Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
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Copyright ⓒ 2019 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2019 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
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Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
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Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2019 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

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Editor Guide
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Copyright ⓒ 2019 GiRI (Global IT Research Institute)Evalution Procedure

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Copyright ⓒ 2019 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2019 GiRI (Global IT Research Institute)Journal Submission Guide
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

Volume. 8 Issue. 1

5J

1 A Triage Triangle Strategy for Law Enforcement to Reduce Digital Forensic Backlogs

1173

Da-Yu Kao*, Ni-Chen Wu*, Fuching Tsai***Department of Information Management, Central Police University, Taiwan**Department of Criminal Investigation, Central Police University, Taiwan

2 ICT Enabling Technologies for Smart Cities

1180

Dzung Van DINH*, Byeong-Nam YOON**, Hung Ngoc LE***, Uy Quoc NGUYEN****, Khoa Dang PHAN*, Lam Dinh
PHAM*
*Information Technology Institute, Vietnam National University (VNU), Vietnam**Global IT Research Institute (GIRI), Korea***VKX Ltd. (Viet Nam Korea Exchange Joint Venture Company), Vietnam****PTIT (Posts and Telecommunications Institute of Technology), Vietnam

Copyright ⓒ 2019 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20191173

A Triage Triangle Strategy for Law Enforcement
to Reduce Digital Forensic Backlogs
Da-Yu Kao*, Ni-Chen Wu*, Fuching Tsai***

Department of Information Management, Central Police University, Taiwan**
Department of Criminal Investigation, Central Police University, Taiwan
dayukao@gmail.com, niniwu841223@gmail.com, fctsai@mail.cpu.edu.tw
Abstract—The explosive growth of computer technologies
creates many electronic data and produces much digital evidenceof people’s lives. As technology has improved, the volume of datafor cybercrime investigation keeps growing at unprecedented
rates and creating a quandary for Law Enforcement Agencies(LEAs). This study discusses the rise of digital evidence and thetriage needs in digital forensic processing. It requires the sincere
examination of all available data volumes at the scene or in the lab
to present digital evidence in a court of law. In order to maintainthe relevance, reliability, and sufficiency of digital evidence,
investigators must establish a process model that can provide aquick response at the scene. This study proposes the novel triage
triangle strategy of digital forensic components and illustrates
TEAR phases from the viewpoint of THOR dimensions to describe
the proper practices for identifying, collecting, acquiring, andpreserving the digital data. It facilitates the efficiency andeffectiveness of reducing digital forensic backlogs for LEAs.
Keyword—Digital Evidence, Digital Triage Forensics, THOR
dimensions, Data Network, Law Enforcement

I. INTRODUCTION
The digital forensic process commences with any piece ofdigital media. Every action taken has to adhere to the legitimacy
rules so that the obtained digital evidence could be presented in
the court. When investigators evaluate evidence, its relevance,
reliability, and sufficiency are of grave importance both in theinvestigative and probative stages of a case. Digital forensics———————————————————————
Manuscript received Dec. 27, 2017. This work was a follow-up of theinvited journal to the accepted & presented paper of the 21st Conference on
Advanced Communication Technology (ICACT2019), and this research waspartially sponsored by the Executive Yuan of the Republic of China under the
Grants Forward-looking Infrastructure Development Program (Digital
Infrastructure-Information Security Project-109) and the Ministry of Scienceand Technology of the Republic of China under the Grants MOST
107-2221-E-015-002..
Da-Yu Kao is with the Department of Information Management, Central
Police University, Taoyuan 333, Taiwan (phone: +886-3-328-2321; fax:
+886-3-328-5189; e-mail: dayukao@gmail.com).
Ni-Chen Wu is with the Department of Information Management, Central
Police University, Taoyuan 333, Taiwan (phone: +886-3-328-2321; fax:
+886-3-328-5189; e-mail: dorislovesnoopy@gmail.com).
Fuching Tsai is with the Department of Criminal Investigation, Central
Police University, Taoyuan 333, Taiwan (Corresponding Author phone:+886-3-328-2321; fax: +886-3-328-5189; e-mail: fctsai@mail.cpu.edu.tw).

addresses the collection, examination, analysis, andpresentation of evidence located on computers, cell phones, or
networks. It brings the evidentiary fruits of electronic data formany types of decisions, such as divorce, child custody,
contract resolution, or criminal judgments [9]. The digitalforensic process is very time-consuming because it requires theexamination of available data volumes from the scene.
Cybercrime is a growing problem year after year. The abilityof Law Enforcement Agencies (LEAs) to investigate andprosecute criminals for these crimes still leaves much to be
desired on the process, tools, or training. There is a lack ofawareness of available training courses or educational materials
on cybercrime investigation and digital forensics, especially for
LEAs [5]. This study provided a digital evidence assessment ininvestigating crimes. Conducting digital forensics on theoriginal evidence sources should be avoided if possible since
the examination on forensic copies or images maintains the data
integrity of digital evidence. However, evidence triage provides
valuable quick intelligence without subjecting digital evidence
to a full examination and determines if a media is worth to be
examined under significant time constraints [11]. This quick
intelligence can be used at the scene to guide the search andseizure, and in the lab to determine if a media is worth to be
examined. With the development of information technology,
LEAs need to improve the speed and quality of cybercrime
investigations. Performing forensic triage can be required toquickly and efficiently review several target systems. This study
tries to propose a practical strategy to mitigate the enormous
backlog of cases for law enforcement.
The structure of this study is organized as follows. Section 2
provides a review of digital evidence and forensic
investigations in LEAs. The proposed triage triangle strategy ofdigital forensic components described in Section 3. Section 4
demonstrates the discussions and analyses. Finally, the lastsection concludes the study.
II. REVIEWS
A. Digital Evidence
Most of our daily activities and interactions are recorded inelectronic devices. Digital devices are everywhere and help
people communicate locally and globally. Computers, cell

Copyright ⓒ 2019 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

phones, and the Internet are valuable sources for digital
evidence. Digital data is always stored in various locations
across a system or physical location. Any piece of information
technology can be used criminally. Digital evidence is defined
as information and data of value to an investigation that is stored
on, received, or transmitted by an electronic device [3]. Digital
evidence can help answer any questions in an investigation
ranging from the whereabouts of a victim at a given time to thestate of mind of the criminal. Answering these questions
requires some degree of crime reconstruction, including a
combination of temporal, functional, and relational analysis ofavailable evidence [3]. This evidence can be acquired whenelectronic devices are seized and secured for examination.
When investigators attempt to retrieve digital data, criticalevidence in cybercrime investigations must be extracted fromthat digital domain. Digital evidence is [2]:1) Fragile: It can be altered, damaged, or destroyed withlittle effort.
2) International: It can cross jurisdictional borders
instantly and effortlessly.
3) Latent: It is hidden like fingerprints or DNA evidence.
4) Volatile: It is volatile and sensitive.
B. Forensic Investigations in LEAs
Cybercrimes often raise new challenges for the LEAs.
Criminals were keeping up with the new technology while law
enforcement lagged. The knowledge, skills, and abilities ofinvestigators in the field of digital evidence are a necessary stepin successfully pursuing cybercriminals. Investigators must1174

have the capability to analyze any evidence retrieved ininvestigating cybercrimes [5]. Crime scene investigation in
LEAs includes case analysis, report writing, and legal
presentation [12].
1) Case Analysis
Different experts should independently report the caseanalysis.
2) Report Writing
Different reports from various experts involved in a case arecalled to give evidence explanations at the courtroom since thereports only have validity after being confirmed in court.
3) Legal Presentation
Legal presentation in court is to give evidence explaining therelevance and the implications of their actions. Evidence is
specific to his/her specialty so that forensic experts can becross-examined and present their cases objectively without fear,
uncertainty, or doubt.
III.

THE PROPOSED TRIAGE TRIANGLE STRATEGY OF
DIGITAL FORENSIC COMPONENTS

Cyberspace introduces a wide range of new vulnerabilities
criminals can use to take advantage of their victims. As newcrimes emerge from the misuse of cyberspace, LEAs mustchange and develop their strategies to address previously
unknown issues [8]. The proposed triage triangle strategy ofdigital forensic components is divided into three categories (Fig.
1): principles, forensics, and governance. This strategy helps
LEAs deal with reducing digital forensic backlogs.

Fig. 1. Triangle strategy of digital forensics process

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

A. Principles
Digital forensics is a branch of forensic science toencompass the investigation in electronic devices. It focuses on
the recovery and analysis of raw data in digital devices.
Investigators face numerous challenges, technical difficulties,
or legal issues surrounding cybercrime investigations. It isessential not to overlook or minimize the vast amount of
information stored in a computer, the nature of volatile memory,and a lack of sufficient strategies. The required principles for
digital evidence handling should be satisfied as follows [6]:1) Sufficiency
Investigators should be able to decide how much and whichmaterial to collect or acquire at the scene. They need to exhaust
different methods, tools, and practices to identify, extract, andconvert data to unequivocal evidence. There must be sufficientevidence to make the investigation convincing. They shouldhave taken into consideration that enough material has beengathered to prove somethings.
2) RelevanceThe data value can assist the investigation of the particularincident. Investigators should be able to describe the followed
procedures form auditing records and explain how the decision
to collect each data was made. The relevance of pertinent data
affects the weight and usefulness of the evidence. Time, efforts,
and cost spent in an investigation could be well controlled if
investigators know what should be collected during theinvestigation. Then they can demonstrate that the acquiredmaterial is relevant to the investigation.
3) Reliability
Data extracting is not merely copying of data. All processes
used in handling potential digital evidence should be auditable.
Chain of custody should be preserved during collecting,examining, analyzing, preserving, and transporting of data. If
the evidence cannot be repudiated and rebutted, then the digital
evidence would be reliable and admissible in a court of law.
B. Forensics
Digital forensics deals with how data is gathered,investigated, recovered, found, analyzed, and stored inelectronic devices in a court of law. Trace evidence in thecomputer environment falls within the realm of digital forensics.
When people interact with people, places, and objects in thedigital realm, these virtual traces will be kept as deleted filefragments, activity logs, document metadata, or email headers.
They may be deemed to be of evidence value in identifying theorigins of the parties involved in a criminal case. In Fig. 1, there
are three forensic elements in our proposed triage triangle
strategy. The detailed description is demonstrated in thefollowing:
1) Triage
Most digital evidence investigation is completed at the scene
promptly. Increasing cases and computers need to be analyzed
effectively and efficiently. Specific investigations with a highlevel of technical difficulty are likely to be conducted in the lab[5]. As the data loads increase exponentially, triage has become
an increasingly important part of digital forensics. Triage aims

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to identify the most relevant data as quickly as possible. It helps
investigate in a limited time. Some digital forensic tools meetthis need by extracting the recently changed files from acomputer.
2) At the Scene
Investigators to any criminal complaint must ask the pressing
questions at the very beginning and handle any relevant digital
evidence appropriately at the scene. That will directly impact
the effectiveness of an investigation and the admissibility of that
evidence in court [5]. When first responders arrive at the scene,
they will look for all relevant evidence, explore some questions,and find the follow-up answers on whom, which, what, when,
where, and how. Everything can be evidential to support orrefute something.
3) In the Lab
Due to the limitations of technology or knowledge, someevidence cannot be identified at the scene and must be broughtback to the laboratory for further examination.
C. Governance
There is always a continuous need for investigators to utilize
tools in the effective handling of digital evidence and ensure atimely, valid, and accurate presentation to a court. Theseabilities of investigators may affect the tools used and theunderstanding of any evidence retrieved to ensure that theevidence is admissible in court [5]. This study proposes aminimum requirement for compliance with digital forensic
processes.
1) People: Investigators
The dependence on using digital forensic tools or techniquesbrings about the detection and recovery of hidden data in LEAs.
The trend of digital forensics implies the imperative need forgovernance on digital forensics. Investigators should
persistently upgrade their skills, tools, and know-how to keep
pace with changing technology. They should know how to
extract the volatile evidence, use appropriate tools, and perform
live investigative response [7]. It is no longer able to unplug a
computer and expand the backlog as a mountain in the lab.
2) Process: THOR Dimensions
Investigators need proper processes to identify, collect,acquire, and preserve digital data. The goal of the CAMINO(Comprehensive Approach to cyber roadMap coordINation anddevelOpment) project was to develop a comprehensive
cybercrime and cyber terrorism research agenda. THOR
dimensions are the foundation of the CAMINO roadmap andaddress the following aspects [4]:a) Technical-related to technology, technological approaches,and solutions.
b) Human-related to human factors, behavioral aspects, privacy
issues, as well as raising awareness and knowledge ofsociety.
c) Organizational-related to processes, procedures, andpolicies within organizations, as well as cooperation
between organizations.
d) Regulatory-related to law provisioning, standardization, andforensics.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20193) Technology: Digital Forensics Tools
The ability to use appropriate digital forensic techniques andtools varies by agency and geographic location. These
techniques and tools are not sufficient to keep pace withchanges in digital devices. Proprietary sources for the softwareor hardware generally limit the particular discipline ofpurchased training courses in the digital forensics process. The
need for freeware or open-source tools within digital forensics
across all LEAs is critical for the nature of the discipline in legal
proceedings [8]. The range of digital forensics includes
computer devices, network servers, and mobile handsets. The
following of digital forensic tools is designed for different typesof examined targets.
a) Memory Forensics: Volatility, WindowsSCOPE, RAM
Capturer, Magnet RAM Capture, and Memoryze.
b) Mobile Device Forensics: CelleBrite UFED, XRY, andOxygen Forensic Suite.
c) Network Forensics: Wireshark, Network Miner, Xplico, andE-Detective.
d) Computer Forensics: EnCASE, FTK, Sleuth Kit Autopsy,
TCT, and DEFT.

IV.

DISCUSSIONS AND ANALYSES

Computers now hold more information and more processing
power than before. That brings a great challenge for LEAs todeal with increasingly large amounts of digital evidence.

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Investigators must have the capability to process large amounts
of information accurately, completely, and promptly [8]. This
practical method of reducing digital forensic backlogs becomes
critical for LEAs.
A. Reducing Digital Forensic Backlogs: Triage
As the prevalence of digital evidence in criminal, civil, or
administrative cases becomes popular, backlogs in forensic labs
continue to proliferate [6]. Triage originally means that when
medical resources are insufficient to deal with all injuries, they
are classified, sorted, or selected to determine the priority orderof emergency treatment. Then the injured can be treatedefficiently and based on the injury situation of the patients todetermine the priority treatment process [10]. First responders
need to set up a series of digital triage processes to classify thevarious cases or scenes and to determine whether some devices
need further examinations in the lab. The purpose of digital
triage is to prioritize digital media and obtain quick intelligence.
It is a solution to the problem of case backlogs [11]. Digital
triage is the investigative beginning of the forensic examination.
Fig. 2 demonstrates two types of digital triage: live and dead. A
triage assessment does not replace the forensic analysis. Someinitial tasks can be performed at the scene by non-digital
evidence specialists to increase the efficiency of aninvestigation promptly and to decrease the examination backlog
in the lab.

Fig. 2. The triage evidence process of TEAR phases in reducing digital forensic backlogs

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20191) Live Triage: Volatile/Logical Data
The primary purpose of live triage is a rapid extraction ofquick intelligence from online sources. Live triage has thepotential to identify evidential data quickly. First responders
need to find the following volatile and logic data from thepower-on device.
a) Volatile Evidence: memory, network connections, running
process, and open files.
b) Logical Data: operating system setting, network status,
execution information, and system log records.

2) Dead Triage: Physical Image Files
Dead triage is conducted in the lab for the possible existenceof the relevant evidence. Some types of tools are [10]:a) Acquiring a copy of the image file.
b) Indexing, searching, cracking, and analyzing digital content
in digital media.
c)Presenting a forensic report

3) Scene Investigation
Scene investigation focuses on the compelling evidence ofthe power-on status to detect and explore any possible answers
to scenarios. At the scene, an active investigation of the primary
memory content can be conducted to collect the volatile

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evidence under the power-on state quickly. The scene must be
photographed or recorded appropriately. Turning off computer
power is not the first thing to do at the scene since immediately
turning off the power of the computer will inevitably result inthe loss of programs and data running in memory. Sometimes, itwill destroy valuable evidence.
4) Lab Forensics
Lab forensics focuses on identifying results in the static
evidence of shutdown status. In the lab, dead forensics analysis
for digital storage media can be repeatedly verified for integrity.
However, this time-consuming processing can result inexcessive backlogs from a large number of new cases for LEAs.
B. TEAR Phases of THOR Dimensions in Digital Forensics
An additional concern was the inability to communicate withother investigators, cross into other jurisdictions, andcoordinate cybercrime investigations during investigations [5].
This proposed triage triangle strategy helps LEAs produce a
quality report [6]. Fig. 3 also illustrates TEAR phases from theviewpoint of THOR dimensions to describe the proper practices
for identifying, collecting, acquiring, and preserving the digital
data [12]. The two axes represent the perspectives andprocesses of digital forensic investigation.

Fig. 3. The TEAR phases of THOR dimensions in the proposed triage triangle strategy

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20191)

TriageFirst responders can use a triage tool to preview data at thescene.
a) Technical: Some tools are available to dump relevant

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investigators. Several tools for conducting cybercrime
investigations in TEAR phases are introduced below (Table I)[11]. Well-planned use of digital forensic tools can improve
efficient and effective investigations.

data.
b) Human: As technology changes, first responders must
continually update their devices and training. They assist at thescene.
c) Organizational: Evidence integrity refers to thepreservation of evidence in its original form. The inspection process
must be supervised or recorded.
d) Regulatory: The ISO/IEC 27037:2012 guidelines for
identification, collection, acquisition, and preservation in handlingpotential digital evidence are required in an investigation tomaintain the integrity of the digital evidence.

TABLE IAVAILABLE TOOLS IN TEAR PHASES
Functions
Tools
Volatility,
WindowsSCOPE,
RAM Capturer,
Identify artifacts in memory
Magnet RAM
Capture, andMemoryze

PhasesTriageExamine images
Examination

Examine information frombrowsers
Carve data files

2) Examination
Evidence specialists extract useful data from digital
devices, bring raw data to the lab, and maintain the chain ofcustody at the same time.
a) Technical: They use tools to extract useful informationfrom collected evidence.

Analyze information from aphone
Analysis
Parse the data of USB
devicesAcquire web pagesb) Human: They are responsible for documenting andpreparing evidence once it arrives at the scene.
c) Organizational: They should comply with the chain ofcustody, which includes the documentation of physical and digital
evidence.

Investigate the
network-related activity
Reconstruction
Recover data fromcomputer devicesd) Regulatory: The forensic examination aids theunderstanding of the evidence and assists the court to close the case.

3) Analysis
Lab analysts should determine the processes necessary tocomplete the analysis.
a) Technical: They should integrate the extracted data anddevelop an intelligence analysis platform.
b) Human: Forensics analysts who work in the lab combine
relevant events into exploring the fact.
c) Organizational: They should put data altogether to help
the court make the right decisions.
d) Regulatory: Investigators can use big data analytics todiagnose, describe, prescribe, or predict the case.

4) Reconstruction
Reconstruction refers to the systematic process of piecing
together evidence and information gathered during a crime togain a better understanding of what transpired between thevictim and the criminal [3]. It involves ordering the evidential
associations from temporal, relational, and functional analysis.
Crime reconstruction is the determination of the actions andevents for establishing the continuity of a crime. It canleverage a wide range of forensic methods to establish ahypothesis about the sequence of events and test whether the
hypothesis is correct or not. If the hypothesis is confirmed,
then one possible explanation can be identified. If it is refuted,
then the explanation is not possible, and other hypotheses will
have to be considered [1].
C. Proposed Tools in Digital Forensic Investigations
Many LEAs use tools to examine a target machine or
network [5]. Available tools can increase the speed of thedigital forensic process and reduce the number of devices in
the lab. That will reduce case backlog and improve the qualityof first responders, evidence specialists, lab analysts, or

FTK Imager
Dumpzilla
SIFT
CelleBrite UFED,
XRY, and Oxygen
Forensic Suite
USB Historian
FAW
Wireshark, Network
Miner, Xplico, andE-Detective
EnCASE, FTK,
Sleuth Kit Autopsy,
TCT, and DEFT

1) Triagea) Volatility: It can identify the running processes, network
activities, and open connections for volatile memory analysis of
Windows, Linux, Mac & Android systems.

b) WindowsSCOPE: It enables memory forensics for
Windows computers and provides the memory forensics analysis forsecurity breaches. It can identify the processes, threads, and drivers
running on the system.
c) RAM Capturer: It can dump the data from thecomputer’s volatile memory, which may contain encryptedvolume’s password and login credentials for web services.
d) Magnet RAM Capture: It can capture the physical memoryof a computer and analyze artifacts in memory.
e) Memoryze: It can create, acquire, and analyze an image
and identify all running process, drivers, or malicious activities inlive memory.

2) Examinationa) FTK Imager: It can create MD5 or SHA1 file hashes andexamine files, memory dumps, or images.
b) Dumpzilla: It can examine the information from browsers.
c) SIFT: SANS Investigative Forensic Toolkit (SIFT) can
carve data files, generate a timeline from system logs, and carry out
forensic analysis.

3) Analysisa) CelleBrite UFED: Cellebrite Universal Forensic
Extraction Device (UFED) can extract information and recoverdeleted data from mobile devices.
b) XRY: It can analyze and recover information in aforensically sound manner from mobile phones, smartphones, andtablet computers.
c) Oxygen Forensic Suite: It can identify app accounts,
communicated contacts, geolocation metadata, and other

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019
information from a mobile phone. It can further analyze photos,
documents, videos, and device database.

[3]d) USB Historian: It can parse the USB history data from thecomputer.

[4]e) FAW: Forensics Acquisition of Websites (FAW) can
acquire web pages and files online.
4) Reconstruction
Evidence on computers and networks should be included togain an understanding of a crime reconstruction [2].
a) Wireshark: It can capture the network-related
activity, analyze network protocols, and support Windows,
Linux, and Mac OS.
b) Network Miner: It can examine hostnames, sessions,open ports, and operating systems from network traffic.
c) Xplico: It can analyze email, HTTP contents, andVoIP call data from network traffic.
d) E-Detective: It can capture, decode, reassemble, andreconstruct various types of network traffic such as Email,
Webmail, Instant Messaging, File Transfer, and VOIP.

[5]

[6]

[7]

[8]

[9]

[10]

e) EnCase: It can collect Internet artifacts, acquire data, andproduce a report.

[11]f) FTK: Forensic Toolkit (FTK) can crack a password,
analyze emails, or look for specific characters in files.

[12]g) Sleuth Kit Autopsy: It consists of command-line tools. It
can analyze smartphones and hard disks.
h) TCT: The Coroner's Toolkit (TCT) can recover data fromcomputer disasters under several Unix-related systems.
i) DEFT: Digital Evidence and Forensics Toolkit (DEFT)
can gather, acquire, and preserve digital evidence on runningsystems.

V. CONCLUSION
Cybercrimes have put LEAs at a significant disadvantage.
There is still much work to ensure that LEAs have thenecessary resources or tools in investigating cybercrimes.
Digital forensics is a continuous procedure. Each phase has ahigh impact on the relevance, reliability, and sufficiency ofthe evidence. The increase of substantial digital evidence
backlogs is encountered in a criminal investigation by LEAs
throughout the world. The crime scene is often the driving
force behind a successful criminal investigation. Properly
collecting digital evidence is essential in cybercrime
investigation. The examination of digital sources should becompleted empirically, logically, and systematicallyconsistent with organizational policy. With the developmentof information technology, investigators need to get theevidence quickly and sufficiently. This study explores thisbacklog challenge in digital forensics from a technicalstandpoint, proposes a triage triangle strategy, and contributeto an efficient digital forensic process. It is also critical toquickly assist the court in finding the root reasons for a case.
This proposed strategy may improve the speed and quality ofinvestigations.
REFERENCES
[1]

[2]

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Casey, E., Digital Evidence and Computer Crime: Forensic Science,
Computers, and the Internet (3rd Edition), Elsevier Inc., pp. 187-306,
2011.
Choraś, M., Project Final Report: Comprehensive Approach to cyberroadMap coordINation and develOpment, ITTI Sp. Z o.o., Poland, pp.
15-39, 2016.
Cummins Flory, T. A., "Digital Forensics in Law Enforcement: A
Needs Based Analysis of Indiana Agencies," Journal of Digital
Forensics, Security and Law, Vol. 11. No. 1, pp. 7-38, 2016.
International Organization for Standardization (ISO), “ISO/IEC
27037:2012 - Information Technology: Guidelines for Identification,
Collection, Acquisition, and Preservation of Digital Evidence,” ISO
Office, 2012.
International Organization for Standardization (ISO), “ISO/IEC
27043:2015 Information Technology - Security Techniques - Incident
Investigation Principles and Processes,” ISO Office, 2015.
Keeling, D. G. and Losavio, M., "Public Security & Digital Forensics
in the United States: The Continued Need for Expanded Digital
Systems for Security," Journal of Digital Forensics, Security andLaw, Vol. 12. No. 3, pp. 47-60, 2017.
Losavio, M.M., Chow, K.P., Koltay, A., and James, J., "The Internetof Things and the Smart City: Legal challenges with digital forensics,
privacy, and security, Security and Privacy, Vol. 1, No. 3, pp. 1-11,
2018.
Pearson, S. and Watson, R., Digital Triage Forensics: Processing theDigital Crime Scene, Elsevier Inc., Burlington, 2010.
Stephenson, P., Official (ISC) 2® Guide to the Certified Cyber
Forensics Professional (CCFP) Common Body of Knowledge (CBK),
Auerbach Publications, pp. 293-404, 2014.
Ubelaker, D. H., The Global Practice of Forensic Science, John
Wiley & Sons Ltd, pp. 34-263, 2015.

Da-Yu Kao received the B.S. and M.S. degree inInformation Management from Central Police
University, Taiwan, in 1993 and 2001, the Ph.D.
degrees in Crime Prevention and Correction fromCentral Police University, Taiwan, in 2009,
respectively. From 1993 to 1996, he was with Taipei
City Police Department, Taiwan, where he was an
information technology police officer involved in thedevelopment of policing information systems. From1996 to 2007, he was with Criminal Investigation Bureau, National Police
Administration, Taiwan, where he was a detective and forensic police officer
in cybercrime investigation and digital forensics. From 2007 to 2013, he waswith Maritime Patrol Directorate General, Coast Guard Administration,
Taiwan, where he was an information technology section chief in thedepartment of information and communication. Since 2013, he has beenwith Central Police University, Taiwan, where he is currently an associate
professor in the Department of Information Management. His researchinterests include cybercrime investigation, digital forensics, digital evidence,
information management, criminal profiling, and cyber criminology.
Ni-Chen Wu received the B.S. degree in Information
Management from Central Police University,
Taiwan, in 2019. Since 2019, she is a police officer
in Taiwan. Her current research interests include
information security, incident response, cybercrime
investigation, digital forensics, information systems
management, criminal profiling, and cybercriminology.
Fu-Ching TSAI received the B.S. degree inInformation Management from Central Police
University, Taiwan, in 2001, the M.S. and Ph. D
degrees in Institute of Information Management from
National Cheng Kung University, Taiwan, in 2005
and 2013. Since 2018, he has been with Central
Police University, Taiwan, where he is currently an
assistant professor in the Department of Criminal
Investigation. His research interests include big data
analysis, data mining, text mining, and artificial
intelligence.

Brooks, C. L., CHFI Computer Hacking Forensic Investigator
Certification All-in-One Exam Guide (1st Edition), McGraw-Hill
Education, pp. 13-50, 2015.
Casey, E., Handbook of Digital Forensics and Investigation,
Burlington, MA: Elsevier Inc., pp. 21-208, 2010.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20191180

ICT Enabling Technologies for Smart Cities
Dzung Van DINH*, Byeong-Nam YOON**, Hung Ngoc LE***, Uy Quoc NGUYEN****,
Khoa Dang PHAN*, Lam Dinh PHAM**Information Technology Institute, Vietnam National University (VNU), Vietnam**Global IT Research Institute (GIRI), Korea***VKX Ltd. (Viet Nam Korea Exchange Joint Venture Company), Vietnam****PTIT (Posts and Telecommunications Institute of Technology), Vietnam
dzung.dinh@vnu.edu.vn, tomayoon@hotmail.com, lnhung@vkx.com.vn,
nguyenquocuy2008@gmail.com, khoapd@vnu.edu.vn, phamdinhlam@vnu.edu.vn

Abstract— A smart city adjusts its social, business, and naturalneeds, improving the assets it has accessible. Information andCommunications Technology (ICT) for shrewd urban areas is to
give city answers for encourage an improvement andmanageability of a city for the advantage of its population, its
economy, and the greater ecosystem in the city. It is to gauge akeen city as far as the enhancements in personal satisfaction andmonetary prosperity that are accomplished through applying ICT
innovations to design, outline, fabricate, and work the city
foundation. In smart city applications, the initial phase in theinformation's voyage through the application is its gathering by
the diverse advancements conveyed all through the city. This
paper surveys data acquisition technologies such as Sensor
Networks, MANETs, Unmanned Aerial Vehicles (UAVs),
Vehicular Ad hoc Networks (VANETs), Internet of Things (IoT),
Software-Defined
Networking(SDN),
Network Functions
Virtualization (NFV), 5G. Next, it demonstrates information
processing technologies, for example, Cloud Platform, IoT
Platform, Big Data Platform, Machine Learning, Deep Learning,and IoT Analytics. Encouraging data spread between various
nodes is vital to savvy city acknowledgment. Last, because of thepresence of various types of end users (e.g., residents,organizations, government offices, and so forth.) requiring
distinctive levels of nature of management, the paper exhibits a
proposed testbed solution and recent associated experiments.
Keywords— IoT, SDN, NFV, 5G, Cloud Platform, IoT Platform,
Big Data Platform, IoT Analytics———————————————————————
Manuscript received October 9, 2018. This work was supported in part by
the “ASEAN forum for Software Defined System on Disaster Mitigation andSmart Cities” and “IoT Open Innovation Platform”, which are funded by
National Institute of Information and Communications (NICT, Japan) underthe ASEAN IVO program (ICT Virtual Organization of ASEAN Institutes
and NICT).
Dzung Van DINH is with the Information Technology Institute, Vietnam
National University (VNU-ITI), Vietnam. (corresponding author phone: +84
91 3222 690 e-mail: dzung.dinh@vnu.edu.vn).
Byeong-Nam YOON is with the Global IT Research Institute (GIRI),
south Korea. (phone: +82 010-9837-8839 e-mail: tomayoon@icact.org)
Hung Ngoc LE is with the Viet Nam Korea Exchange Joint Venture Company(VKX Ltd.), Vietnam. (e-mail: inhung@vkx.com.vn)
Uy Quoc NGUYEN is with the Posts and Telecommunications Institute ofTechnology (PTIT), Vietnam. (e-mail: nguyenquocuy2008@gmail.com)
Khoa Dang PHAN is with the VNU-ITI, Vietnam. (e-mail:khoapd@vnu.edu.vn)
Lam Dinh PHAM is with the VNU-ITI, Vietnam. ( e-mail:phamdinhlam@vnu.edu.vn)

INTRODUCTION
By 2030, the total population is anticipated to be 8.5 billion
and increment to 9.7 billion by 2050 and 11.2 billion by 2100.
Half of humankind today lives in urban areas. Numerous urban
communities are encountering exponential development as
individuals move from rustic zones looking for better
occupations and training. Subsequently, urban administrations
and foundations are being extended as far as possible as far as
adaptability, condition, and security as they adjust to help thispopulation growth. Visionaries and organizers are subsequently
looking for a maintainable, present carbon economy on enhancevitality productivity and limit carbon-emanation levels.
The European Parliament defined: "A smart city is a cityseeking to address public issues via information andcommunication technology (ICT) - based solutions on the basisof a multi-stakeholder, municipality based partnership." This isvery expansive, including many fields, while the Japanese
definition is more particular, concentrating on energy,
infrastructure, ICT, and lifestyle.
From these definitions, we derive ICT assumes an urgent
part in building up a city that can adjust to the necessities of its
nationals. By utilizing advanced power systems, networking,and communication technologies, a smart city aims to enhance
the lives of its citizens and optimize territorial, economic, andenvironmental resources. Navigant Research says investment
in smart cities is divided into smart government, smart building,smart transport, and smart utilities. By 2020, $13 billion in
funding is expected to establish smart cities all over the world(see Figure 1). [1]The rest of this paper is organized as follows. We havereviewed the ICT enabling technologies for data acquisition in
the context of smart cities (Section II). Next, we have analyzed
potential networking and computing technologies for smart
cities (Section III). Section IV presents data processing
technologies candidates for smart cities. The proposed solution
and associated experiments are provided in Section V. Finally,
we summarize and conclude this study in Section VI.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

Figure 1. Smart city investments by the regions, 2014–2023 [1]

I. DATA ACQUISITION TECHNOLOGIES FOR SMART CITIESIn this section, a review of the ICT enabling technologies fordata acquisition in the context of smart cities is given. These
technologies include Wireless Sensor Networks (WSNs),
Mobile Ad hoc Networks (MANETs), Unmanned Aerial
Vehicles (UAVs), Vehicular Ad hoc Networks (VANETs), 5G.

1181

A. Wireless Sensor Networks
Sensor networks are used for many applications (e.g.,
environment monitoring, waste management, healthmonitoring, smart grids, etc.), for collecting data in smart cities.
Current cities are already deploying a wide range of sensors(e.g., motion sensors, cameras, sensors for collecting
environmental parameters, etc.) [2]. In general, the applications
can be divided into two types: monitoring and tracking.
Monitoring is used for analysing and controlling a system inreal-time (environment, agriculture, industry, health care,ecology, urban, smart house, military, etc.). Tracking is used
for recording the change of an event, a person, an animal, andso on (industry, public health, ecology, military, etc.) [3].
1) Radio technologies: The standardization of WSN has
been very active in the recent years. A comparison of emerging
and existing radio technologies for WSNs is presented in Table
1.

TABLE 1. WSN STANDARDS AND TECHNOLOGIES [4]2) Machine Learning in Wireless Sensor Networks:
WSNs screen dynamic conditions that change quickly aftersome time. This dynamic conduct is either caused by outer
factors or started by the framework originators themselves. To
adjust to such conditions, sensor networks often adopt machinelearning techniques to wipe out the requirement for pointless
upgrade. Machine learning additionally motivates numerous
solutions that boost asset use and draw out the life expectancyof the network.
Supervised learning methods are broadly used to understanda few difficulties in WSNs, for example, localization andobjects targeting, event detection and query processing, media
access control, security and intrusion detection, and quality ofservice (QoS), data integrity and fault detection.
Unsupervised learning methods are not provided with labels.
Fundamentally, the objective of an unsupervised learning
calculation is to arrange the example set into various gatherings

by examining the closeness between them. Of course, thissubject of learning calculations is generally utilized as a part ofnode clustering and data aggregation problems.
Reinforcement learning empowers an agent (e.g., a sensor
node) to learn by cooperating with its surroundings. The agent
will figure out how to take the best actions that augment its long
haul remunerates by utilizing its own particular experience. [5]3) Software-Defined Wireless Sensor Networks: WSN is a
low-rate wireless transmission platform with little resources
and short communication ranges. As the scale of WSN
increases, it faces some challenges, such as network
management and heterogeneous-node networks. As Software
Defined Networking (SDN) provides simplicity in network
management, and computing resources configuration, it cansolve these problems.
The SDN way to deal with remote sensor systems involves
abstracting distinctive functionalities and revamping them

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

along the three coherent planes in the SDN architecture:application, control and data. The advancement of the SDWSN
design is still in its early research stages. Despite the fact that
there are a number of models, they all fit in with the basics of
SDN: decoupling. Fig. 2 shows the fundamental functionalities
of SDWSN as proposed by different authors. SDN display
brings along its own difficulties, particularly the trade-off
between functionalities that should be held on the sensor nodeand the effect on basic network factors, for example, latency,
congestion, and so on. [6]1182

B. IEEE 802.11-enabled wake-up radio system
The Internet of Things is anticipated to comprise of billionsof devices by 2020. The Wi-Fi, Mobile, and Bluetooth
protocols are likewise the three main candidates for interfacing
the Internet of Things - and each has own advantages anddrawbacks. Huge numbers of these devices will keep runningon batteries. To draw out the helpful existence of these batteries,
while ensuring devices don't remain in a rest state too long andrun gradually, devices must have the capacity to keep runningin a low-control, low-inertness state. Wake-Up Radio, which is
being created by the IEEE 802.11ba working group, offers ananswer for this challenge. The IEEE 802.11ba Wake-Up Radio
adds an additional low-power radio receiver to the device (seeFig. 4). The low-power radio listens for the system call. [11]Figure 4. Overview of low-power radio for IEEE 802.11ba [11]Figure 2. Basic SDWSN architecture [6]4) Wireless Sensor Network Virtualization: WSNs' hubs
are ending up increasingly intense, it is getting increasingly
correlated to explore how different applications could share an
exceptionally same WSN platform. Virtualization is aninnovation that can possibly empower this sharing (see Fig. 3).
It is a promising method that can permit the effective usage of
WSN arrangements, as various applications will have thecapacity to exist together on the same virtualized WSN.
There are open research issues that should be tended to forgiving new WSN virtualization arrangements: propelled nodelevel virtualization, network level virtualization, discovery andpublication, service integration, sensor node choice andassigned task, application undertaking dispersal, reference
design and structures, new protocols, techniques and simulation
tools, WSN virtualization plan of action and standardization,energy effective arrangements, access control, authentication,
and accounting, and WSN virtualization user cases and testing
environments. [7]C. Vehicular Ad hoc Networks (VANETs)
VANETs are a sort of MANETs that are widely consideredwith regards to smart mobility and intelligent transportation.
VANETs encourage Vehicle to Vehicle communication (V2V)
and Vehicle to Infrastructure communication (V2I). Vehicleto-anything (V2X) communications represent both V2V andV2I. Since VANETs are a variation of MANETs, the issues that
torment MANETs additionally impact the data obtaining
process in VANETs. Distinctive sorts of uses can use theinformation gathered in VANETs. These applications can besorted into security and infotainment applications. Security
applications are utilized to enhance street wellbeing, forexample, forward collision warning, while infotainment
applications are equipped towards solace and amusement forvehicle travellers, for example, traffic and weather information.
[8]
V2X innovation has an extraordinary capability of
empowering an assortment of novel applications for street
safety, traveller infotainment, car producer administrations, andcar traffic optimization. Today, V2X communications depends
on one of two fundamental methods: dedicated short range
communications (DSRC) and mobile communications
networks (see Fig. 4).

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20191183

on UAVs is restricted, conventions in different layers ought tocontribute towards greening of the system. [10]E. 5G Communications

Figure 5. V2X communications in a DSRC-cellular hybrid scenario [9]To enable V2X applications for an expansive number ofvehicles, interworking amongst DSRC and cell arrange
advancements is a promising methodology, which can befounded on a flat or a hierarchical DSRC-cellular hybridarchitecture. Nonetheless, so as to proficiently accomplish such
DSRC-cell interworking, we have to determine numerous
specialized issues, primarily starting from the profoundly
unique vehicular system topology, together with the pattern oflittle cell arrangement in cutting edge cell systems, which
requires successful vertical handover strategies and systemdetermination plans. What's more, DSRC is accomplished oversaved radio range groups, which contrast in North America(IEEE 1609), Europe (ETSI EN 102/202/300/302), and Japan(ARIB STD-T55/75/88/109/110), leading to incompatibility
problems among these regions. [9]1) Vision: The fifth generation mobile communications
system (5G) is expected to launch in 2020, is required to givearound 1000 times higher wireless area traffic capacity and save
up to 90 percent of energy consumption per service comparedwith the current 4G system. More than 1000 Gb/s/km2 area
spectral capacity in dense urban environments, 10 times higher
battery life of associated devices, and five times lessened endto-end (E2E) delay are expected in 5G systems. The new 5G airinterface and range ought to be joined together with the Long
Term Evolution (LTE) and WiFi to give all inclusive high-ratecoverage and seamless user experience. [12]Figure 6 shows the key 5G requirements in contrast with earlier
mobile networks generations, for example, IMT-Advanced.
[13][14]TABLE 2. AD-HOC NETWOKS COMPARISION [10]D. Unmanned Aerial Vehicles (UAVs)
Unmanned Aerial Vehicles (UAVs) show potential in thegeneral the public and civil areas. These are especially helpful
in applications where human lives would some way or anotherbe jeopardized. Multi-UAV frameworks can cooperatively
entire missions all the more effectively and financially whencontrasted with single UAV frameworks. Be that as it may,there are many issues to be settled before viable utilization of
UAVs can be made to give steady and solid setting particularsystems.
Table 2 provides a summary of MANETs, VANETs andUAV systems. Most of the studies of MANETs, and VANETs
does not address the exceptional attributes of the UAV systems.
UAV systems may change from ease back unique to dynamic;
have discontinuous connections and fluid topology. While it istrusted that ad hoc mesh network would be most reasonable for
UAV arranges yet the engineering of multi-UAV systems is fora further study. SDN could encourage deployment andmanagement of new services and help decrease cost, increment
security and accessibility in systems. Steering requests of UAV
systems go past the necessities of MANETs and VANETs.
Conventions are required that would adjust to high versatility,
dynamic topology, irregular connections, control imperatives
and changing connection quality. UAVs may come up short andthe system may get divided making deferral and disturbanceresistance a critical outline thought. Restricted existence of thenode and dynamicity of the system prompts the necessity ofseamless handovers where investigators are taking a result at
the work done for MANETs and VANETs. As vitality supply

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20191184

multiple-access schemes are almost equivalent with respect tocapacity. The differences between multiple-access schemes
become visible when transmission channels exhibit frequency
selectivity and time variability.
Novel various access plans take into account for overloading
the spectrum by multiplexing users in the power and the code
domain, resulting in non-orthogonal access, where the numberof simultaneously served users is no longer bound to thenumber of orthogonal resources. This approach empowers thequantity of associated devices to be expanded by a factor of 2–
3 and, in the meantime, to obtain gains in user and systemthroughput of up to 50%. Potential schemes are NonOrthogonal Multiple Access (NOMA), Sparse Code Multiple
Access (SCMA) and Interleave Division Multiple Access(IDMA).
Figure 6. IMT-2020 and IMT-A requirements [14]TABLE 3. 5G MULTIPLE AND MEDIUM ACCESS SCHEMES. [14]2) Machine-Type Communication (MTC): MTC indicates
the wide territory of wireless communication with sensors,
actuators, physical articles and different devices notstraightforwardly used by people, shaping the supposed
Internet of Things (IoT). The present view on the 5G wirelesssystems specifies MTC into two groups, as appeared in Figure
7: massive MTC (mMTC) and ultra-reliable and low-latency
MTC (uMTC). mMTC accept delay-tolerant services forregularly occasional data transmissions, with a huge number ofdevices that are now and then even battery-worked. Meanwhile,uMTC can provide reliable and low delay communications forcontrolling objects and processes in real-time. [14]Figure 7. MTC in the 5G wireless systems. [14]3) The 5G radio-access technologies: Table 3 gives a short
diagram on the radio-access technologies, featuring some of
their attributes and properties. [14]Orthogonal multiple-access techniques depend on
partitioning radio assets (in time or frequency) between
different subscribers. The schemes are respectively Frequency
Division Multiple Access (FDMA), Time Division Multiple
Access (TDMA) and Orthogonal Frequency Division Multiple
Access (OFDMA). FDMA and OFDMA are very similar,
however, FDMA has non-overlapping frequency sub-bands,
while OFDMA has overlapping frequency sub-bands. In asingle cell infrastructure with AWGN channels, all orthogonal

4) Network Slicing for 5G with SDN/NFV: One of the key
drivers of 5G systems is the need to help an assortment ofvertical businesses, for example, manufacturing, automotive,
healthcare, energy, and media and entertainment. Such verticals
begin altogether different utilize cases, which force asignificantly more extensive scope of requirements thanexisting mobile services. The present systems, with their "oneestimate fits-all" compositional approach, can't address theseparating execution prerequisites that verticals force as far as
latency, scalability, availability, and reliability. System
softwarization, a trend that looks to change systems utilizing
programming based arrangements, can be a potential
empowering influence for comprehending this. Throughinnovations like SDN and Network Functions Virtualization(NFV), organize softwarization can give the programmability,
adaptability, and measured quality that is required to makenumerous consistent (virtual) systems, each customized for agiven utilize case, over a typical physical system. [15]Copyright ⓒ 2019 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

Figure 8. 5G network independent slices running on a common underlying
physical multi-vendor and multi-access network for user cases. [15]5) 5G Standardisation: The overall 5G roadmap issummarized in Figure 8, capturing trials phases and the highlevel standardization time plans. The standards for 5G are to beprepared by the ITU-R. Working Party (WP) 5D is at present
getting ready assessment criteria to be trailed by entries ofrecommendations and assessment of candidate technologies.
This procedure is relied upon to be finished by late 2019,
prompting the first certified 5G standards. 3GPP distributes
major releases roughly once per year. Standardisation of 5G
technologies is broken into two stages. Stage 1 will be finishedby September 2018 in Release 15. Stage 2 will fuse morecapacities to stretch out the abilities of 5G to dynamicallybolster more services, case studies and significantly higher
frequency bands (e.g., over 40 GHz). Stage 2 will be finishedaround the end of 2019 in Release 16. IEEE has as of late started
a 5G track to supervise the guide of upgrades that will happen
for various existing and new IEEE standards, for example,
802.11ax (WLAN), 802.15 (short range communications),
802.22 (Fixed Wireless Broadband), P1914.3 (for Cloud RAN),
P1918.1 (tactile and haptic Internet). Standardization roadmaps
vary for the individual specification groups. [17]centers [18]. The solid coupling amongst control and data
planes has made it hard to add new features to existing networks(see Fig. 10). Solving these issues, SDN decouples the control
plane from the network devices and becomes an external entity:
the Network Operating System (NOS) or SDN controller. [19]SDN provides extreme flexibility and high programmability.
The data plane managed by the controller enables cost-effective
and dynamic network configuration in support of smart cities.
SDN is being proposed as a networking candidate for smart
cities, by extending connectivity to homes using SDN for
capacity sharing, securing communications and user mobility
management. [2]B. Network Functions Virtualization
Network Function Virtualization (NFV) has drawn hugeconsideration from both industry and the scholarly world as a
critical move in media transmission benefit provisioning. By
decoupling Network Functions (NFs) from the physical nodes
on which they run, NFV can possibly prompt huge
diminishments in Operating Expenses (OPEX) and Capital
Expenses (CAPEX) and encourage the deployment of newservices with increased agility and faster time-to-value.
Network Function Virtualization (NFV) has drawn significant
attention from both industry and academia as an important shift
in telecommunication service provisioning.

Figure 10.

Figure 9. 5G Trials and Standardization Roadmaps [16]II. NETWORKING AND COMPUTING TECHNOLOGIES FOR
SMART CITIES
A. Software-Defined Networking
SDN is a generally novel approach to program the switches
used in present day networking systems. SDN’s uses a
centralized control architecture which is better suited to theextremely large networks prevalent in today’s mega-scale data

1185

Traditional networking versus SDN. [19]Connection between NFV, SDN, and Cloud computing is
depicted in Figure 11. Each of these fields is a deliberation ofvarious assets: figure for distributed computing, organize for
SDN, and capacities for NFV. The points of interest that gather
from each of them are comparable; readiness, costdiminishment, dynamism, computerization, asset scaling andso forth. The inquiry isn't whether NFs will be relocated to thecloud, as this is in reality the general thought of NFV. It iswhether the cloud will be an open one like Amazon, or if TSPs
will want to client private ones appropriated over theirinfrastructure. In any case, it is to make the cloud carrier-gradein terms of performance, reliability, security, communication
between functions and so on. Then again, NFV objectives canbe accomplished utilizing non-SDN instruments, andCopyright ⓒ 2019 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

depending on the systems at present being used in numerous
server farms. Nonetheless, approaches depending on thedivision of the control and data forwarding planes as proposedby SDN can improve execution, disentangle similarity withexisting infrastructure, and encourage operation and upkeep
systems. Similarly, NFV can bolster SDN by giving theframework whereupon the SDN programming can be run. At
long last, the cutting edge variation of a data center (the cloudand it's self-benefit perspective) depends on automatedmanagement that might be gotten from SDN and NFV.
Specifically, functions such as network as a service, loadbalancing, firewall, VPN could run via APIs. [20]C. Cloud and Fog/Edge computing
Figure 12 demonstrates the customary Cloud processing
structure. Information makers create crude information andexchange it to cloud, and consumers send ask for expending
information to cloud, as supported by the blue line. The red
dotted line shows the demand for expending information being
sent from information consumers to cloud, and the outcome
from cloud is spoken to by the green specked line. In any case,this structure isn't adequate for IoT. Data amount at the edge is
too big, which will prompt enormous pointless datatransmission and registering asset use. Also, the security
assurance requirement will represent an impediment for Cloudregistering in IoT. Ultimately, most of the end IoT nodes areenergy constrained, so releasing some computing tasks at theedge could save their energy.

Figure 13.

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Fog/Edge computing paradigm. [21]Different sorts of smart city applications can be planned andexecuted with the help of Cloud and Fog/Edge computing.
These include applications for intelligent transportation
systems, smart energy systems, infrastructure and environment
monitoring, and public safety applications. Table 4 shows some
smart city applications and how they can benefit from both the
Cloud and Fog / Edge computing. [22]III. DATA PROCESSING FOR SMART CITIES
A. Database Technologies
Database innovation has experienced over three decades ofimprovement. Different database frameworks have been
proposed for various sizes of datasets and assorted applications.
Current relational database systems certainly cannot address thevariety and scale challenges required by big data. Due to certain
essential characteristics, including being schema free,
supporting easy replication, possessing a simple API, eventual
consistency and supporting a huge amount of data, the NoSQL
database is becoming a solution to solve big data problems.
Table 5 shows a comparison of NoSQL databases with different
data models such as key-value stores, column-oriented
databases, and document databases.
B. Big Data Analytics

Figure 11.

Fog/Edge computing paradigm. [20]Fog/Edge computing refers to the enabling technologies
allowing computation to be performed at the edge of the
network, on downstream information in the interest of cloudservices and upstream data in the interest of IoT services (seeFig. 13). Edge computing is tradable with Fog computing, yet
Edge computing concentrates more toward the Things side,
while Fog computing does more around the the infrastructureside. [21]Figure 12.

Cloud computing paradigm [21]1) Techniques for Content Analytics
The data of the substance could be gathered from both socialand physical detecting nodes. Social detecting is to gather databy means of web-based social networking, and physicaldetecting is gathering data through sensors in the physicalworld. We chiefly concentrate on the famous procedures
embraced in social big data analytics, in spite of the fact thatthe said methods can be utilized as a part of the physical data
analytics area.
A distinction exists between utilizing huge data analytics andregular counterfeit consciousness to remove knowledge frominformation. Manmade brainpower has focused on the
improvement of calculations and models. It particularly
endeavors to set up however many principles as could
reasonably be expected to influence registering nodes tocomprehend the data sources and afterward get expected results.
The current enormous information approach focuses on the
nonstop learning process after the calculations or potentiallymodels are created. This approach particularly concentrates on

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

the issue of how a right arrangement can be created in a brief
span (i.e., constrained time). The arrangement is spoken to by
the methodologies for getting and applying the outcomes.
Content analytics which isn't another field of study, depends
on existing hypotheses and techniques, for example, data
mining, machine learning, and measurements and likelihood.
From a more extensive point of view, these techniques can be1187

perceived as statistical learning. Despite the fact that thesetechniques have been changed to meet the reasons and needs invarious zones of enormous information issues, for example,
item advancement, producing, production network
administration, medicinal services applications, promoting,deals forecast, human relationship examination, and so forth.,they can be basically arranged in the frame appeared in Fig. 14.

TABLE 4. SMART CITY APPLICATIONS BASED ON CLOUD AND FOG COMPUTING. [22]TABLE 5. COMPARISION OF NOSQL DATABASE SYSTEMS. [23]Copyright ⓒ 2019 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 20191188

from the raw data earlier/after the procedure of machinelearning. Data mining is completed by a man in a particular
circumstance, on a specific data set, and on account of an
objective. The data set is often big and sophisticated, and may
have special problems, which we can expect from raw data.
Usually, the goal is either to discover some preliminary insights
in an area with little knowledge acquired beforehand or to
predict future observations accurately. Note that the goal isgenerally not to develop a more sophisticated knowledge of theunderlying data generating process.

Figure 14.

Family Tree of Statistical Learning. [24]By and large, two fields of study, which are machine learningand data mining, are best executed in the statistical learning
process. Machine learning predicts a specific result through theinfo information. An arrangement of calculations is regularly
connected to remove data consequently without online human
direction. Some of these methodology incorporate thoughts got
specifically from or roused by traditional measurements. Most
sensible strategies for machine learning can be planned as aformal probabilistic model. Subsequently, machine gaining
from this perspective is especially the same as measurements,
however it contrasts in that the parameter gauges have lesssignificance and the emphasis is on computational effectivenessand huge datasets. Data mining focuses on design disclosure2) Big Data Analytics for Disaster Management
A smart communication network is essentially critical in adisaster happen to a city, since both rescue and recovery greatly
depend on the provided communication channels.
Notwithstanding, the regular correspondence arrange isdelicate once a debacle happens. A worldly correspondence
framework, to be specific, an emergency communication
network (ECN) of a smart city, must be immediately built toconnect people together.
Thinking about the highlights of a disaster (e.g., constrained
assets and dynamic changing of condition), it is dependably a
key issue to utilize restricted assets viably to give the best
response communications. Big data analytics in the hazardous
situation gives conceivable answers for comprehend thecircumstances occurring in a disaster zones, with the goal that
constrained assets can be ideally sent in light of the analysis
results. Analysing different stages of disaster such as before andafter a disaster, content analytics can support the disastermanagement as listed in Table 6.

TABLE 6. CONTENT ANALYTICS CAN SUPPORT THE DISASTER MANAGEMENT. [25]3) Deep Learning for Health Informatics
With a huge convergence of multimodality data, the data
analytics in health informatics has developed quickly in themost recent decade. This has likewise provoked expanding
premiums in the age of systematic, data driven models in lightof machine learning in wellbeing informatics. Deep learningtechnique with the artificial neural networks, is emerging inrecent years as a powerful tool for machine learning, promising

to reshape the future of artificial intelligence. Quick
improvements in computational power, fast data storage, andparallel processing have also contributed to the rapid uptake ofthe technology in addition to its predictive power and ability togenerate automatically optimized high-level features andsemantic interpretation from the input data.
Table 6 outlines the important applications in the five
regions of health informatics: translational bioinformatics,

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

medical image processing, sensing, medicinal informatics, andpublic healthcare.
TABLE 7. SUMMARY OF DEEP LEARNING METHODS BY AREAS
AND APPLICATIONS IN HEALTH INFORMATICS. [25]1189

prevent outages. Moreover, electric utilities are using predictive
analytics on the smart grid data to forecast several indicatorsthat can support running the grid operations efficiently,
economically, and reliably. [26]5) Big Data Analytics for Geolocation Prediction
Geolocation prediction (GP) can be applied to geolocationbased services (GBS). GP is based on Markov-based andBayesian network-based methods. Mobility Big Data (MBD)
rises new challenges and opportunities for geolocation
prediction. The geolocation prediction can be divided into threestages as shown in Fig. 15. First, mining popular geolocation
region is to pre-process raw geolocation data collected by smart
mobile devices; second, mining personal trajectory information;
third, building a geolocation prediction model. [27]4) Big data analytics for the smart grid
The data from smart meters, Phasor-Measurement-Units(PMUs), charging, climate sensors, and other intelligent
electronic devices (IEDs) has opened-up a plenty of chances,for example, predictive analytics, demand-side-management,
real-time grid awareness, outage detection, asset management,
and theft detection to name a few. Big data can help take gridmanagement to another level. For example, expanding demandresponse for saving energy has been made possible as a resultof enhanced accessibility to customer’s energy usage data. In
addition, evaluating data from PMUs and IEDs is to maximize
safety, ensure service reliability, improve customer service, andFigure 15.

Mobility big data-based geolocation prediction. [27]

IV. PROPOSED SOLUTION AND EXPERIMENTS
A. Inter-Smart Cities Testbed
Our inter-smart cities testbed is to connect smart city
testbeds located in Hanoi, Manila, Myanmar. This platform isbeing connected to SDN testbed (RISE) and IoT testbed inJapan (JOSE). This is to investigate ICT enabling technologies
for smart cities such as IoT protocols, SDN/NFV,edge/fog/cloud computing, big data platform, IoT data analytics,
and smart city applications (see Fig. 16).
.

Virtual Storages (Sub-Project)

SDN-IP (Sub-Project)Internet / TEIN (Trans-Eurasia Information Network) / APAN / RISE / JOSE

mmREN

PREGINETSDN-IP based VinaREN

Mapua SDN / NFV – IoT Testbed

UCSY SDN / NFV – IoT Testbed

NECTEC SDN / NFV – IoT Testbed

SDN / NFV – IoT Testbed for
Smart City (X)SDN / NFV – IoT Testbed for Smart City (Y)ThaiREN

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019
Figure 16.

1190

Inter Smart Cities Testbed

B. IoT Gateways, WSNs
In order to compare the pros and cons between MQTT and
CoAP protocols, we use Raspberry Pi3 as IoT Gateway, whichcollects data sensor through ESP8266, using MQTT (see Fig.
16). The messages collected by the gateway from the sensors
are usually very small because they contain only the current
value of the temperature, humidity, light… measured by thesensor and they are just decimal numbers. This is a very
important thing to remember: the gateway collects and operates
on a very large number of small messages. The gateways areconnected to the Internet using WiFi, or Ethernet. After
collecting data sensors, it sends data to the IoT server. In our
experiment, we use laptop as a IoT server.

This model uses the Mininet tool and Opendaylight tosimulate the virtual switches and controller. Opendaylight(ODL) is a modular open platform for customizing andautomating networks of any size and scale. The Opendaylight
Project arose out of the SDN movement, with a clear focus on
network programmability. It was designed from the outset as afoundation for commercial solutions that address a variety ofuse cases in existing network environments.
Mininet simulates 3 virtual switches connected together,each virtual switch has a virtual host associated. The version ofOpendaylight used here is Carbon SR1. Through Opendaylight,
two networks between two Mininet machines are connected,
exchanging data between virtual hosts in the network controlled
by Opendaylight. Figure 19 shows a simplified model of the
SDN testbed in Hanoi.
h11: 10.0.11.11
h12: 10.0.12.12
h13: 10.0.13.13

h11

h12

Ovs11

Ovs12

Ovs13 h13
VNU Hosted
27.118.21.208

Opendaylight - Hanels
27.118.21.209

Internet

h21: 10.0.11.21
h22: 10.0.12.22
h23: 10.0.13.23

Figure 17.

IoT system for testing MQTT và CoAP protocols

h21

h22

Ovs21

Raspberry Pi 3 acts as publisher and subscriber. The data issent to the server and received using two protocols MQTT and
CoAP, with different QoS. The two open source servers usedon laptop are Mosquitto and libcoap, and a Python-built
program will repeatedly emulate the data received from thesensor, and then send it to the server. and also receive data fromthe server, then calculate the difference between the time fromsending data to receiving. NetEm software is used to create
delay, lost in network. To ensure the reliability of the results,
each message sent and received is closely monitored by the
Wireshark software.
In Figure 17, data flow simulation in the IoT system is used
for the experiment. Data published from the IoT gateway, via
the LAN or Wifi, which is simulated parameters such as latency,
lossy ... with the NetEm software, is sent to the server, and thenthe data returned from the server to the subscriber through LAN
or Wifi.

Ovs22
TNU
125.214.0.54

Ovs23 h23

Figure 19.

Simplified model of the SDN testbed in Hanoi

D. IoT Platform
Now we will describe about our ITI testbed using Kaa and
HDP platform. The Kaa IoT Platform will be used for frontline
activities that are real-time data collection across connected
physical assets (humidity sensors) and dispatching that data to
Cassandra of HDP for further processing and storage. The
architecture of the ITI testbed is show in Figure 19.

Figure 20.
Figure 18. IoT system using MQTT và CoAP

We also tested to send Data from IoT Gateway to IoT server,which is installed IoT Platform Kaa.
C. Networking solutions for smart cities

The IoT platform based on Kaa and HDP

In our testbed, we have a Kaa cluster and this cluster supports
and handles request from Raspberry Pi application, so thatapplication collects raw data from humidity sensors about
relative humidity in air and then it pushes this data using Kaa
SDK to the Kaa cluster. The Kaa SDK transfers not raw data
but structured data to Kaa node. When the data is transferred to

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019

Kaa node, the Cassandra appender of Kaa platform will used topush this data to Apache Cassandra of HDP. Cassandra
appender and all other appenders, which are implemented andsupported by the Kaa platform have ability to guarantee datadelivery and in case of any delivery and data delivery errors theerror message is pushed back to Kaa SDK. When data ispushed into Cassandra we will use Apache Zeppelin of HDP inorder to build server dashboard and visualize the IoT data.
V. CONCLUSIONSIn this paper, we review the ICT enabling technologies fordata acquisition in the context of smart cities. These
technologies include Wireless Sensor Networks, Mobile Ad
hoc Networks, Unmanned Aerial Vehicles, Vehicular Ad hoc
Networks, 5G. Next, we analyze potential networking andcomputing technologies for smart cities such as SDN, NFV,
Fog/ Edge/ Cloud computing. For data processing in smart
cities, it is recommended to deploy scalable IoT platforms withadvanced machine learning techniques.
For validating the proposed ICT architecture for smart cities,
we have build local testbeds in Vietnam, Philippines, Myanmar
which then being connected to SDN, IoT national testbeds inJapan.
ACKNOWLEDGMENT
Authors acknowledge support from the research projects:“ASEAN forum for Software Defined System on Disaster
Mitigation and Smart Cities” and “IoT Open Innovation
Platform”, which are funded by National Institute ofInformation and Communications (NICT, Japan) under the
ASEAN IVO program (ICT Virtual Organization of ASEAN
Institutes and NICT).
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Dzung Van DINH - Dr. Dzung has over 25 years ofexperience in ICT R&D, consulting, and training. Heworks in a number of research areas including network
optimization, mobility management, Future Internet,
IoT, 5G, and Big Data. Dr Dzung is a Fulbright
Scholar at New York University, USA. He has obtained
his Ph.D. from the Post and Telecoms Institute ofTechnology, Vietnam, the M.E. degree from Universityof Technology, Sydney, Australia, B.E. degree fromthe Odessa Institute of Telecommunications, the former Soviet Union.
Dr. Dzung led many national research projects, acted as a principle member ofinternational projects at RisTI (Indonesia), NTT, NICT (Japan), LG, ETRI
(Korea), and NYU (USA). He was the ICT project leader, trainer, andconsultant for VNPT, Saigon Postel, EVNTelecoms, VNU HCM City,
Thailand Telecoms, and AIT. He actively contributes to International Telecoms
Union (ITU) and APT activities in the roles of Vice-Rapporteur, Vice-chairmanof ITU-D Study Group 2. Dr. Dzung published more than 30 papers in national
and international journals and conferences. He has been the Deputy Director of
Research Institute of Post and Telecoms and the Assistant Director of Vietnam
Branch - Korea Information and Communications Society (KICS). Dr. Dzungis the Deputy Director of Information Technology Institute – Vietnam National
University, Hanoi (VNU). He has been appointed as an Adjunct Professor at
the University of Technology, Sydney. Dr. Dzung’s education background:

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 8, Issue 1, January 2019
Byeongnam Yoon (M’97) He became a Member ofIEEE in 1997. He was born in Seoul Korea 15
November 1949. He got the PhD in computer science,
Chungnam National University, Dejon city, Korea,
1997. He worked for the Sperryrand UNIVAC as a
Computer Specialist 1974 -1978, Samsung as aManager of Telecommunications Section 1978 -1982,
Electronics & Telecommunications Research Institute(ETRI) as a Principal Researcher 1982 – 1999, National
Information society Agency (NIA) as a Senior Executive Director General 1999
– 2010, Kyonggi University as an Associate Professor Faculty of Computer
Science 2010 - 2016. Global IT Research Institute GIRI) as a President 1999 –current. His research area includes a Telecommunications, Internet, Software,
Web programming & security, e-Government, Enterprise Architecture, Work
Flow, Information Control Nets.
Le Ngoc Hung received the B.E in computerengineering from Hanoi university of science andtechnologies, Hanoi, Vietnam, in 1992 and the M.E incomputer science from Hanoi national university,
Hanoi, Vietnam, in 2000. His research interests aredecision making, mobility management, computationalmoderling methods. He is currently working toward
Ph.D degree at Post and Telecommunication Instituteof Technology (PTIT), Vietnam.

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Uy Quoc NGUYEN has obtained his Ph.D in Russia. He is with the Post andTelecoms Institute of Technology, Vietnam. His research area includes blockchain, IoT, Artificial Intelligence.
Khoa Dang PHAN is with the Information Technology Institute, Vietnam
national University, Hanoi. His research area includes big data analytics,
Artificial Intelligence.
Lam Dinh, PHAM He was born in Vietnam 5
December 1986. He got the MSc in computer science,
Thai Nguyen University, Thainguyen, Vietnam, 2010.
He worked for Thainguyen University 2008-2016,
Information Technology Institute, Vietnam national
University, Hanoi, 2016-current as Deputy head of
Science, Technology and Training Department. His
researcharea
includesBusiness
Processing
Management, Work Flow, Information Control Nets,
Artificial Intelligence.

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