects. There are concerns of data security and privacy, surveillance concerns, threats of potential espionage of foreign users by 5G equipment vendors (e.g. Australia, UK and India have taken actions to restrict or eliminate the use of a vendor's equipment in their 5G networks).

**Cloud Computing, Cloud Streaming & Cloud Analytics** : The technologies of cloud computing, cloud streaming and cloud analytics are closely associated. It is an interesting instance of technology association. With the significant advancement of information and communication technology, computing is perceived to be used as the next utility after water, electricity, gas and telecommunication. The concept can
be extended to cloud computing and grid computing for a market oriented grid. Cloud computing uses the concept of *utility computing*, *fog computing*, *grid computing* and *utility computing* [1-24]. Utility computing is associated with a parallel and distributed system that enables the sharing, selection and aggregation of geographically distributed autonomous computational resources dynamically at runtime depending on their availability, capability, performance, cost and quality through web service. The computational resources include different types of sophisticated software applications such as data mining, scientific computing and image processing, data, CPU or processing power, servers, storage devices, scanners, UPS and network interfaces which can be shared through web service. The objective of utility computing is to provide computing power and storage capacity that can be used and reallocated for any application and billed on a pay-per-use basis. Utility computing consists of a virtualized pool of information systems and other IT resources that can be continually reallocated to meet changing business and service needs of the consumers. These resources can be located anywhere and managed internally or externally. The service provider tracks the usage of computational resources of the consumers and makes invoice based on predefined price setting and usage data. An efficient resource management system coordinates and monitors the complex operation.

Utility computing supports virtualization. Cloud computing is basically a distributed computing where dynamically scalable and virtualized resources are provided as a service over the internet to achieve cost saving, easy scalability and high availability. The services offered through cloud computing usually include Software-as-a-Service (SaaS), Infrastructure-as-a-service (IaaS), Platform-as-a-service (PaaS), data-Storage-as-a-Service (dSaaS) and database-as-a-service (DaaS). SaaS allows users to run applications remotely from the cloud. IaaS provides a set of computing resources as a service which includes virtualized computers with guaranteed processing power and reserved bandwidth for storage and Internet access. PaaS includes operating systems and required services for particular applications along with data security, backup and recovery, application hosting and scalable architecture. dSaaS provides data storage, data warehousing and data mining facilities. This is a cost effective, innovative IT infrastructure from which the consumers are able to access desired computational resources and from anywhere in the world on demand.

The key technologies that enable cloud computing are virtualization, web service, service oriented architecture, service flows and work flows. The trading in cloud computing depends on several technological issues such as high availability of service, business continuity, data lock-in, security and privacy of data, efficient data transfer, performance predictability, scalable storage, efficient bugs management in large distributed system, adaptive scaling of operation, innovative software licensing and reputation mechanisms. Strategic pricing considers all these QoS factors to define optimal price setting for cloud computing. In fact, an intelligent, innovative competitive pricing mechanism and highly secure QoS can make cloud computing an attractive IT business model as compared to traditional corporate computing model based on direct IT investment. Nowadays, pay-for-use or pay-as-you-go licensing are becoming popular in cloud computing market. Thus, the
computing world is rapidly transforming towards developing information systems to be consumed as a service. Various service providers have started to build scalable data centers at various locations for hosting cloud computing.

The key players of the market of cloud computing are a set of service providers, service consumers and resource brokers. There are several challenges of trading in cloud computing : fair resource allocation protocols, optimal task scheduling, tendering, contract net protocols, auction, market clearing and negotiation mechanisms and pricing algorithms. The major threats are reduced contract duration, uncertainty, risk and variable duration of a portfolio of contracts, reduced switching costs and customer lock-in, uncertain customer demand, short life-cycle and high sunk cost. Cloud computing may require high development cost for instrumentation, provisioning and monitoring and start up costs in the face of uncertain demand. Cloud computing, web technologies and internet are expected to be extremely useful in higher education, R&D and efficient knowledge management (creation, storage, sharing, transfer and application of knowledge) in terms of innovative multi-dimensional education methodologies (e.g. projects, case discussion, consulting assignments), sources of high quality online education materials, provision of sophisticated softwares for data analysis and data visualization techniques (e.g. Youtube, online test and evaluation).

**Cloud streaming:** Cloud based mobile video streaming techniques are used in online gaming, videoconferencing, augmented reality and watching videos (e.g. movies, music) through smart phones (e.g. mobile visual search smart phones, Tablets). The scope of cloud streaming may be explored through a set of technologies such as mobile multimedia, wireless network, cloud computing, video streaming and video sharing technologies. In mobile communication network, video sharing is done through wireless link (e.g. 3G, 4G, Wi-Fi); on-demand, dynamic and easily accessible video data are provided through streaming protocols in cloud environment. Mobile devices have various constraints such as computation, memory and energy capacity. Mobile cloud computing paradigm is used in transmission of real-time data (e.g. audio, video, text, GPS) transmission; it is bridging the gap between the demand of the service consumers and capability of various mobile devices in terms of data storage and processing of video and audio data.

**Edge Computing:** Edge computing is a distributed computing paradigm which enables computation and data storage closer to the location where it is needed; improves response times and saves bandwidth. It supports data processing at or near the source of data generation. IoT connected devices interact with remote sensors and may generate data. Edge computing is a perfect fit for IoT - data is processed near the point of origin, the latency between devices and data processing layer is reduced and enable faster response and correctness in decision making. The increase of IoT devices at the edge of the communication network may generate massive amount of data to be computed to data centers and may result the constraints of network bandwidth. Data centers may not guarantee acceptable transfer rates and response times. The devices at the edge constantly consume data
from the cloud and demand the development of content delivery networks to
decentralize data and service provisioning.

The basic objective of edge computing is to move the computation away from data centers towards the edge of the network through a set of smart objects, smart phones and network gateways to perform various tasks such as service delivery, storage and IoT management and ensure improved response time and transfer rate. But there are various news issues in distributed computation such as security and privacy of data, scalability, resiliency, reliability and consistency of system performance. The data should be encrypted for the protection from hacking but it may result increased cost of computation and communication. Scalability in a distributed network should consider different constraints of system performance, energy constraints, dynamic data management and heterogeneity of IoT devices. The system should be protected in terms of liveness, fast fault detection and recovery and stability of the topology of entire distributed system. It is interesting to explore the applications of edge computing in cloud streaming, smart cities and villages and home automation systems.

Internet of Things (IoT), IIoT, RFID, Sensors: Internet of Things (IoT) is a system of interrelated computing devices, mechanical, electrical, electronics and biomedical machines, objects, animals and people with unique identifiers (UIDs) with the ability to transfer data over a network without human-to-human or human-to-computer interaction. IoT has been evolving due to the convergence of multiple technologies such as real-time analytics, machine learning, sensors, embedded systems, wireless sensor networks, control systems, automation (home and building automation), smart home (e.g. lighting fixtures, thermostats, home security systems, cameras), smart phones and smart speakers. But, there are constraints of information security and privacy. The technology has been evolving through R&D on RFID, sensor networks and Industrial IoT (IIoT). The technologies of RFID and sensors are applicable to real-time supply chain management, tracking and inventory management in logistics and retail sectors. Sensor networks can be used for real-time monitoring in defense sector and intelligent military applications. It is also interesting to deploy intelligent sensor networks (e.g. webcams, CCTVs) for real-time surveillance and development of smart cities and smart villages and intelligent transportation networks.

Information technology

Analytics: Analytics is one of the most promising digital technologies today. The technology is going through an evolution of various phases such as shallow, predictive, collaborative, big and deep analytics. Deep analytics is an intelligent, complex, hybrid, multi-phased and multi-dimensional data analysis system. The basic steps of computation are data sourcing, data filtering / preprocessing, data ensembling, data analysis and knowledge discovery from data. The authorized data analysts select an optimal set of input variables, features and dimensions (e.g. scope, system, structure, security, strategy, staff-resources, skill-style-support) correctly being free from malicious attacks (e.g. false data injection, shilling); input data is
sourced through authenticated channels accordingly. The sourced data is filtered, preprocessed (e.g. bagging, boosting, cross validation) and ensembled. It is rational to adopt an optimal mix of quantitative (e.g. regression, prediction, sequence, association, classification and clustering algorithms) and qualitative (e.g. case based reasoning, perception, process mapping, SWOT, CSF and value chain analysis) methods for multi-dimensional analysis. The analysts define intelligent training and testing strategies in terms of selection of correct soft computing tools, network architecture – no. of layers, nodes; training algorithm, learning rate, no. of training rounds and stopping criteria;. The hidden knowledge is discovered from data in terms of collective, collaborative, machine, security and business intelligence. The analysts audit fairness and correctness of computation and also reliability, consistency, rationality, transparency and accountability of the analytics.

Deep analysis (e.g. in memory analytics) can process precisely targeted, complex and fast queries on large (e.g. petabytes and exabytes) data sets of real-time and near real-time systems. For example, deep learning is an advanced machine learning technique where artificial neural networks (e.g. CNN) can learn effectively from large amount of data like human brain learn from experience by performing a task repeatedly and gradually improves the outcome of learning. Deep analytics follows a systematic, streamlined and structured process that can extract, organize and analyze large amount of data in a form being acceptable, useful and beneficial for an entity (e.g. individual human agent, organization or BI information system). It is basically a specific type of distributed computing across a number of server or nodes to speed up the analysis process. Generally, shallow analysis use the concept of mean, standard deviation, variance, probability, proportion, pie chart, bar chart and tables to analyze small data set. Deep analytics can analyze large data sets based on the concepts of data visualization, descriptive and prescriptive statistics, predictive modeling, machine learning, multilevel modeling, data reduction, multivariate analysis, regression analysis, logistic regression analysis, text analysis and data wrangling. Deep analytics is often coupled with business intelligence applications which perform query based search on large data, analyze, extract information from data set hosted on a complex and distributed architecture and convert that information into specialized data visualization outcome such as reports, charts and graphs.

Collaborative analytics is a set of analytic processes where the data analysts work jointly and cooperatively to achieve shared goals through data sharing as per revelation principle, privacy and information disclosure policy, collective analysis and coordinated decisions and actions. Collaborative analytics allows sharing of strategic information among various phases of data analysis such as demand planning of data, data sourcing, organizing data, exception management, data warehousing, execution of data analysis, reporting conclusions and determining actions. The data loop promotes data sharing to avoid redundancy. Communication and sharing improves reliability and consistency of data analysis across an organization. The analysis loop explores insights in terms of multi-dimensional analysis through a recursive process of developing and validating hypotheses for robust and complete conclusions. The action loop focuses on coordination of complete and connected set of actions across an organization through better
understanding and in-depth analytical skills. Conventional analytics explore hidden intelligence of data to make rational decisions. Collaborative analytics is focused on increased coordination, cooperation and integration among various units to improve alignment of decisions and actions across entire business unit.

***Information Security Intelligence (ISI) Analytics:*** ISI analytics is an emerging digital technology; its scope may be analyzed in terms of adaptive security, dynamic data protection, self-healing mechanism and crash proof codes. An enterprise information system may face various threats of malicious attacks from external and internal environments; it is essential to protect the system through ISI analytics. ISI analytics monitors enterprise information system in real-time to detect any anomalies and vulnerabilities. If a threat is detected, ISI analytics should be able to mitigate the risks through a set of measures. Let us consider the technology of crash proof codes which verify the reliability of an operating system through formal verification methods. The concept is applicable to the operating system designed for processors embedded in smart phones, vehicles, aircrafts, drones and medical devices where software bugs can be disastrous and unnecessarily risky programs may put lives in danger. Is it possible to mitigate the risks by making kernel i.e. the core component of an operating system in such a way that it will never crash? Section 4 outlines the concept of adaptive analytics and dynamic data protection. Session 3 highlights self-healing mechanism of a smart grid.

***Applied AI, Deep learning & Robotics:*** AI simulates human intelligence and develops algorithms that learn and perform intelligent behavior with minimal human intervention, high precision, accuracy and speed. Robotics is an interdisciplinary branch of mechanical, electrical and electronics engineering and computer science. The basic objectives of Robotics are design, construction, operation and use of robots and related information system for control, sensory feedback, and information processing; human robot interface, mobility, manipulation, programming and sensors development. Various domains of artificial intelligence (AI) are being used in Robotics such as computer vision, NLP, Edge computing, deep, transfer and reinforcement machine learning. A robot is a programmed machine designed to execute one or more tasks automatically and repeatedly with speed and precision. There are as many different types of robots. Engelberger and George Devol developed first industrial robot; there are different types of industrial robots such as cartesian, SCARA, cylindrical, delta, polar and vertically articulated. The basic components of a robot are controller (or brain run by a computer program); robotic operating system; electrical parts such as motors, sensors for sensing and touch, power sources (e.g. solar cells, pneumatics, flywheel, hydraulic); mechanical parts such as actuators, effectors, grippers, manipulators, air muscles, muscle wire, pistons, grippers, wheels, and gears that make the robot move, grab, turn and lift and locomotion: walking, hopping, dynamic balancing, flying, snaking, skating, climbing, swimming and sailing. Robotics is extensively used in automotive industry in various types of applications such as collaborative robots, painting, welding, assembly, material removal, parts transfer and machine tending. Medical robots are used for delicate surgical operation in healthcare.
Robots can substitute for humans and replicate human actions in dangerous environments (e.g. bomb detection and deactivation, toxic manufacturing environment or where humans cannot survive such as space, under water, high heat, hazardous materials and radiation) and other various types of interesting application (e.g. Cobots, Nano robots, autonomous drones in defense and bio-inspired robots). The advantages of robotics include the execution of heavy duty jobs with precision, repeatability, reliability and consistency. Is it possible to develop robots with intelligent human skills such as innovation, creativity, decision making, flexibility and adaptability, speech and voice recognition, gestures, facial expression, artificial emotions, personality and social intelligence? This is an interesting open agenfa for future generation robotics technology.

Miscellaneous

**Solar Computing:** Solar computing is an emerging technology which should be able to interact with the consumers of energy on various issues such as demand response schemes, current energy sources, information on availability of power, peak load, energy consumption, payments, discounts, variable pricing mechanisms and charging of electrical and hybrid vehicles. The objective of demand response schemes is to accommodate variable supply of renewable energy sources and high frequency monitoring of demand and supply for smart homes, buildings and micro-grids. Solar computing is associated with various types of emerging applications such as internet of energy, smart homes and autonomous micro-grids to manage and monitor the use, storage and production of electrical energy through a network of automated modules. It is essential to explore the scope of solar computing from different perspectives such as sustainability, efficiency, stability, reliability and consistency of generation, transmission, distribution and consumption of power in a complex and dynamic business environment; threats of climate change; challenges of integration between conventional energy grid and renewable energy sources, energy policy and market mechanisms. The scope of solar computing spans over several factors such as demand and supply management, electrical and hybrid vehicles, virtual power plants, prosumers and self healing networks, increased demand of electrical energy, extensive use of intermitted, distributed, clean and time variable renewable energy. The ultimate objective is to match demand with supply. A smart grid may consist of thousands of generators, power transmission and distribution network and distributed network of prosumers. The other important objectives of solar computing are to build a clean and efficient power grid for smart life-style that can support bi-directional flow of both electrical energy and information for real-time demand and supply management at economies of scale through intermittent renewable sources. Solar power is an interesting option for running computers in rural area and remote zone (e.g. forest, hills, desert, sea coast). Is it possible to explore a highly energy efficient, low cost (e.g. operation, transport and service), lightweight, rugged and reliable system that can run from direct current generated by solar panels and smart batteries in a hot and dusty hazardous environment?
**Virtual and augmented reality:** VR and AR are sophisticated, creative and powerful tools to offer digital experience by integrating AI, computer vision, graphics and automation in various applications such as manufacturing, retail, healthcare and entertainment. These reality technologies can effectively support direct-to-consumer e-commerce models through vertical integration bypassing tiers of supply chain at reduced costs and enhanced profit margin. Virtual reality technology provides immersive and interactive experiences to the human agents through computer graphics or visual elements and support V-Commerce business models through an alternate technology platform. Voice activated commerce (e.g. voice logistics for warehouse management system) uses natural language processing, speech and voice recognition technologies for the interaction between the users and commercial platforms and applications. It is an interesting agenda to broadcast the programmes of religious and cultural festivals, carnivals and special events to the viewers using the concept of virtual reality to avoid the hassles in crowd control.

**Digital twins:** How is it possible to represent the structure of a system associated with a technology innovation correctly and transparently? Digital twins may be an interesting solution; it integrates the concept of industrial IoT, AI, machine learning and software analytics to optimize the operation and maintenance of physical assets, systems and manufacturing processes. A digital twin is the digital replica of a living or non-living physical entity (e.g. physical asset, process, agent, place, system, device); it is expected to bridge and support data sharing between the physical and virtual entities. Digital twins can learn from multiple sources such as itself through sensors, historical time series data, experts and other nodes of the networking schema of the system and get updated continuously to represent real-time status, working conditions or positions.

The concept of digital twins are expected to be useful for manufacturing, energy (e.g. HVAC control systems), utilities, healthcare and automotive industries in terms of connectivity, digital traces and product life-cycle management. The concept can be used for 3D modeling to create digital companions of the physical objects i.e. an up-to-date and accurate copy of the properties and states of the objects (e.g. shape, position, gesture, status, motion) based on the data collected by the sensors attached to the system. It may be useful for the maintenance of power generation equipment such as turbines, jet engines and locomotives; monitoring, diagnostics and prognostics to optimize asset performance and utilization through root cause analysis and to overcome the challenges in system development, testing, verification and validation for automotive applications. The physical objects are virtualized and can be represented as digital twin models seamlessly and closely integrated in both physical and cyber spaces. Digital twins should represent the structure of a product innovation intelligently through various phases of the product life-cycle.

**Ray tracing:** In computer graphics, ray tracing is a rendering technique to generate an image by tracing the path of light as pixels in an image plane and simulate the effects with virtual objects. The technique can create a very high degree of visual effects at high cost of computation. This concept is useful for film, TV and video games applications. Ray tracing can simulate various optical effects like reflection, refraction, scattering, and dispersion.
**Quantum computing:** Quantum computing is a promising technology to solve computation problems significantly faster as compared to classical computers. This technology is based on the concepts of quantum mechanical phenomena such as superposition and entanglement to process data efficiently. Quantum theory analyzes the nature of energy and matter on the atomic and subatomic level. Quantum computing is a branch of quantum information science (e.g. quantum cryptography, quantum communication). In a classical computer, Boolean logic is represented through a set of bits where each bit is either 1 or 0. Quantum computers are not limited to two states and encode information as quantum bits. Qubits are the basic building blocks of quantum computing. Qubits can be in a 1 or 0 quantum state; can also be in a superposition of 1 and 0 states. When qubits are measured, the result is always either 0 or 1; the probabilities of the two outcomes depend on the quantum state of the qubits. Qubits represent atoms, ions, photons or electrons that work together to act as computer memory and a processor.

**Solar computing - self-healing mechanism for a smart grid:** Please refer to session 6 which evaluates the potential of solar power technology in terms of solar power electronics and nanotechnology of solar cells. This session explores the technology of solar computing; which is basically the support of digital technology for solar power system. Solar computing is an emerging technology which should be able to interact with the service consumers on various issues such as solar and grid energy sources, demand response schemes, information on availability of power, peak load, energy consumption, payments, discounts, variable pricing mechanisms and charging of electrical and hybrid vehicles. The objective of demand response schemes is to accommodate variable supply of renewable energy sources and high frequency monitoring of demand and supply for smart homes, buildings and micro-grids. Solar computing is associated with various types of emerging applications such as internet of energy, smart homes and autonomous microgrids to manage and monitor the use, storage and production of electrical energy through a network of automated modules. It is essential to explore the scope of solar computing from different perspectives such as sustainability, efficiency, stability, reliability and consistency of generation, transmission, distribution and consumption of power in a complex and dynamic business environment; threats of climate change; challenges of integration between conventional energy grid and renewable energy sources, energy policy and market mechanisms. The scope of solar computing spans over several factors such as demand and supply management, electrical and hybrid vehicles, virtual power plants, prosumers and self healing networks, increased demand of electrical energy, extensive use of intermitted, distributed, clean and time variable renewable energy. The ultimate objective is to match demand with supply. A smart grid may consist of thousands of generators, power transmission and distribution network and distributed network of prosumers. The other important objectives of solar computing are to build a clean and efficient power grid for smart life-style that can support bi-directional flow of both electrical energy and information for real-time demand and supply management at economies of scale through intermittent renewable sources. Solar power is an interesting option for running computers in rural area and remote zone (e.g. forest, hills, desert, sea coast)
and maximum power point tracking (MPPT) is a critical function of solar computing. Is it possible to explore a highly energy efficient, low cost (e.g. operation, transport and service), lightweight, rugged and reliable system that can run from direct current generated by solar panels and smart batteries in a hot and dusty hazardous environment?

This session shows the application of deep analytics [7-S], SWOT analysis and technology life-cycle analysis on the technological innovation of a self-healing smart power grid from the perspective of solar computing. We have done the scope analysis of the technological innovation on a smart power grid in terms of self-healing mechanism, solar computing, demand supply management, virtual power plants, electrical and hybrid vehicles and energy prosumers. This work also shows the analysis on adaptive security and dynamic data protection, strategy, staff-resources and skill-style-support for the innovation, adoption and diffusion of solar computing. It is essential to verify the security intelligence of the power grid at multiple levels and assess the risks of various types of threats from the perspectives of over current, earth fault, short-circuit, voltage, reactive power and distance protection. Finally, this work outlines a self-healing mechanism [SHM] based on case study and review of relevant works on security of smart grid. The key focus areas are solar computing, smart solar grid, self-healing mechanism, adaptive security, dynamic data protection, maximum power point tracking, AI, threat analytics, digital relay protection and Green IS.

What is a smart grid in the context of solar computing? What is the problem of technology innovation of solar computing and a smart power grid? A smart grid is a fully automated power delivery network that monitors and controls a set of nodes, supports a bidirectional flow of electricity and information between the power plants, loads and all intermittent points. Today's smart grid needs the support of distributed intelligence, broadband communication and automated control system for real-time market transactions and seamless interfaces among people, building, industrial plants, power generation, transmission and distribution networks. There are various types of challenges, complexities, constraints, security and privacy issues in power system engineering, telecommunication, cyber security, distributed intelligence, automation and information exchange among various system components of a smart grid.

It is essential to explore the scope of a smart power grid from different perspectives such as sustainability, efficiency, stability, reliability and consistency of generation, transmission, distribution and consumption of power in a complex and dynamic business environment; strength, weakness, opportunities, threats of climate change, natural disasters and acts of terrorism; challenges of integration between conventional energy grid and renewable energy sources, energy policy and market mechanisms (e.g. dynamic pricing, swing option, trading agent competition); emerging applications such as electrical and hybrid vehicles; supply chain planning, collaboration, execution and resource management; various scopes of energy informatics and green information system; computing methodologies (e.g. AI, machine learning), data, networking and security schema, application integration and intelligent analytics.
Typically, the scope of a smart grid spans over several factors such as demand and supply management, electrical vehicles, virtual power plants (VPP), prosumers and self healing networks, increased demand of electrical energy, extensive use of intermitted, distributed, clean and time variable renewable energy. The ultimate objective is to match demand with supply. A VPP may consist of thousands of generators, power transmission and distribution network and distributed network of prosumers (both consume and produce power). The other important objectives are to build a clean and efficient power grid for smart life-style that can support bi-directional flow of both electrical energy and information for real-time demand and supply management at economies of scale through intermittent renewable sources. It is essential to develop a set of coordination mechanisms for a decentralized, autonomous and intelligent smart power grid.

Let us explore the scope of Artificial intelligence (AI) for a smart power grid. AI is basically the simulation of human intelligence. It represents and executes a processes through machines. The objective of AI is how to make computers do things at which, at the moment, the people are better. A smart grid system is expected to be an intelligent knowledge based system having a set of features from the perspectives of AI: ability of learning and understanding from experience, rational reasoning, making sense out of fuzzy data or approximate reasoning, adaptive system performance i.e. sense and respond, analytical, logical and case base reasoning in solving problems and ability in dealing with complex situations. Is it possible to develop a smart grid that can mimic human intelligence? Another critical issue is heuristics search; heuristics are intuitive knowledge or rules of thumbs learnt from experience; it can reduce the complexity of problem solving. It is not required to rethink completely to solve a problem of a smart grid if it occurs repeatedly.

**Adaptive security for IIoT enabled SCADA & industrial control system :** Information Security Intelligence (ISI) analytics is an emerging digital technology; its scope is analyzed in terms of adaptive security and dynamic data protection. Supervisory Control & Data Acquisition (SCADA) & Industrial Control System (ICS) may face various threats of malicious attacks from external and internal environments; it is essential to protect the system through ISI analytics. ISI analytics can monitor SCADA / ICS in real-time to detect any anomalies and vulnerabilities. If a threat is detected, the technology should be able to mitigate the risks through a set of preventive, detective, retrospective and predictive capabilities. This session is focused on digital defense of Industrial Internet of Things (IIoT) enabled SCADA and Industrial Control System (ICS), an emerging technology in energy, utility, defense, transportation and financial service sectors. The complexity of this technology has been analyzed through deep analytics along seven 'S' dimensions: scope, system, structure, strategy, security, staff-resources and skill-style-support. It highlights technology life-cycle analysis on S-Curve; the technology is passing through growth phase at present. The complexity of technology has been analyzed based on related literature review on IIoT, SCADA and ICS and also reasoning of three cases: (a) SCADA for a smart power grid, (b) adaptive industrial control system (ICS) and (c) border security surveillance in defense. We have also outlined
security intelligence verification mechanism (SIVM) of IIoT enabled SCADA and ICS based on an adversary model and intelligent threat analytics. The key focus areas are ISI analytics, adaptive security, dynamic data protection, industrial Internet of Things, SCADA, industrial control system, deep analytics, threat analytics, adversary model, security intelligence verification algorithm, defense, smart power grid, fuzzy control and expert System.

The basic objective of the technological innovation of ISI analytics is to verify the security intelligence of SCADA and industrial control system so that the assets of information and communication technology of an enterprise are protected from various types of malicious attacks. ICS & SCADA performs key functions to provide essential services such as defense, energy (e.g. smart power grid), utilities, transportation and communication system of a country. It is a part of a nation's critical infrastructure and operates with the support of industrial control systems, supervisory control and data acquisition (SCADA), sensor networks, information and communication technologies. ICS / SCADA systems are potentially vulnerable to various types of malicious attacks which may affect the safety of common people and the performance of critical infrastructure seriously and may cause huge financial loss.

Any comprehensive and complete solutions are missing in the existing works for the verification of security intelligence of SCADA / ICS. The present work evaluates the technology associated with IIoT enabled SCADA / ICS and also outlines algorithmic security intelligence verification mechanism (SIVM) in terms of system, input, output, objectives, constraints, moves, revelation principle, payment function, security intelligence and proactive and reactive risk mitigation approaches. The security intelligence of ICS / SCADA is explored in association with computing, data, networking, application and security schema at five levels: L1, L2, L3, L4 and L5 in terms of intrusion and access control, secure multi-party computation, system performance, malicious attacks and multi-party corruption respectively through an intelligent threat analytics. The present work assesses the risk of different types of threats on ICS / SCADA and presents a set of intelligent verification mechanisms which can protect the system from potential malicious attacks. The verification mechanisms are based on cryptography and distributed secure multi-party computation. An efficient system is expected to be resilient. Resiliency measures the ability to and the speed at which the system can return to normal performance level following a disruption. The vulnerability of the system to a disruptive event can be viewed as a combination of likelihood of a disruption and its potential severity.

Let us first present a deep analytics for IIoT enabled ICS and SCADA. It is basically an integrated framework which is a perfect combination or fit of seven factors. A complex operation of ICS and SCADA may fail due to the inability of the system administrator to recognize the importance of the fit and the tendency to concentrate only on a few of these factors and ignore the others. These factors must be integrated, coordinated and synchronized for effective SCADA operation. The panel has defined the technology of IIoT enabled SCADA and ICS. They have analyzed the technology through scope, system, structure, strategy, security, staff-resources and skill-style-support respectively. Section 5 outlines security intelligence verification mechanism (SIVM). They have also analyzed three test cases: (a)
SCADA for a smart power grid, (b) industrial control system and (c) defense for border security surveillance.

Supervisory control and data acquisition (SCADA) networks perform key functions to provide essential services for energy and utilities (e.g. electricity, oil, gas, water), defense and communication sectors. SCADA is a part of a nation's critical infrastructure and operates with the support of industrial control systems, sensor networks and information and communication technologies. SCADA networks are potentially vulnerable to various types of malicious attacks that result disruption of critical security services in terms of confidentiality, integrity, availability, authentication, non-repudiation, access and inference control, intrusion, process redirection or manipulation of operational data. These attacks may affect the safety of common people and the performance of critical infrastructure seriously and may cause huge financial loss. Therefore, the protection of SCADA networks should be a national priority.

Industrial control systems for critical infrastructure like national power grid make increasingly use open technologies and Internet protocols. A smart energy grid often faces the challenge of malicious attacks (e.g. cyber attack) from power play and politics, industrial espionage and terrorists and also compromises of information infrastructure due to user errors, equipment failure and natural disaster. A malicious agent may be able to penetrate an energy grid, gain access to control software and hardware and alter load conditions to destabilize the grid in an unpredicted way.

Let us analyze the scope of IIoT technology. Internet of Things (IoT) is a networked smart devices equipped with sensors and RFID tags, connected to the Internet, all sharing information with each other without human intervention, dynamic global network infrastructure with self configuring capabilities, standard interoperable communication protocols, intelligent interfaces and are seamlessly integrated with information network. The fast development of networked smart devices with Internet, sensors and radio frequency identification devices (RFID) is enabling the emergence of many new applications in remote access control, effective monitoring and supervision, better performance, real-time decision making, system integration and access to cloud based resources. IIoT is used for various types of industrial applications such as aerospace and aviation, airports, automotive, environment monitoring, food technology, smart cities, intelligent buildings, intelligent transportation infrastructures, healthcare, operation in hazardous environment, retail, logistics, supply chain management and monitoring of safety and security of plant (e.g. advanced metering, energy management, interaction with smart appliances).

Next, let us explain the scope of digital defense: how to protect information and communication technology (ICT) assets of an enterprise from various types of malicious attacks. Why should a business model address information security in an effective and comprehensive way? It is basically a problem of risk management; attacks on ICT technology schema and the theft or misuse of critical data should be assessed and mitigated appropriately for digital transformation. Digital defense is associated with a complex process which includes identification of information assets, assessment of critical vulnerabilities, selection of appropriate security
solution for the protection of ICT schema, trusted computing environment
development, preserving the authenticity, authorization, correct identification,
privacy and audit of electronic transactions, access control and defining security
policy, process and maintenance intelligently [1]. This work talks about traditional
issues of user permission control, confidentiality by encryption, authorization
through passwords, tokens, digital certificates, role based access control, audit trails
and digital signature for the protection of general information assets such as data on
sales, customers, vendors and human resources.

The existing works have been reviewed on system architecture, computational
intelligence of verification mechanisms, potential threats and vulnerabilities,
security concerns and risk analysis of various types of industrial control systems
and SCADA. The review of existing literature could not find out an efficient
mechanism for SCADA / ICS. The existing works have several gaps. The security
intelligence is expected to be defined strongly with robustness, completely and
precisely. The protocols should have intelligent model checking or system
verification strategies based on rational threat analytics. The present work has
identified three critical issues to be associated with digital defense of information
and communication technology assets of ICS and SCADA: threats analytics,
verification mechanisms of security intelligence and relevant computational
challenges. Case based reasoning approach is adopted also for experimenting three
test cases on smart power grid, industrial plant and defense SCADA.

This session assesses the risk of different types of threats on ICS & SCADA and
presents a set of intelligent verification mechanisms which can protect the system
from potential malicious attacks. The mechanisms are based on cryptography and
distributed secure multi-party computation and check the security intelligence of a
resilient system from the perspectives of intrusion detection, secure communication,
service oriented computing, credential based biometric access control, privacy and
inference control. The research methodology adopted in the present work includes
case analysis and review of relevant literature. The logic of the verification
mechanisms has been explored through analysis of three cases.

**Secure Multi-party Quantum Computing for authentication through signcryption:**

This session analyzes the complexity and potential of secure multi-party quantum computing (SMQC) technology in terms of seven elements of deep analytics : scope, system, structure, security, staff-resources and skill-style-support. Quantum computing is a promising technology to solve computation problems significantly faster as compared to classical computers. The technology is at emergence phase of S-curve. This session explores a set of interesting scope of secure multi-party quantum computing : how to construct various concepts of cryptography such as encryption and decryption, digital signature, signcryption, authentication, zero knowledge proof and oblivious transfer in the context of secure multi-party quantum computing? How to construct secure multi-party quantum computing protocols verifying security intelligence at various levels : L1 (access control), L2 (computational intelligence), L3(system performance), L4(malicious attacks) and L5(multi-party corruption)? This session presents SMQC algorithm and analyzes the complexity of the algorithm in terms of verification of security intelligence. The
key focus areas are secure multi-party computing, quantum computing, information
theory, secret sharing, authentication, signcryption, authorization, correct
identification, privacy, audit and multi-party corruption.

Secure multiparty computation allows a group of mutually distrustful parties to perform correct, distributed computations under the sole assumption that some parties will follow the protocol honestly. It has been studied extensively in the classical setting and may be extended to the setting of quantum computing. A secure quantum multiparty protocol allows n parties $P_1, ..., P_n$ to compute an n input quantum circuit where each party $P_i$ is responsible to provide one of the input states. The output of the circuit is decomposed into n components $H_1 \otimes ... \otimes H_n$ and $P_i$ receives the output $H_i$. There exists pairwise quantum channels and a classical broadcast channel among n parties, there exists a universally composable, statistically secure multiparty quantum computation protocol that tolerates an adaptive adversary controlling up to $t < n/2$ faulty parties.

The complexity of the protocol is polynomial in terms of the number of parties and the size of the circuit. The critical issue is how much trust is necessary for secret sharing: how many parties must remain honest for distributed quantum computation. A verifiable quantum secret sharing protocol and a general secure multiparty quantum computing protocol can tolerate any [(n-1)/2] cheaters among n parties. Is it possible to improve the threshold of an efficient secure multiparty quantum computing protocol? The scope of secure multi-party quantum computing may be explored in terms of a set of open issues: how to construct various cryptographic algorithms such as encryption and decryption, digital signature, signcryption, authentication, authorization, correct identification, privacy, secret sharing, zero knowledge proof and oblivious transfer in the context of secure multi-party quantum computing? How to tailor secure multi-party quantum computing protocols verifying security intelligence at various levels: L1 (access control), L2 (computational intelligence: fairness, correctness, transparency, accountability, rationality, trust), L3(system performance: reliability, consistency, resiliency). L4(malicious attacks: Sybil attack, false data injection attack, denial of service attack) and L5(multi-party corruption)? How to construct the protocols of oblivious transfer, secure function evaluation, mixnet and private comparison protocols in the setting of secure multi-party quantum computing? The next section presents Secure Multi-party Quantum Computing (SMQC) algorithm where a client sendS challenge to an administrator of private data schema and receives authenticated response. Authentication is done through signcryption algorithm.

*Secure adaptive filter in adversarial environment – threat analytics:* This session analyzes the complexity and potential of secure adaptive filter in adversarial environment in terms seven elements of deep analytics: scope, system, structure, security, staff-resources and skill-style-support. The contributions of this work is follows. The scope of adaptive filters has been analyzed in terms of a set of interesting data base and communication networking applications, distributed systems, data streaming, packet classification, spam filtering and web caching. The system element presents algorithmic secure adaptive filter mechanism which
outlines two differe types of filter : (a) private search and (b) optimal margin
classifier enabled private search. We have explored whether private Support Vector
machine based on optimal marging classifier can be applied to the problem of
packet classification in communication networks. The structure element highlights
various types of filters such as Bloom, Cuckoo, Adaptive Cuckoo and parallel D-
piepline and their strength and weakness. The technology is at emergence phase of
S-curve. This work also outlines how to verify the security intelligence of adaptive
filter at multiple levels : L1 (access control), L2 (computational intelligence), L3
(system performance), L4 (malicious attacks) and L5(multi-party corruption).
Specific focus has been given to correctness (e.g. false positive and false negative)
and privacy of computation by the adaptive filter. The correctness of the filter is
deeply associated with right configuration of the data schema. The privacy of
computation can be ensured through a robust revelation principle of the filter. The
key focus areas are adaptive filter, bloom filter, cuckoo filter, adversarial
environment, secure multiparty computation, threat analytics, optimal margin
classifer, private support vector machine.

## Scope Analytics

Objects : Secure adaptive filter;

Scope : Data base applications, Distributed systems, Data streaming, Communication networking applications, Packet classification, Spam filtering, Web caching

Secure adaptive filter is a simple space efficient randomized data structure for
representing a set to support membership queries. It allows false positives but the
saving in space complexity often outweigh this drawback when the probability of an
error is made sufficiently low. The scope of secure adaptive filters has been
explored in terms of a set of interesting data base and communication networking
applications, distributed systems, data streaming, packet classification, spam
filtering and web caching. The filters are used for collaborating in overlay and peer-
to-peer networks, resource routing, packet routing and measurrement for making
data summary in routers and other various types of devices in intelligent
communication network. The filters are used as a data structure for approximate
set membership by reacting to false positives. The filter is used as data dictionaries
in table look up and UNIX spell checkers, as a means of storing a dictionary of
unsuitable passwords for information security and also distributed database
applications. The filters are also used for distributed web cache sharing protocols,
desktop web browsing for P2P/Overlay networks, approximate set reconciliation for
content delivery, set intersection for keyword searches, resource routing in P2P
networks, geographic routing for mobile computers and packet routing applications
(e.g. early detection of forwarding loops in unicast and multicast protocols, queue
management, multicast) and to provide a reasonable measuremrt infrastructure
(e,g, recording heavy flows, IP traceback, how many packets from a given flow pass
through a router? has a packet from this source passed through this router
recently?)
## 2. SYSTEM

### System Analytics

Prof. Simon Watson and Dr. Henry Plank are exploring the systems associated with emerging digital technologies. The basic objectives of digital technology is to intelligent decision making in complex and rapidly changing business environment, fast decision making in adaptive situation, improved accuracy in decision making, discovery of hidden intelligence from large pool of data, fast and correct transaction processing; support creation, storage, transfer and application of knowledge in an enterprise, support office automation and efficient management of resources (e.g. man, machine, materials, method and money) of an enterprise. An information system associated with digital technology can be classified into different categories such as transaction processing, decision support, group decision support, knowledge management, knowledge based office automation and business intelligence system. It is possible to analyze digital technology in terms of computing (e.g. centralized, distributed, local, global), data, networking (e.g. wired, wireless, Internet), application (e.g. features, modules, functions, application integration) and security schema.

**dSaaS / DaaS** : The basic objective of DaaS is to avoid the complexity and cost of running a database with improved availability, performance, price and flexibility. It gives the access to various types of business intelligence solutions (through web) which include distributed database, data warehousing, data mining, business and web analytics, data visualization and business performance measurement applications. The pricing of dSaaS is based on the cost of hardware (e.g. data warehouse, servers), the cost of software (e.g. business intelligence solutions) and system administration cost (e.g. data centre administration, data base security, backup, recovery and maintenance). A consumer can lease a data storage space where it is required to measure different system parameters such as stored data (GB/month) and number of processed queries (per 10k requests / month) to compute the price of dSaaS / DaaS. The provider can offer quantity discount in case of group buying of storage space. The prices of DaaS / dSaaS are also determined by various QoS parameters such as connection speed, data store delete time, data store read time, deployment latency (i.e. the amount of latency between when an application is posted and ready to use) and lag time (how slow the system is). The pricing of dSaaS is also governed by the security and privacy of data. Some applications (e.g. education sector) require low level of privacy of data. Some applications (e.g. financial service, healthcare) need high level of security and privacy in data outsourcing and this involves high cost of computation and communication from the perspectives of statistical disclosure control, private data analysis, privacy preserving data mining, intelligent access control and query processing on encrypted data. The service provider should define a discriminatory pricing mechanism for dSaaS: high level of security and privacy of data demands high price and low level of security asks low price.
The price of dSaaS is a function of miscellaneous cost elements of a data center. A data centre or data bank is the collection of servers where the applications and data are stored. Data center consists of a set of servers and network architecture. The servers store the data from different organizations and network architecture facilitates the services to use, store, and update the data of the servers. The cost of administration of data centre includes several factors: initial development cost, operating cost, maintenance cost and cost associated with disaster recovery plan. The development cost includes the cost that requires making master plan, building infrastructure, buying hardware and software, making database and security schema. Operating cost includes the cost of energy, cooling system, system administrators, software license and network cost. Maintenance cost is the cost of maintaining the system which includes upgradation of hardware and software. One of the most challenging issues of data center management is the resource allocation strategy: how it is possible to cater the demand of the service consumers using minimum number of servers. It has an impact on the size, complexity and cost of data center. The data centre administrator can follow dedicated or shared server allocation strategy.

The price of dSaaS is also a function of energy consumption of cloud computing system in a data center. There are many open challenges of energy efficient design of computing systems and green IT covering the hardware, operating system, virtualization and data center levels. The basic objective of the cloud computing system design has been shifted to power and energy efficiency to improve the profit of the service provider. Energy consumption is not only determined by hardware efficiency, but it is also dependent on the resource management system deployed on the infrastructure and the efficiency of applications running in the system. Solar power electronics is an interesting option of green IT. Higher power consumption results not only high energy cost but also increases the cost of cooling system and power delivery infrastructure including UPS and power distribution panels. The consolidation of IT infrastructure should be done intelligently to reduce both energy consumption and performance degradation through improved power management. Energy consumption can be reduced by increasing the resource utilization and use of energy efficient cloud computing system.

Software-as-a-Service (SaaS): SaaS is an application hosted on a remote server and accessed through web; it can be business service or customer oriented service. The basic objective is to reduce software licensing cost and improve productivity by using sophisticated applications. The pricing strategy of SaaS is based on pay-as-you-go basis; not dependent on number of licensing period and licensing users as in case of direct software procurement. Another concept is software plus service where an enterprise uses a locally hosted software application and additionally uses SaaS through cloud for a specific type of application. Using the existing software paradigm, the consumer purchases a software package and license by paying a one-time fee. The software then becomes the property of the consumer. Support and updates are provided by the vendor under the terms of the license agreement. This can be costly if the user is installing a new application on hundreds or thousands of computers. SaaS, on the other hand, has no licensing. Rather than buying the
application, the consumer pay for it through the use of a subscription based on number of concurrent users and only pay for what is used.

The computation of subscription fee can be stochastic pricing or simple cost based pricing. The price of SaaS depends on specific business model of the service provider. Suppose, a service provider develops in-house software products. Another service provider buys COTS from third-party vendor based on number of licensed users and licensing period and provides SaaS to the consumers. There may be restriction of number of concurrent users and different subscription rate of SaaS in second case.

This pricing strategy should also consider cost of upgrading software application; the provider may offer incentive for upgrading applications. In case of security software pricing, there may be different alternative strategies to manage network security: (i) consumer self-patching where no external incentives are provided for patching or purchasing, (ii) mandatory patching, (iii) patching rebate and (iv) usage tax. For proprietary software, when the software security risk and the patching costs are high, a patching rebate dominates the other strategies. When the patching cost or the security risk is low, self-patching is the best option.

Stochastic risk based pricing mechanism considers several risk factors and optimizes the expected net present value of revenue subject to maximum acceptable risk of the provider. In this case, the service provider does not give much focus on cost accounting model or profit margin but tests the price sensitivity of the customers experimentally or through trial and error method. The provider does not have any precise perception about the demand of the new software products. But, it follows dynamic risk based pricing based on assessed risks and competitive intelligence. For in-house software development, software cost is a function of efforts on feasibility study, requirement analysis, system design, program design, coding, testing and modification following waterfall / v-process / spiral / prototyping / incremental delivery model. The service provider estimates effort for a specific SDLC model and then selects an optimal profit margin.

**Infrastructure-as-a-Service (IaaS):** A cloud computing infrastructure consists of different types of elements: clients (e.g. mobile, PDA, laptop, thin and thick), the data center and distributed servers. *Thin clients* are less costly than thick clients. A growing trend in the cloud computing is *virtualization* of servers. In a virtualized environment, applications run on a server and are displayed on the client. The server can be local or on the other side of the cloud. Software can be installed allowing multiple instances of virtual servers which run on a physical server. Full *virtualization* is a technique in which a complete installation of one machine is run on another. It allows the running of different and unique operating systems.

**Hardware-as-a-Service (HaaS)** simply offers the hardware required by a consumer. Cloud computing is a business model of delivering IT resources and applications as services accessible remotely over the Internet rather than locally. IaaS supports remote access of computer infrastructure as a service.

Cloud computing supports elastically scaling computation to match time varying demand. But, the uncertainty of variable loads necessitate the use of margins i.e. the servers that must be kept active to absorb unpredictable potential load surges which
can be a significant fraction of overall cost. There are challenges of minimizing
margin costs and true costs for IaaS. The provider should not adopt a fixed margin
strategy; the margin should be load dependent. The margin required at low loads
may be higher than the margin required at high loads. Secondly, the tolerance i.e.
the fraction of time when the response time target may be violated need not be
uniform across all load levels. It is really challenging to achieve optimal margin cost
while guarantying desired response time for IaaS.

The pricing strategy of IaaS is based on the cost of servers, storage space, network
equipment and system software like operating systems and database systems. The
price of IaaS is basically a subscription fee for a specific timeline. Now the question
is how to compute this subscription fee. The rate should be fixed based on the cost of
hardware and software, target revenue and profit margin. The service provider may
adopt a profit maximizing pricing strategy or revenue maximizing pricing strategy
within reasonable, stable target profit margin. The profit margin is a dynamic
variable; it should be set intelligently according to competitive intelligence and
quality of service. The quality of service is measured in terms of computing time.
For small firm or individual service consumer, the provider can set a fixed price per
unit time; there may be SLA but there is no scope of negotiation of price. Large PSU
can negotiate with the service provider to set a rational price for fixed timeline.

Incentive compatibility plays a significant role in IaaS pricing, it is important to
analyze the significance of incentives for network infrastructure investment under
different pricing strategies: *congestion based negative externality pricing* and the flat
*rate pricing*. A lack of proper infrastructure investment incentive may lead to an
environment where network growth may not keep pace with the service
requirements. It is really complex to compute maximum capacity that IaaS provider
will be willing to invest under different pricing schemes. Optimal capacity of IaaS is
determined by different factors: per unit cost of capacity of network resources,
average value of the user's requests, average value of the user's tolerance for delay
and the level of exogeneous demand for the services on the network. It is hard to
determine whether time based pricing is more profitable than flat rate pricing. IaaS
consumers always try to identify whether average stream of the net benefits realized
under congestion based pricing is higher than the average net benefits under flat
rate pricing. IaaS provider may adopt different types of pricing strategies at
different points of time but the service consumers may control their demand of IaaS
service adaptively to avoid the increase in cost.

*Platform-as-a-Service (PaaS)* : PaaS supplies all the resources required to build
applications and services completely from the web without any download or
installation of any software in the clients. The price of PaaS can be negotiated for a
specific project. There can be different types of project environments such as
application-delivery-only-environment (e.g. security and on demand scalability),
standalone environment and add-on-developmental-environment (e.g. subscriptions
of add-on SaaS application are bought). The price of system software can be
charged as a subscription fee based on number of concurrent users and usage
period. The pricing of PaaS is also governed by the complexity of platform services
which may include application design, development, testing, deployment, hosting,
geographically dispersed team collaboration, web service integration, database integration, security, scalability, storage, state management and versioning. The developers, project managers, and testers can access the development and testing softwares of the service provider through web; but lack of interoperability and portability may be a critical issue in PaaS. The price of PaaS is determined by the complexity of interoperability between the systems of the service provider and service consumer.

***Virtual and Augmented Reality*** : There are three types of reality technologies: virtual reality (VR), mixed reality (MR) and augmented reality (AR). These reality technologies are sophisticated, creative and powerful tools to offer a complete computerized digital experience through artificial intelligence, computer vision, computer graphics and automation. A virtual entity may not exist physically but created by software in a digital environment. Augmented reality is an enhanced version of the real-world by overlaying our existing reality with an additional layer of digital information, which can be viewed through smartphones or smart glasses (ARSGs). Mixed reality facilitates the merger of and real-time interaction between, digitally rendered and real-world data and objects through MR headset. Virtual reality is characterized by generating real-time, immersive and interactive multi-sensory experiences situated in and artificially induced by a responsive three-dimensional computer-generated virtual environment - usually paired with advanced input and output devices.

The expert panel are analyzing a smart grid in terms of self-healing network. The basic building blocks of a self healing network are computationally efficient state estimation algorithms that can predict voltage and phase at different nodes of a smart grid in real-time given the current and predicted energy demand and supply of the prosumers. Distributed coordination is important for automated voltage regulators, voltage control and balancing demand and supply during recovery of faults. It is really challenging to develop automated and distributed active network management strategies given the uncertainty of demand and supply at different levels in the smart grid, fault correction mechanisms, self healing strategies, cause-effect analysis on various types of faults (e.g. overload, over current, earth fault, short circuit, over voltage, under voltage, over frequency, under frequency, automatic voltage regulation). An active network configures the topology automatically, sends control signals to individual customers to adjust generation and also load control, automatically correct faults and self-heals the smart grid.

A self-healing mechanism should maintain the stability of a distribution network, perform accurate and timely monitoring and control of the prosumers; big data analysis for multiple actors and sensors, micro-level measurement and predict the future state of smart grid. It can adopt a set of active network management techniques based on distributed intelligence in the self healing network for fast recovery from faults. In case of voltage drift, automatic action is necessary on the transformer to reestablish correct voltage levels. It is essential to balance the mismatch between supply and demand to avoid blackout situation. Essential need of a self healing mechanism is that various components of a smart grid should be able
to communicate for voltage regulation and control of generation capacity and load demand.

Artificial intelligence can be applied to a smart grid in various ways such as knowledge based expert system for knowledge acquisition, inference engine, knowledge base, applications, fault diagnosis; real time alarm handling and fault analysis (AHFA); voltage control such as voltage collapse monitor (VCM), reactive power management (RPM), combined active and reactive dispatch (CARD), power system protection for protective relay setting, phase selection, static security assessment, condition monitoring, scheduling and maintenance of electrical transmission networks and intelligent system for demand forecasting. It is interesting to apply various types of soft computing tools for smart grid system performance analysis. Adaptive fuzzy system is used for fuzzy reasoning, defuzzification and function approximation on imprecise and uncertain data. Artificial neural network can be useful for intelligent data mining, neuro-fuzzy control, neuro expert power system and evolutionary computing (e.g. neuro GA).

Let us do technical analysis on evolution of AI in a smart grid system. A knowledge based expert system captures the knowledge of human expert in a specific domain. It uses the knowledge for decision making and appropriate reasoning of complex problems. The expert system needs a knowledge structure in the form of production rules, frames and rules. A knowledge base is a form of database containing both facts and rules. Let us present here examples of few rules.

* Rule 1: If X = True, Then Y = True.

* Rule 2: If X = True and Y = True then Z = True.

* Rule 3: IF I is An isolator AND current through I is 0 AND I = closed
  o THEN open I.

* Rule 4: AND Rule - A.B = X.

* Rule 5: NAND Rule - NOT (A.B) = NOT (X).

* Rule 6: OR rule - A+B = Y.

* Rule 7: NOR rule - NOT (A + B) = NOT (Y).

* Rule 8: XOR rule - A ⊕ B = Z /* NOT (A). B + A. NOT (B) = Z*/

An expert system (ES) can function based on knowledge of power system operation which may or may not be complete. An ES performs several functions such as knowledge acquisition and inference engine [16]. Data mining algorithms analyze SCADA data. This component acquires new facts or rules. Inference engine performs several functions such as verification, validation, cause-effect analysis, sequence control for rule firing, data processing, meta knowledge management, forward and backward chaining. But, ES may have some limitations from the perspectives of inappropriate representation of knowledge structure. Expert systems can be used in power system analysis

* Planning: AC/DC network design, power plant management, capacity planning;

* Operation: alarm processing, fault diagnosis, forecasting. Maintenance scheduling, demand side management, reactive voltage control;

* Analysis: Control system design, power system protection and coordination;

* AC load flow analysis: Input data includes network parameters, connections, loads, maximum active and reactive power output. It minimizes a set of
objective functions subject to a set of constraints such as network laws, plant
loading limits, busbar voltage limits, line loading constraints and security.

Case : Self-healing smart solar grid

North American power grid, one of the greatest engineering innovations of 20th
century is now 50 years old and needs a smart self healing grid to incorporate
renewable energy sources, reduced number of power outages and reduced carbon
emissions [44]. North America has already experienced a number of power outages.
In 2012, the occurrence of hurricane Sandy caused power outages in twenty four
state of USA and shut down of schools and offices. In 2003 a blackout occurred for
two days throughout North-eastern and Midwestern parts of USA and United States
and the Canadian province of Ontario. The cause of the blackout was a software
bug in the alarm system at a control room of the First Energy Corporation in Ohio.
In 2011, New England experienced a Halloween snowstorm that put millions of
people in the dark for several days. The following section presents a self healing
mechanism [SHM] for the smart power grid.

Self Healing Mechanism [SHM]

Agents : Service consumers (B)[e.g. smart home, smart building, industrial load, solar pump for agriculture, microgrid], Service provider (S);

Structure

* Smart power grid comprising of power generation, transmission and distribution system, generators, transformers, transmission lines, loads, switchyards, microgrids comprising of AC / DC sources and loads, renewable energy sources (e.g. PV panels) and energy storage system;

* fully automated power delivery network that monitors and controls a set of nodes, supports a bidirectional flow of electrical power and information among the power plants, loads and all intermittent points;

Scope:

* ensure stability, reliability, consistency and improved efficiency during normal operating conditions;

- self-recovery during human error or natural disaster;

* enable better integration between conventional grid and renewable energy sources;

- mitigate the impact of power outages;

- ensure fewer blackouts for shorter periods;

- maintain the stability of the smart grid;

* perform accurate and timely monitoring and control of the prosumers;

- big data analysis for multiple actors and sensors through micro-level measurement;

- predict the future state of smart grid;

- fast recovery from faults;
* automatic action on the transformer to reestablish correct voltage levels in case of voltage drift

* balance the mismatch between supply and demand to avoid blackout situation

* various components of a smart grid should be able to communicate for voltage regulation and control of generation capacity and load demand;

* Constraints: time, cost, technology;

Strategy: Select a set of intelligent strategic moves rationally.

* Call deep analytics: ‘7-S’ model

* Automated model checking and system verification

* Real-time smart grid monitoring; adaptive and resilient approach in fault analysis and fault clearing

* Adoption of self-stabilizing and self-organizing distributed network management strategy

* Digital power system protection system for giving alarm / alert in time, voltage and reactive power control

* SWOT analysis: AI enabled smart grid has more benefits in terms of cost, flexibility and

* TLC analysis /* AI enabled smart grid is at growth phase of S-curve today */.

System: AI enabled expert system

* Input: Demand plan of B, Supply plan of S;

* Output: Energy contract;

* Protocol:

* define and configure expert system in the form of knowledge base, knowledge acquisition system, inference engine, workplace or memory, justifier, user interface, knowledge refining system and consulting environment;

* develop self-stabilizing and self-organizing distributed network management algorithms;

* call computationally efficient state estimation algorithms that can predict voltage and phase at different nodes of a smart grid in real-time given the current and predicted energy demand and supply of the prosumers;

* distributed coordination for automated voltage regulators, voltage control and balancing demand and supply during recovery of faults;

* automated and distributed active network management strategies given the uncertainty of demand and supply at different levels in the smart grid, fault correction mechanisms, self healing strategies, cause-effect analysis on various types of faults;

* Configuration of the network automatically, sends control signals to individual customers to adjust generation and also load control, automatically correct faults and self-heals the smart grid.
* verify security intelligence through automated or semi-automated system verification.

* Adaptive security for dynamic data protection through preventive, detective, retrospective and predictive capabilities.

* call threat analytics and assess risks on smart grid; analyze performance, sensitivity, trends, exception and alerts.

* what is corrupted or compromised: agents, communication schema, data schema, application schema and computing schema ?

* time : what occurred? what is occurring? what will occur? assess probability of occurrence and impact.

* insights : how and why did it occur? do cause-effect analysis.

* recommend : what is the next best action?

* predict : what is the best or worst that can happen?

* Verify security intelligence of a smart grid at various levels such as L₁, L₂, L₃, L₄ and L₅.

* Level L₁ verifies system performance in terms of stability, reliability, consistency, safety, liveness, robustness, resiliency, deadlock-freeness and synchronization.

* Unsymmetrical fault analysis

* Shunt type faults

* Line-to-Line fault

* Single Line-to-Ground fault

* Double Line-to-Ground fault

* Series type faults

* Open conductor fault

* Symmetrical fault analysis

* Smart grid stability analysis

* Transient stability

* Steady state stability

* Voltage stability analysis

* Smart grid security analysis

* Contingency analysis

* Sensitivity analysis

* Level L₂ verifies access control in terms of authentication, authorization, correct identification, privacy, audit, confidentiality, trust and commitment of the users and system administrator.

* Level L₃ verifies computing schema in terms of fairness, correctness, transparency, accountability and accuracy of measurement of data.

* Level L₄ verifies the efficiency of digital relay protection such as overload, over current, earth fault, short circuit, over voltage, under voltage, over frequency, under
frequency, automatic voltage regulation, reactive power control and distance protection;

* Level L₅ assesses the risks of various types of malicious attacks on a smart grid such as denial of service (DoS), sybil attack, false data injection attack and coremelt attack.

* Revelation principle : B and S preserve privacy of energy contract as per revelation principle;

Staff-Resources : system administration, technical, management, operation, maintenance;

Skill-Style-Support :

* intelligent coordination and integration among 7-S elements

* Proactive and preventive support

* Reactive support

Payment function :

* The agents settle single or multiple intelligent service contracts.

* Collaborative planning, forecasting and replenishment (CPFR)

* Swing option

* verify business intelligence of service contracts in terms of (pay per use, payment mode, payment terms).

A smart self-healing grid is a sophisticated electrical platform that is expected to ensure stability, reliability, consistency and improved efficiency during normal operating conditions; self-recover during human error or natural disaster and enable better integration of renewable energy sources. A self-healing grid should be able to mitigate the impact of power outages during polar vortex, flood, cyclone or snow storm and must ensure fewer blackouts for shorter periods. Self-stabilization and self-organization are two important properties of a self-healing smart grid. Availability, robustness and the possibility for on-demand reconfiguration of distributed complex systems are important in various types of applications such as self-healing smart grid, dynamic sensor network and communication network. A smart grid is expected to be a self-stabilizing system that reaches an arbitrary inconsistent state due to the occurrence of transient faults but can recover automatically from such faults and converge to a desired state. It is not only applicable to a large, distributed and heterogeneous smart grid but also to routing algorithms in communication networks.

Is it possible to adopt collaborative planning, forecasting and replenishment (CPFR) as a strategic tool for comprehensive value chain management of a group of trading agents associated with the smart grid? It may be an interesting initiative among all the stakeholders of the smart grid in order to improve their relationship through jointly managed planning, process and shared information. The interplay between trust and technology encourages the commitment of collaboration among the trading agents.

Let us consider a specific scenario of multi-party negotiation in trading of smart grid. Swing option is a specific type of supply contract. It gives the owner of the
swing option the right to change the required delivery of a resource through short time notice. It gives the owner of the swing option multiple exercise rights at many different time horizons with exercise amounts on a continuous scale. A typical swing option is defined by a set of characteristics and constraints. There are predefined exercise times $t_i$, $i \in [1, 2, ..., n]$, $1 \le t_1 \le t_2 \le ... \le t_n \le T$ at which a fixed number of $d_0$ units of a resource may be obtained. With a notice of specific short period, the owner of the option may use swing right to receive more (up-swing) or less (down-swing) than $d_0$ at any of $n$ moments. The scheme permits swing only at $g$ out of possible $n$ time moments where $g \le n$ is swing number constraint. A freeze time constraint forbids swings within short interval of the moments. The local constraints up-swing [$\alpha$] and down-swing limits [$\beta$] define how much the requested demand $d_i$ at time $t_i$ may differ from $d_0$.

There are two global constraints which restrict the total requested volume $D$ within the contract period by maximum total demand ($\gamma$) and minimum total demand ($\lambda$). The option holder must pay penalty determined by a function for violating local or global constraints. In this contract, the primary negotiation issue may be a discriminatory pricing plan which depends on the negotiation of a set of secondary issues such as up-swing limit, down-swing limit, maximum total demand, minimum total demand, penalty function and number of swings for a specific period.

Self-stabilization is well defined but self-organization property is not. Self-organization satisfies locality and dynamicity. A smart grid is self-organizing if the distributed algorithm associated with it converges or stabilizes in sub-linear time with regards to the number of nodes and reacts fast to the changes of the topology of the distributed network. The addition and removal of nodes influences a small number of states of other nodes of the distributed network. If $s(n)$ is upper bound on the convergence time and $d(n)$ is upper bound on the convergence time following a change in topology, then $s(n) \in o(n)$ and $d(n) \in o(s(n))$. The algorithm converges in $O(\log n)$ expected number of rounds, responds to dynamic changes locally and is therefore self-organizing.

In a distributed smart grid, it is hard to predict in advance the exact combination of failures and the systems require intelligent and complex coordination mechanisms among the processors, computers and communication networks. A distributed system can be modeled by a set of $n$ state machines or processors ($P_{i,i=1,...,n}$) which communicate with other neighbors. A distributed smart grid can be represented by a graph $G = (V,E)$ in which each processor is known as node and each two neighboring nodes are connected by a link of the graph. Each node runs a program and changes state with execution of each executable program statement.

**Self-stabilization in solar computing** : Is it possible to imagine the smart grid as a complex graph in solar computing system? Let us consider a unidirected graph $G = (V,E)$ to represent the system associated with the smart grid, each processor $p_i$ is represented by a vertex $v_i \in V$ and each communication link used for transferring data from $p_i$ to $p_j$ is an edge $(i, j) \in E$; opposite directed edge $(j, i) \in E$; the number of edges linked to a node is bounded by a constant. The distance between two processors $p$ and $q$ is $dist(p, q)$, the shortest path between $p$ and $q$ in the graph.
Overlay edge denotes a path of edges that connects two processors in the system. When the path is predefined and fixed, it acts as a virtual link where a processing time is required by intermediate processors to forward the data from source to destination. We regard the time it takes a message to traverse such an overlay link as the time for traversing a link that directly connects two neighboring processors. A configuration c of the system is a tuple $c = (S,L)$; S is a vector of states, $s_1, s_2, \dots, s_{ni}$, where the state $s_i$ is a state of processor $p_i$; L is a vector of link states. A processor changes its state according to its transition function. A transition of processor $p_i$ from a state $s_j$ to state $s_k$ is called a step and is denoted by a. A step consists of local computation and of either a single send or a single receive operation. An execution is a sequence of global configurations and steps, $E = \{c_0, a_0, c_1, a_1, \dots\}$, so that the configuration $c_i$ is reached from $c_{i-1}$ by a step $a_i$ of one processor $p_j$. The states changed in $c_i$, due to $a_i$, are the one of $p_j$ and possibly that of a link attached to $p_j$. The content of a link state is changed when $p_j$ sends or receives data during $a_i$. An execution *E* is *fair* if every processor executes a step infinitely often in *E* and each link respects the bounded capacity loss pattern. In the scope of self-stabilization, we should consider executions that are started in an arbitrary initial configuration. A *task* is defined by a set of executions called *legal executions* and denoted LE. A configuration *c* is a *safe configuration* for a system and a task LE if every fair execution that starts in *c* is in LE. A system is self-stabilizing for a task LE if every infinite execution reaches a safe configuration with relation to LE. The algorithm stabilizes if it has reached a safe configuration with regards to the legal execution of the corresponding task.

## Smart grid security analysis

**Unsymmetrical fault analysis** : The majority of system faults are not 3 phase symmetrical faults but the faults involving shunt types faults (e.g. Line-to-Line fault, double Line-to-Ground fault and series types faults (e.g. open conductor fault). Can a solar grid face such types of unsymmetrical faults for both single and three phases system? What are the countermeasures? Unsymmetrical system operation often results unsymmetrical faults Under unbalanced conditions, the system impedance in each phase may not be identical; three phase voltages and currents throughout the system may not be completely balanced i.e. may not have equal magnitude in each phase and are not displaced in time phase correctly. System operation and loads may be unbalanced. Unsymmetrical faults are generally analyzed through symmetrical components wherein three phase unbalanced voltages and currents may be unbalanced and transformed into three sets of balanced voltages and currents.

**Symmetrical fault analysis:** This is the analysis of abnormal behavior of a smart grid under the conditions of symmetric short circuit 3 phase faults due to various reasons such as insulation failure of power system equipments or flashover of lines due to lightning strike or accidental faulty operation. The smart grid must be protected against flow of heavy short circuit current by disconnecting faulty system through circuit breakers operated by protective relays. Symmetric fault is rare but the
analysis is crucial to protect a smart grid against the flow of most severe fault current. Can a solar grid face such type of symmetrical faults? What are the countermeasures?

**Smart grid stability analysis** : The stability of a smart grid is the ability of an interconnected power system to return to normal or stable operation after some form of disturbance due to loss of synchronization or falling out of step. Stability analysis is an essential part of smart grid planning, coordination and integration among various components of power system. The stability of a smart grid may be classified into steady state, dynamic and transient; is it valid for a solar grid? The study of steady state stability is the determination of upper limit of machine loading before losing synchronism provided the loading is increased gradually. The steady state stability limit of a smart grid is the maximum power that can be transmitted to the receiving end without loss of synchronism. It is crucial to know the steady state limit to improve the stability limit of the grid. Dynamic stability is more probable than steady state instability. Small disturbances occur continuously in a smart grid. The grid is dynamically stable if the oscillations do not acquire more than certain amplitude and die out quickly? Transient stability is the impact of sudden large disturbance of power system on various components of smart grid due to the occurrence of opening or closing power generation units, tripping of a loaded generation unit, abrupt dropping of a large load or fault on a heavily loaded line. It is also crucial to determine the effects of short circuit faults, the most severe type of disturbances on a solar grid. The stability of the solar grid may depend on various factors such as type and location of fault, rapidity and method of fault clearing (e.g. sequential or simultaneous opening of circuit breakers or isolators). A practical approach to transient stability analysis is to list all important severe disturbances along with their possible locations in the grid. The grid may be stable on occurrence of a fault or may be stable if the fault is cleared in time. A smart grid may be associated with multiple generation units and load. Computer simulation is an effective method of stability analysis of complex multi-machine system associated with a solar grid. A digital computer programme can compute the system parameters due to transients for effective system control. Another critical issue is to improve the transient stability of solar grid through various ways such as use of high speed reclosing circuit breakers, reduction in system transfer reactance, use of high speed excitation system and increasing system voltage.

**Voltage stability** is the ability of a smart solar grid to maintain acceptable voltages at all buses in the system under normal conditions and after being subjected to disturbance. The grid may lose stability when a disturbance results a progressive and uncontrollable decline in voltage. Inadequate reactive power support from generation units and transmission lines leads to voltage instability, voltage collapse and may result blackout. Voltage stability may be ensured by raising the terminal voltage of generation unit, tap value of generation transformer, Q-injection, load end OLTC and strategic load shedding. It is essential to ensure voltage security in terms of the ability of a system, not only to operate stably but also to remain stable following any reasonably credible contingency or adverse system change such as increase in load. It is essential to perform voltage stability analysis of a smart solar
grid though the study of load flow based static and system dynamics analysis in
terms of various parameters such as small and long disturbance voltage stability,
stable voltage, voltage collapse, transient voltage and voltage security.

*Security analysis:* Security constrained optimization is essential to ensure secure and economical operation of a smart grid. The grid may face emergency condition based on the severity of violations of operating limits. The grid must withstand the effects of contingencies to avoid heavy overloading of system components, cascading failure and system blackout. If the process of cascading failure continues, the grid as a whole or its major parts may completely collapse. System security analysis is carried out in computerized energy control centres in terms of system monitoring, contingency analysis and corrective action analysis. The basic objective of system monitoring is to give the best estimate of current system conditions or states of the smart grid through SCADA (Supervisory Control & Data Acquisition System). The basic objective of contingency analysis is to carry out emergency identification and what-if simulation; prediction of possible system outages before the occurrence and give alert against potential overloads and / or voltage violations. Corrective action analysis provides preventive and post contingency control and permits the system administrators to change the operation of the power system if contingency analysis predicts a serious problem in occurrence of a certain outage.

Contingency analysis is normally performed in three distinct states : contingency definition, selection and evaluation. Contingency definition gives the list of contingencies to be processed where the probability of occurrence is high. Contingency selection ranks a set of contingencies as per the order of severity using direct and indirect method. Contingency evaluation is performed in decreasing order of severity and is continued upto the point where no post contingency violations are encountered. The security control is achieved through security constrained optimization program. It is necessary to ensure the reliability of the smart grid so that adequate amount of power is generated and distributed with enough redundancy so that the grid can withstand all major failure events. Another critical issue is sensitivity analysis to compute possible overloads based on generation shift factors and line outage distribution factors.

In this session, the panel have considered the technology of IIoT enabled Industrial Control System (ICS) and SCADA configured with computers, electrical, electronic and mechanical equipments; operated in automated or semi-automated operation mode and used in manufacturing and chemical plants, power plants, energy and utilities distribution, communication and transportation systems. ICS interacts with the physical environment and is associated with a complex information system. ICS integrates computing, networking, data, application and security schema, sensors and actuators. ICS may be threatened by cyber and other various types of malicious attacks; safety, reliability, consistency and fault tolerance are critical issues.
<figure><img src="image_93.png" /><figcaption>Figure 10.2 : ICS / SCADA Architecture</figcaption></figure>

In general, ICSs can be simple, medium and very complex systems which may include thousands of different components distributed over different zones and controlling real-time complex processes. Industrial Control Systems can be broadly segmented into three different zones such as Enterprise, Control and Field zone (as per IEC TS 62443-1-1 2009) [Figure 10.2]. The enterprise zone includes business networks and enterprise systems; the control zone includes various control elements; the field zone includes the devices (e.g. PLC) and networks used for automation. A plant may have different segments such as power generation, transmission, and distribution and metering. Next, let us focus on networking schema of ICS/ SCADA architecture. Typically, several wireless technologies can be used for ICS / SCADA such as Wireless Local Area Network (WLAN) and Bluetooth (as per open IEEE 802.15.1 standard; operates in 2.4 GHz ISM band). Various components of an ICS / SCADA can communicate with each other through a set of protocols such as IEEE 802.11x, Bluetooth, cellular, Wireless HART, ISA 100.11a, Z-Wave, Zigbee, Microwave and satellite technologies.

Let us discuss IoT enabled SCDA & ICS. Due to recent innovation of internet connectivity, digital and smart manufacturing technologies, IoT has become a revolutionary technology. The applied domains include supply chain management, transport, utilities, industrial automation, healthcare, building and home automation. IoT is an emerging technology that connects physical objects, internet and communicate with one another (similar to HCI). IoT connects systems, sensors and actuators to the internet. Physical objects + Sensors, actuators and controllers + Internet = IoT (Connect, communicate and data exchange). Another emerging trend of SCADA & ICS is IIoT; this includes real-time monitoring and process control like real-time optimization of production and supply chain networks in manufacturing industry; deployment of smart machines, sensors and controllers with proprietary communication and internet technologies, automated process
control using digital controllers to achieve enhanced productivity and safe
distribution system; maximizing safety, sensitivity, security and reliability through
high precision automation and control. What is the scope of IoT for electric power
system? It includes smart metering, AMI (Advanced Metering Infrastructure),
connected public lighting, smart inverters, plant, SCADA, remote control operation
of energy consuming devices, home entertainment (audio, video, projectors), smart
lighting adapting ambient conditions based switching, wireless internet connected
lights, smoke and gas detection, smart appliances, management and control,
intrusion detection and surveillance system.

### SMQC Algorithm

Agents: Client (C), Database Administrator (A); /* C and A may be automated agents. */

System : Private database (SDB);

Input : Query (q);

Output: Response (r);

Procedure:

1. C challenges A → sends query (q).

2. A retrieves response (r) from SDB.

3. A selects correct move to preserve privacy of r.

⊕ Suppress r partially.

⊕ Perturb r by randomization.

⊕ Achieve k-anonymity through generalization / suppression / de-identification of r.

⊕ Summarize or aggregate r.

⊕ Replace r with a small sample.

⊕ Partition r.

⊕ Swap r.

⊕ Audit r.

4. A applies quantum signcryption algorithm on r and sends signcrypted r to C.

5. C unsigncrypts r w.r.t. q → verifies correctness and data integrity of r.

+

**Theorem :** *SMQC algorithm preserves the privacy of data through a set of information disclosure control method and signcryption of critical data.*

Quantum computing is a promising technology to solve computation problems significantly faster as compared to classical computers. This technology is based on the concepts of quantum mechanical phenomena such as superposition and entanglement to process data efficiently. Quantum theory analyzes the nature of energy and matter on the atomic and subatomic level. Quantum computing is a branch of quantum information science (e.g. quantum cryptography, quantum communication). In a classical computer, Boolean logic is represented through a set of bits where each bit is either 1 or 0. Quantum computers are not limited to two states and encode information as quantum bits. Qubits are the basic building blocks of quantum computing. Qubits can be in a 1 or
0 quantum state; can also be in a superposition of 1 and 0 states. When qubits are measured, the result is always either 0 or 1; the probabilities of the two outcomes depend on the quantum state of the qubits. Qubits represent atoms, ions, photons or electrons that work together to act as computer memory and a processor. It is rational and an interesting research agenda to understand the consequences of quantum technologies in the context of secure multi-party computation. Is it possible to construct secure multiparty computation protocols against quantum attacks? How to preserve privacy in secure multi-party quantum computation by optimizing quantum computational power, space and time complexity. Is it possible that dishonest parties gain no information about the private inputs of honest agents in secure quantum computing setting? Is it possible to ensure fairness and correctness in secure multi-party quantum computing : if the parties do not abort the protocol, then at the end of the protocol they share a state corresponding to the correct computation applied to the inputs of honest parties and some choice of inputs for the dishonest parties ?

Distributed computing considers the scenario where a number of distinct, yet connected computing agents wish to execute a joint computation. The objective of secure multi-party computation is to enable these agents to carry out such distributed computing tasks in a secure manner. The advancement of computer network technologies, multiagent system and cryptography has improved the efficiency of secure multi-party computation significantly. Let us define *Secure Multi-party Quantum Computation*. Two or more agents want to conduct a computation based on their private inputs but neither of them wants to share its proprietary data set to other. The objective of secure multiparty computation is to compute with each party's private input such that in the end only the output is known and the private inputs are not disclosed except those which can be logically or mathematically derived from the output. In case of secure multi-party computation, a single building block may not be sufficient to do a task; a series of steps should be executed to solve the given problem. Such a well-defined series of steps is called SMC protocol. It has some useful properties - privacy, correctness, independence of inputs, guaranteed output delivery and fairness. A protocol ensures correctness if each party receives correct output. Corrupted (or malicious) parties select their inputs independently of the inputs of honest parties and honest parties must receive their output. Corrupted parties should receive their outputs if and only if the honest parties receive their outputs and this ensures fairness of the protocol.

In the study of SMC problems, two models are commonly assumed : semi-honest and malicious model. A semi-honest party follows the protocol properly with correct input. But after the execution of the protocol, it is free to use all its intermediate computations to compromise privacy. A malicious party does not need to follow the protocol properly with correct input; it can enter the protocol with an incorrect input. A third party may exist in a protocol. A trusted third party is given all data; it performs the computation and delivers the result. In some SMC protocols, an untrusted third party is used to improve efficiency.

A protocol preserves privacy if no agent learns anything more than its output; the only information that should be disclosed about other agent's inputs is what can be derived from the output itself. Secure multi-party computation preserves privacy of
data in different ways; is it possible to apply following methods for secure multi-party quantum computing model? What are the algorithms? is the cost of computation and communication high in case of quantum computing? It is rational to apply following methods on the original data first and then apply quantum cryptographic algorithms (e.g. signcryption, encryption) on the perturbed data.

* *Add random noise to data* : The basic objective of data perturbation is to alter the data so that real individual data values cannot be recovered. For an input x, (x+r) preserves the privacy of x if r is a secret random number.

* *Split* a data or message into multiple parts randomly and sending each part to a DMA through a number of parties hiding the identity of the source,

* *Control the sequence of passing* selected data or messages from an agent to others through serial or parallel mode of communication.

* *Dynamically modify the sequence of events* and agents through random selection.

* *Permute* the sequence of messages randomly.

* *Masking*

In SMQC algorithm as stated above, the client (C) interacts with the system administrator (A) through enterprise applications or web; submits simple or complex queries and searches for intelligent information. A malicious agent may be able to attack the server during this communication between C and A. SMQC tries to protect sensitive data from unsolicited or unsanctioned disclosure of data by calling different information disclosure control methods such as suppression, perturbation, randomization, k-anonymity through generalization or de-identification, summarization or aggregation, data partition and data swap. The privacy of sensitive data may be preserved by suppressing the data intelligently before any disclosure or computation. Specific attributes of particular records may be suppressed completely. In case of partial suppression, an exact attribute value is replaced with a less informative value by rounding or using intervals. K-anonymity is achieved through generalization, suppression and de-identification. The attribute values are generalized to a range to reduce the granularity of representation. Quasi-identifier attributes are completely or partially suppressed. De-identification is achieved by suppressing the identity linked to a specific record or altering the dataset to limit identity linkage. Summarization releases the data in the form of a summary that allows approximate evaluation of certain classes of aggregate queries while hiding individual records. The sensitive data set may be replaced with a small sample. Aggregation presents data in the form of sum, average or count. Randomization perturbs the data randomly before sending them to the server and introduces some noise. The noise can be introduced by adding or multiplying random values to numerical attributes. The system administrator generally preserves the privacy of sensitive data through signcryption. C checks whether different statistical disclosure control tools are really able to preserve the privacy of sensitive data from the adversaries during communication.

It may be possible to restrict the query of the client through various ways such as query set size control, query set overlap control, auditing, cell suppression, data swap and data partitioning. The query set size control method permits information
w.r.t. a query be released only if the size of the query set satisfies specific condition. Auditing keeps up-to-date logs of all queries made by a client and checks constantly possible compromise whenever a new query is issued. Auditing provides the client with unperturbed response if the response does not compromise privacy of critical information. Bit, audit may require high cost of data processing time and storage requirements. The basic objective of partitioning is to cluster individual entities of the population in a number of mutually exclusive subsets or atomic populations. The basic objective of suppression is to restrict the disclosure of confidential information by suppressing a specific set of cells of a data schema. Data perturbation may have probability distribution and fixed data perturbation strategy. The first method extracts a sample from the population with a probability distribution. In the second category, the values of specific attributes are perturbed once and for all. Another interesting method is approximate data swap of multicategorical attributes of data schema.

Privacy is the primary concern of secure multi-party quantum computing; is it possible to address the issue utilizing the concept of cryptography and secure multiparty computation? The fundamental objectives of cryptography are to provide confidentiality, data integrity, authentication and non-repudiation. Cryptography ensures privacy and secrecy of information through encryption methods. The sender (S) encrypts a message (m) with encryption key and sends the cipher text (c) to the receiver (R). R turns c back into m by decryption using secret decryption key. In this case, an adversary may get c but cannot derive any information. R should be able to check whether m is modified during transmission. R should be able to verify the origin of m. S should not be able to deny the communication of m. There are two types of key based algorithms - symmetric and public key. Symmetric key encryption scheme provides secure communication for a pair of communication partners; the sender and the receiver agree on a key k which should be kept secret. In most cases, the encryption and decryption key are same. In case of asymmetric or public-key algorithms, the key used for encryption (public key) is different from the key used for decryption (private key). The decryption key cannot be calculated from the encryption key at least in any reasonable amount of time. The widely-used public-key cryptosystem is RSA cryptosystem (1978), Elgamal's cryptosystem (1985) and Paillier's cryptosystem (1999). The challenge is how to apply existing encryption and decryption algorithms in quantum computing: is it possible to perform quantitative operations of quantum computing on encrypted data? How?

Is it possible to apply *digital signature* for secure multi-party quantum computing model? A digital signature is a cryptographic primitive by which a sender (S) can electronically sign a message and the receiver (R) can verify the signature electronically. S informs his public key to R and owns a private key. S signs a message with his private key. R uses the public key of S to prove that the message is signed by S. The digital signature can verify the authenticity of S as the sender of the message. A digital signature needs a public key system. A cryptosystem uses the private and public key of R. But, a digital signature uses the private and public key of S. A digital signature scheme consists of various attributes such as a plaintext message space, a signature space, a signing key space, an efficient key generation
algorithm, an efficient signing algorithm and an efficient verification algorithm. There are various forms of digital signature such as group signature and ring signature. A group signature scheme allows a member of a group to sign a message anonymously on behalf of the group. A designated entity can reveal the identity of the signer in case of any dispute. The challenge is how to apply existing digital signature algorithms in quantum computing : is it possible to perform quantitative operations of quantum computing on digitally signed data? How?

Is it possible to apply signcryption for secure multi-party quantum computing model? Traditional signature-then-encryption is a two step approach. At the sending end, the sender signs the message using a digital signature and then encrypts the message. The receiver decrypts the cipher text and verifies the signature. The cost for delivering a message is the sum of the cost of digital signature and the cost of encryption. Signcryption is a public key primitive that fulfills the functions of digital signature and public key encryption in a logically single step and the cost of delivering a signcrypted message is significantly less than the cost of signature-then-encryption approach. A broadcasting system is vulnerable to insecure communication. The basic objective is that the system properly signcrypts all sensitive data. A pair of polynomial time algorithms (S,U) are involved in signcryption scheme where S is called signcryption algorithm and U is unsigncryption algorithm. The algorithm S signcrypts a message m and outputs a signcrypted text c. The algorithm U unsigncrypts c and recovers the message unambiguously. (S,U) fulfill simultaneously the properties of a secure encryption scheme and a digital signature scheme in terms of confidentiality, unforgeability and nonrepudiation. Signcryption can ensure efficient secure communication. The basic objective is to provide confidentiality, data integrity, authentication and non-repudiation in the communication of sensitive data. The challenge is how to apply existing signcryption and unsigncryption algorithms in quantum computing : is it possible to perform quantitative operations of quantum computing on signcrypted data? How?

System Analytics : Secure Adaptive Filter Mechanism (SAFM)

Agents: System analysts;

• Scope :

- Spam filtering

- Web caching - DoS attack prevention in cache miss

- Distributed systems, networking applications, database applications, data streaming algorithms

o Constraints: space and time complexity;

• System: Adaptive filter in adversarial environment;

- Adaptive Filter (Type 1) /* Private search mechanism */

* Input: A secure adaptive filter represents a set S of elements approximately.

* Lookup: Given an element x, the buckets given by h_i(x) are examined to see if x is in the list i.e. data schema.

* Insertion: Given an element, first check that x is not already in the list via a lookup. If not, sequentially
examine the buckets $h_i(x)$. If there is empty space, $x$ is inserted into the first available space. If no space is found, a value $j$ is chosen independently and uniformly at random from $[1; d]$, and $x$ is stored in the $j$th sublist at bucket $h_j(x)$. It displaces an element $y$ from that bucket and then insertion is recursively executed for $y$.

* Deletion: Given an element $x$, $x$ is searched via a lookup; if it is found it is removed; selectively remove false positive without introducing false negative.

* Output: for any $x \in S$, the filter always answers ‘Yes’ and for any $x \notin S$ it answers ‘Yes’ only with small probability;

* **Hybrid Adaptive Filter (Type 2) /* Private search and classification */**

* Input: a set $S$ of elements approximately.

* Lookup: Given an element $x$, the buckets given by $h_i(x)$ are examined to see if $x$ is in the list.

* Insertion /* same as type 1 filter */

* Deletion /* same as type 2 filter */

* Call private *optimal margin classifier*. / * refer section 3.1*/

* Output:

* for any $x \in S$, the filter should always answer ‘Yes’;

* determines class of $x \in C_i$; /* $C_i$: $i^{th}$ class */

* for any $x \notin S$ it should answer ‘Yes’ only with small probability;

* **Structure**: Select right structure based on objective of filter - *Adaptive Bloom filter / Adaptive Cuckoo filter / Parallel pipeline*

* **Security**: call intelligent threat analytics, verify security intelligence at multiple levels.

* **Level 1**: audit computational intelligence in terms of correctness of computation and rationality of filter configuration,

* **Level 2**: verify system performance of the adaptive filter in terms of reliability, consistency, resiliency and liveness;

* **Level 3**: malicious attacks – verify the risks of Denial of Service (DoS), false data injection, intrusion and Sybil attack on adaptive filter;

* **Level 4**: multi-party corruption - assess the risks of corruption of system administrator and filtering mechanism of adaptive filter;

* **Level 5**: verify the efficiency of access control of adaptive filter in terms of authentication, authorization, correct identification, privacy, audit, nonrepudiation, confidentiality and data integrity.

* **Revelation principle**: Preserve privacy of output of adaptive filter.
* *Strategy / Moves:* Adaptive security, private search, Cuckoo hashing;

* *Staff-resources:* Creative talent for K-A-B-C-D-E-T-F innovation model on
development of secure adaptive filter;

* *Skill-style-support:* Data Structure, Optimal margin classifier, Secure Multi-party Computation;

**Analysis of SAFM:** An adversary may pick the inputs but gets no information about the randomness of data structure. An adversarial model allows an adversary to choose inputs and queries adaptively according to previous response. With high probability over internal randomness of data structure, the output will be correct if an adversary chooses a sequence of inputs adaptively. A secure adaptive filter (e.g. Bloom filter) exists in an adversarial model if one way function exists. Bloom filters use significantly less memory and have very fast query time. These filters are used in distributed systems, networking and database applications, spam filtering, web caching, data straming algorithms and information security.

**U:** Large universe; **S:** A set of elements from U; **x:** input query to be answered by a
secure adaptive filter; **For any Output:** for any $x \in S$, the filter should always
answer 'Yes'; For any $x \notin S$ it should answer 'Yes' only with small probability.
What happens if an adversary chooses the next query according to the responses of
previous query? Does the bound on the error probability still hold? Under the
adversarial model, secure adaptive filters are resilient to spam filtering and web
caching attacks by efficient and computationally bounded adversaries.

The security of an adversarial resilient filter can be defined through a security game with an adversary. An adversary is allowed to make a sequence of t adaptive queries to the adaptive filter and gets their responses. The adversary has only oracle access to the filter and can not see the internal memory representation. The adversary must output an element $x*$ as a false positive. A filter (n,t,e) is adversarial resilient if when initialized over sets of size n, then after t queries, the probability of $x*$ being a false positive is at most e. If a filter is resilient for any polynomiallymany queries, it is strongly resilient.

An adaptive filter consists of two algorithms (i) an initialization algorithm that gets a set and outputs a compressed representation of the set. (ii) a membership query algorithm that gets a representation and an input. An adaptive secure filter has a randomized algorithm but a deterministic query algorithm

Security game :

Notation : U- universe of elements; size of U : *u*;

S : Subset of U, S∈U, size of S :n; n≥ log log u;

An adaptive filter is a data structure that is composed of two algorithms, B= (B₁,B₂);

B₁ : Randomized set up algorithm; gets as input a set S and outputs a compressed representation of it B₁(S) =M.

B₂ :Query algorithm; it answers membership queries to S given compressed representation M. It is deterministic and can not change the representation. B₂ is a deterministic algorithm that gets as input representation and a query element x ∈U.

B is (n.t.e) adaptive filter with a steady representation for any set S∈U of size n if it
holds.