Internet of Things - wikipedia Internet of Things Drawing representing the Internet of things ( IoT ). The Internet of things ( IoT ) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect, collect and exchange data. IoT involves extending Internet connectivity beyond standard devices, such as desktops, laptops, smartphones and tablets, to any range of traditionally dumb or non-internet - enabled physical devices and everyday objects. Embedded with technology, these devices can communicate and interact over the Internet, and they can be remotely monitored and controlled. With the arrival of driverless vehicles, a branch of IoT, i.e. the Internet of Vehicles starts to gain more attention. Contents 1 History 2 Applications 2.1 Consumer applications 2.1. 1 Smart home 2.1. 2 Elder care 2.2 Commercial applications 2.2. 1 Medical and healthcare 2.2. 2 Transportation 2.2. 3 Building and home automation 2.3 Industrial applications 2.3. 1 Manufacturing 2.3. 2 Agriculture 2.4 Infrastructure applications 2.4. 1 Metropolitan scale deployments 2.4. 2 Energy management 2.4. 3 Environmental monitoring 3 Trends and characteristics 3.1 Intelligence 3.2 Architecture 3.2. 1 Network architecture 3.3 Complexity 3.4 Size considerations 3.5 Space considerations 3.6 A solution to `` basket of remotes '' 4 Frameworks 5 Enabling technologies for IoT 5.1 Addressability 5.2 Short - range wireless 5.3 Medium - range wireless 5.4 Long - range wireless 5.5 Wired 5.6 Standards and standards organizations 6 Politics and civic engagement 7 Government regulation on IoT 8 Criticism and controversies 8.1 Platform fragmentation 8.2 Privacy, autonomy, and control 8.3 Data storage 8.4 Security 8.5 Safety 8.6 Design 8.7 Environmental sustainability impact 8.8 Intentional obsolescence of devices 8.9 Confusing terminology 9 IoT adoption barriers 9.1 Lack of interoperability and unclear value propositions 9.2 Privacy and security concerns 9.3 Traditional governance structures 9.4 Business planning and models 10 See also 11 References 12 Bibliography 13 External links History The definition of the Internet of things has evolved due to convergence of multiple technologies, real - time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, wireless sensor networks, control systems, automation ( including home and building automation ), and others all contribute to enabling the Internet of things. The concept of a network of smart devices was discussed as early as 1982, with a modified Coke machine at Carnegie Mellon University becoming the first Internet - connected appliance, able to report its inventory and whether newly loaded drinks were cold. Mark Weiser 's 1991 paper on ubiquitous computing, `` The Computer of the 21st Century '', as well as academic venues such as UbiComp and PerCom produced the contemporary vision of IoT. In 1994, Reza Raji described the concept in IEEE Spectrum as `` ( moving ) small packets of data to a large set of nodes, so as to integrate and automate everything from home appliances to entire factories ''. Between 1993 and 1997, several companies proposed solutions like Microsoft 's at Work or Novell 's NEST. The field gained momentum when Bill Joy envisioned Device to Device ( D2D ) communication as a part of his `` Six Webs '' framework, presented at the World Economic Forum at Davos in 1999. The term `` Internet of things '' was likely coined by Kevin Ashton of Procter & Gamble, later MIT 's Auto - ID Center, in 1999, though he prefers the phrase `` Internet for things ''. At that point, he viewed Radio - frequency identification ( RFID ) as essential to the Internet of things, which would allow computers to manage all individual things. A research article mentioning the Internet of things was submitted to the conference for Nordic Researchers in Logistics, Norway, in June 2002, which was preceded by an article published in Finnish in January 2002. The implementation described there was developed by Kary Främling and his team at Helsinki University of Technology and more closely matches the modern one, i.e. an information system infrastructure for implementing smart, connected objects. Defining the Internet of things as `` simply the point in time when more ' things or objects ' were connected to the Internet than people '', Cisco Systems estimated that IoT was `` born '' between 2008 and 2009, with the things / people ratio growing from 0.08 in 2003 to 1.84 in 2010. Applications A Nest learning thermostat reporting on energy usage and local weather. The extensive set of applications for IoT devices is often divided into consumer, commercial, industrial, and infrastructure spaces. Consumer applications A growing portion of IoT devices are created for consumer use, including connected vehicles, home automation, wearable technology, connected health, and appliances with remote monitoring capabilities. Smart home IoT devices are a part of the larger concept of home automation, which can include lighting, heating and air conditioning, media and security systems. Long term benefits could include energy savings by automatically ensuring lights and electronics are turned off. A smart home or automated home could be based on a platform or hubs that control smart devices and appliances. For instance, using Apple 's HomeKit, manufacturers can get their home products and accessories be controlled by an application in iOS devices such as the iPhone and the Apple Watch. This could be a dedicated app or iOS native applications such as Siri. This can be demonstrated in the case of Lenovo 's Smart Home Essentials, which is a line of smart home devices that are controlled through Apple 's Home app or Siri without the need for a Wi - Fi bridge. There are also dedicated smart home hubs that are offered as standalone platforms to connect different smart home products and these include the Amazon Echo, Apple 's HomePod, and Samsung 's SmartThings Hub. Elder care One key application of smart home is to provide assistance for those with disabilities and elderly individuals. These home systems use assistive technology to accommodate an owner 's specific disabilities. Voice control can assist users with sight and mobility limitations while alert systems can be connected directly to cochlear implants worn by hearing impaired users. They can also be equipped with additional safety features. These features can include sensors that monitor for medical emergencies such as falls or seizures. Smart home technology applied in this way can provide users with more freedom and a higher quality of life. The term `` Enterprise IoT '' refers to devices used in business and corporate settings. By 2019, it is estimated that EIoT will account for 9.1 billion devices. Commercial applications Medical and healthcare The Internet of Medical Things ( also called the internet of health things ) is an application of the IoT for medical and health related purposes, data collection and analysis for research, and monitoring. This ' Smart Healthcare ', as it can also be called, led to the creation of a digitized healthcare system, connecting available medical resources and healthcare services. IoT devices can be used to enable remote health monitoring and emergency notification systems. These health monitoring devices can range from blood pressure and heart rate monitors to advanced devices capable of monitoring specialized implants, such as pacemakers, Fitbit electronic wristbands, or advanced hearing aids. Some hospitals have begun implementing `` smart beds '' that can detect when they are occupied and when a patient is attempting to get up. It can also adjust itself to ensure appropriate pressure and support is applied to the patient without the manual interaction of nurses. A 2015 Goldman Sachs report indicated that healthcare IoT devices `` can save the United States more than $300 billion in annual healthcare expenditures by increasing revenue and decreasing cost. '' Moreover, the use of mobile devices to support medical follow - up led to the creation of ' m - health ', used `` to analyze, capture, transmit and store health statistics from multiple resources, including sensors and other biomedical acquisition systems ''. Specialized sensors can also be equipped within living spaces to monitor the health and general well - being of senior citizens, while also ensuring that proper treatment is being administered and assisting people regain lost mobility via therapy as well. These sensors create a network of intelligent sensors that are able to collect, process, transfer and analyse valuable information in different environments, such as connecting in - home monitoring devices to hospital - based systems. Other consumer devices to encourage healthy living, such as connected scales or wearable heart monitors, are also a possibility with the IoT. End - to - end health monitoring IoT platforms are also available for antenatal and chronic patients, helping one manage health vitals and recurring medication requirements. As of 2018 IoMT was not only being applied in the clinical laboratory industry, but also in the healthcare and health insurance industries. IoMT in the healthcare industry is now permitting doctors, patients and others involved ( i.e. guardians of patients, nurses, families, etc. ) to be part of a system, where patient records are saved in a database, allowing doctors and the rest of the medical staff to have access to the patient 's information. Moreover, IoT - based systems are patient - centered, which involves being flexible to the patient 's medical conditions. IoMT in the insurance industry provides access to better and new types of dynamic information. This includes sensor - based solutions such as biosensors, wearables, connected health devices and mobile apps to track customer behaviour. This can lead to more accurate underwriting and new pricing models. Transportation Digital variable speed - limit sign. The IoT can assist in the integration of communications, control, and information processing across various transportation systems. Application of the IoT extends to all aspects of transportation systems ( i.e. the vehicle, the infrastructure, and the driver or user ). Dynamic interaction between these components of a transport system enables inter and intra vehicular communication, smart traffic control, smart parking, electronic toll collection systems, logistic and fleet management, vehicle control, and safety and road assistance. In Logistics and Fleet Management for example, The IoT platform can continuously monitor the location and conditions of cargo and assets via wireless sensors and send specific alerts when management exceptions occur ( delays, damages, thefts, etc. ). If combined with Machine Learning then it also helps in reducing traffic accidents by introducing drowsiness alerts to drivers and providing self driven cars too. Building and home automation IoT devices can be used to monitor and control the mechanical, electrical and electronic systems used in various types of buildings ( e.g., public and private, industrial, institutions, or residential ) in home automation and building automation systems. In this context, three main areas are being covered in literature : The integration of the Internet with building energy management systems in order to create energy efficient and IOT driven `` smart buildings ''. The possible means of real - time monitoring for reducing energy consumption and monitoring occupant behaviors. The integration of smart devices in the built environment and how they might to know who to be used in future applications. Industrial applications Manufacturing The IoT can realize the seamless integration of various manufacturing devices equipped with sensing, identification, processing, communication, actuation, and networking capabilities. Based on such a highly integrated smart cyberphysical space, it opens the door to create whole new business and market opportunities for manufacturing. Network control and management of manufacturing equipment, asset and situation management, or manufacturing process control bring the IoT within the realm of industrial applications and smart manufacturing as well. The IoT intelligent systems enable rapid manufacturing of new products, dynamic response to product demands, and real - time optimization of manufacturing production and supply chain networks, by networking machinery, sensors and control systems together. Digital control systems to automate process controls, operator tools and service information systems to optimize plant safety and security are within the purview of the IoT. But it also extends itself to asset management via predictive maintenance, statistical evaluation, and measurements to maximize reliability. Smart industrial management systems can also be integrated with the Smart Grid, thereby enabling real - time energy optimization. Measurements, automated controls, plant optimization, health and safety management, and other functions are provided by a large number of networked sensors. The term industrial Internet of things ( IIoT ) is often encountered in the manufacturing industries, referring to the industrial subset of the IoT. IIoT in manufacturing could generate so much business value that it will eventually lead to the fourth industrial revolution, so the so - called Industry 4.0. It is estimated that in the future, successful companies will be able to increase their revenue through Internet of things by creating new business models and improve productivity, exploit analytics for innovation, and transform workforce. The potential of growth by implementing IIoT may generate $12 trillion of global GDP by 2030. Design architecture of cyber-physical systems - enabled manufacturing system While connectivity and data acquisition are imperative for IIoT, they should not be the purpose, rather the foundation and path to something bigger. Among all the technologies, predictive maintenance is probably a relatively `` easier win '' since it is applicable to existing assets and management systems. The objective of intelligent maintenance systems is to reduce unexpected downtime and increase productivity. And to realize that alone would generate around up to 30 % over the total maintenance costs. Industrial big data analytics will play a vital role in manufacturing asset predictive maintenance, although that is not the only capability of industrial big data. Cyber-physical systems ( CPS ) is the core technology of industrial big data and it will be an interface between human and the cyber world. Cyber-physical systems can be designed by following the 5C ( connection, conversion, cyber, cognition, configuration ) architecture, and it will transform the collected data into actionable information, and eventually interfere with the physical assets to optimize processes. An IoT - enabled intelligent system of such cases was proposed in 2001 and later demonstrated in 2014 by the National Science Foundation Industry / University Collaborative Research Center for Intelligent Maintenance Systems ( IMS ) at the University of Cincinnati on a bandsaw machine in IMTS 2014 in Chicago. Bandsaw machines are not necessarily expensive, but the bandsaw belt expenses are enormous since they degrade much faster. However, without sensing and intelligent analytics, it can be only determined by experience when the band saw belt will actually break. The developed prognostics system will be able to recognize and monitor the degradation of band saw belts even if the condition is changing, advising users when is the best time to replace the belt. This will significantly improve user experience and operator safety and ultimately save on costs. Agriculture There are numerous IoT applications in farming such as collecting data on temperature, rainfall, humidity, wind speed, pest infestation, and soil content. This data can be used to automate farming techniques, take informed decisions to improve quality and quantity, minimize risk and waste, and reduce effort required to manage crops. For example, farmers can now monitor soil temperature and moisture from afar, and even apply IoT - acquired data to precision fertilization programs. In August 2018, Toyota Tsusho began a partnership with Microsoft to create fish farming tools using the Microsoft Azure application suite for IoT technologies related to water management. Developed in part by researchers from Kindai University, the water pump mechanisms use artificial intelligence to count the number of fish on a conveyor belt, analyze the number of fish, and deduce the effectiveness of water flow from the data the fish provide. The specific computer programs used in the process fall under the Azure Machine Learning and the Azure IoT Hub platforms. Infrastructure applications Monitoring and controlling operations of sustainable urban and rural infrastructures like bridges, railway tracks and on - and offshore wind - farms is a key application of the IoT. The IoT infrastructure can be used for monitoring any events or changes in structural conditions that can compromise safety and increase risk. IoT can benefit the construction industry by cost saving, time reduction, better quality workday, paperless workflow and increase in productivity. It can help in taking faster decisions and save money with Real - Time Data Analytics. It can also be used for scheduling repair and maintenance activities in an efficient manner, by coordinating tasks between different service providers and users of these facilities. IoT devices can also be used to control critical infrastructure like bridges to provide access to ships. Usage of IoT devices for monitoring and operating infrastructure is likely to improve incident management and emergency response coordination, and quality of service, up - times and reduce costs of operation in all infrastructure related areas. Even areas such as waste management can benefit from automation and optimization that could be brought in by the IoT. Metropolitan scale deployments There are several planned or ongoing large - scale deployments of the IoT, to enable better management of cities and systems. For example, Songdo, South Korea, the first of its kind fully equipped and wired smart city, is gradually being built, with approximately 70 percent of the business district completed as of June 2018. Much of the city is planned to be wired and automated, with little or no human intervention. Another application is a currently undergoing project in Santander, Spain. For this deployment, two approaches have been adopted. This city of 180,000 inhabitants has already seen 18,000 downloads of its city smartphone app. The app is connected to 10,000 sensors that enable services like parking search, environmental monitoring, digital city agenda, and more. City context information is used in this deployment so as to benefit merchants through a spark deals mechanism based on city behavior that aims at maximizing the impact of each notification. Other examples of large - scale deployments underway include the Sino - Singapore Guangzhou Knowledge City ; work on improving air and water quality, reducing noise pollution, and increasing transportation efficiency in San Jose, California ; and smart traffic management in western Singapore. French company, Sigfox, commenced building an ultra-narrowband wireless data network in the San Francisco Bay Area in 2014, the first business to achieve such a deployment in the U.S. It subsequently announced it would set up a total of 4000 base stations to cover a total of 30 cities in the U.S. by the end of 2016, making it the largest IoT network coverage provider in the country thus far. Another example of a large deployment is the one completed by New York Waterways in New York City to connect all the city 's vessels and be able to monitor them live 24 / 7. The network was designed and engineered by Fluidmesh Networks, a Chicago - based company developing wireless networks for critical applications. The NYWW network is currently providing coverage on the Hudson River, East River, and Upper New York Bay. With the wireless network in place, NY Waterway is able to take control of its fleet and passengers in a way that was not previously possible. New applications can include security, energy and fleet management, digital signage, public Wi - Fi, paperless ticketing and others. Energy management Significant numbers of energy - consuming devices ( e.g. switches, power outlets, bulbs, televisions, etc. ) already integrate Internet connectivity, which can allow them to communicate with utilities to balance power generation and energy usage and optimize energy consumption as a whole. These devices allow for remote control by users, or central management via a cloud - based interface, and enable functions like scheduling ( e.g., remotely powering on or off heating systems, controlling ovens, changing lighting conditions etc. ). The smart grid is a utility - side IoT application ; systems gather and act on energy and power - related information to improve the efficiency of the production and distribution of electricity. Using advanced metering infrastructure ( AMI ) Internet - connected devices, electric utilities not only collect data from end - users, but also manage distribution automation devices like transformers. Environmental monitoring Environmental monitoring applications of the IoT typically use sensors to assist in environmental protection by monitoring air or water quality, atmospheric or soil conditions, and can even include areas like monitoring the movements of wildlife and their habitats. Development of resource - constrained devices connected to the Internet also means that other applications like earthquake or tsunami early - warning systems can also be used by emergency services to provide more effective aid. IoT devices in this application typically span a large geographic area and can also be mobile. It has been argued that the standardization IoT brings to wireless sensing will revolutionize this area. Trends and characteristics Technology roadmap : Internet of things. The IoT 's major significant trend in recent years is the explosive growth of devices connected and controlled by the Internet. The wide range of applications for IoT technology mean that the specifics can be very different from one device to the next but there are basic characteristics shared by most. IoT creates opportunities for more direct integration of the physical world into computer - based systems, resulting in efficiency improvements, economic benefits, and reduced human exertions. The number of IoT devices increased 31 % year - over-year to 8.4 billion in the year 2017 and it is estimated that there will be 30 billion devices by 2020. The global market value of IoT is projected to reach $7.1 trillion by 2020. Intelligence Ambient intelligence and autonomous control are not part of the original concept of the Internet of things. Ambient intelligence and autonomous control do not necessarily require Internet structures, either. However, there is a shift in research ( by companies such as Intel ) to integrate the concepts of IoT and autonomous control, with initial outcomes towards this direction considering objects as the driving force for autonomous IoT. In the future, the Internet of Things may be a non-deterministic and open network in which auto - organized or intelligent entities ( web services, SOA components ) and virtual objects ( avatars ) will be interoperable and able to act independently ( pursuing their own objectives or shared ones ) depending on the context, circumstances or environments. Autonomous behavior through the collection and reasoning of context information as well as the object 's ability to detect changes in the environment ( faults affecting sensors ) and introduce suitable mitigation measures constitutes a major research trend, clearly needed to provide credibility to the IoT technology. Modern IoT products and solutions in the marketplace use a variety of different technologies to support such context - aware automation, but more sophisticated forms of intelligence are requested to permit sensor units and intelligent cyber-physical systems to be deployed in real environments. Architecture This section needs attention from an expert in Technology. The specific problem is : The information is partially outdated, unclear, and uncited. Requires more details, but not so technical that others wo n't understand it... WikiProject Technology may be able to help recruit an expert. ( July 2018 ) IIoT system architecture, in its simplistic view, consists of three tiers : Tier 1 : devices, Tier 2 : the Edge Gateway, and Tier 3 : the cloud Tier 1 of the IIoT architecture consists of networked things, typically sensors and actuators, from the IIoT equipment, which use protocols such as Modbus, Zigbee, or proprietary protocols, to connect to an Edge Gateway. Tier 2 includes sensor data aggregation systems called Edge Gateways that provide functionality, such as pre-processing of the data, securing connectivity to cloud, using systems such as WebSockets, the event hub, and, even in some cases, edge analytics or fog computing. Tier 3 includes the cloud application built for IIoT using the microservices architecture, which are usually polyglot and inherently secure in nature using HTTPS / OAuth. Tier 3 also includes storage of sensor data using various database systems, such as time series databases or asset stores using backend data storage systems such as Cassandra or Postgres. In addition to the data storage, we analyze the data using various analytics, predictive or threshold - based or regression - based, to get more insights on the IIoT equipment. Building on the Internet of things, the web of things is an architecture for the application layer of the Internet of things looking at the convergence of data from IoT devices into Web applications to create innovative use - cases. In order to program and control the flow of information in the Internet of things, a predicted architectural direction is being called BPM Everywhere which is a blending of traditional process management with process mining and special capabilities to automate the control of large numbers of coordinated devices. Network architecture The Internet of things requires huge scalability in the network space to handle the surge of devices. IETF 6LoWPAN would be used to connect devices to IP networks. With billions of devices being added to the Internet space, IPv6 will play a major role in handling the network layer scalability. IETF 's Constrained Application Protocol, ZeroMQ, and MQTT would provide lightweight data transport. Fog computing is a viable alternative to prevent such large burst of data flow through Internet. The edge devices ' computation power can be used to analyse and process data, thus providing easy real time scalability. Complexity In semi-open or closed loops ( i.e. value chains, whenever a global finality can be settled ) IoT will often be considered and studied as a complex system due to the huge number of different links, interactions between autonomous actors, and its capacity to integrate new actors. At the overall stage ( full open loop ) it will likely be seen as a chaotic environment ( since systems always have finality ). As a practical approach, not all elements in the Internet of things run in a global, public space. Subsystems are often implemented to mitigate the risks of privacy, control and reliability. For example, domestic robotics ( domotics ) running inside a smart home might only share data within and be available via a local network. Managing and controlling high dynamic ad hoc IoT things / devices network is a tough task with the traditional networks architecture, Software Defined Networking ( SDN ) provides the agile dynamic solution that can cope with the special requirements of the diversity of innovative IoT applications. Size Considerations The Internet of things would encode 50 to 100 trillion objects, and be able to follow the movement of those objects. Human beings in surveyed urban environments are each surrounded by 1000 to 5000 trackable objects. In 2015 there were already 83 million smart devices in people ` s homes. This number is about to grow up to 193 million devices in 2020 and will for sure go on growing in the near future. The figure of online capable devices grew 31 % from 2016 to 8.4 billion in 2017. Space Considerations In the Internet of things, the precise geographic location of a thing -- and also the precise geographic dimensions of a thing -- will be critical. Therefore, facts about a thing, such as its location in time and space, have been less critical to track because the person processing the information can decide whether or not that information was important to the action being taken, and if so, add the missing information ( or decide to not take the action ). ( Note that some things in the Internet of things will be sensors, and sensor location is usually important. ) The GeoWeb and Digital Earth are promising applications that become possible when things can become organized and connected by location. However, the challenges that remain include the constraints of variable spatial scales, the need to handle massive amounts of data, and an indexing for fast search and neighbor operations. In the Internet of things, if things are able to take actions on their own initiative, this human - centric mediation role is eliminated. Thus, the time - space context that we as humans take for granted must be given a central role in this information ecosystem. Just as standards play a key role in the Internet and the Web, geospatial standards will play a key role in the Internet of things. A solution to `` basket of remotes '' Many IoT devices have a potential to take a piece of this market. Jean - Louis Gassée ( Apple initial alumni team, and BeOS co-founder ) has addressed this topic in an article on Monday Note, where he predicts that the most likely problem will be what he calls the `` basket of remotes '' problem, where we 'll have hundreds of applications to interface with hundreds of devices that do n't share protocols for speaking with one another. For improved user interaction, some technology leaders are joining forces to create standards for communication between devices to solve this problem. Others are turning to the concept of predictive interaction of devices, `` where collected data is used to predict and trigger actions on the specific devices '' while making them work together. Frameworks IoT frameworks might help support the interaction between `` things '' and allow for more complex structures like distributed computing and the development of distributed applications. Currently, some IoT frameworks seem to focus on real - time data logging solutions, offering some basis to work with many `` things '' and have them interact. Future developments might lead to specific software - development environments to create the software to work with the hardware used in the Internet of things. Companies are developing technology platforms to provide this type of functionality for the Internet of things. Newer platforms are being developed, which add more intelligence. REST is a scalable architecture that allows things to communicate over Hypertext Transfer Protocol and is easily adopted for IoT applications to provide communication from a thing to a central web server. Enabling technologies for IoT There are many technologies that enable IoT. Crucial to the field is the network used to communicate between devices of an IoT installation, a role that several wireless or wired technologies may fulfill : Addressability The original idea of the Auto - ID Center is based on RFID - tags and unique identification through the Electronic Product Code, however, this has evolved into objects having an IP address or URI. An alternative view, from the world of the Semantic Web focuses instead on making all things ( not just those electronic, smart, or RFID - enabled ) addressable by the existing naming protocols, such as URI. The objects themselves do not converse, but they may now be referred to by other agents, such as powerful centralized servers acting for their human owners. Integration with the Internet implies that devices will use an IP address as a unique identifier. Due to the limited address space of IPv4 ( which allows for 4.3 billion unique addresses ), objects in the IoT will have to use the next generation of the Internet protocol ( IPv6 ) to scale to the extremely large address space required. Internet - of - things devices additionally will benefit from the stateless address auto - configuration present in IPv6, as it reduces the configuration overhead on the hosts, and the IETF 6LoWPAN header compression. To a large extent, the future of the Internet of things will not be possible without the support of IPv6 ; and consequently, the global adoption of IPv6 in the coming years will be critical for the successful development of the IoT in the future. Short - range wireless Bluetooth mesh networking -- Specification providing a mesh networking variant to Bluetooth low energy ( BLE ) with increased number of nodes and standardized application layer ( Models ). Light - Fidelity ( Li - Fi ) -- Wireless communication technology similar to the Wi - Fi standard, but using visible light communication for increased bandwidth. Near - field communication ( NFC ) -- Communication protocols enabling two electronic devices to communicate within a 4 cm range. QR codes and barcodes -- Machine - readable optical tags that store information about the item to which they are attached. Radio - frequency identification ( RFID ) -- Technology using electromagnetic fields to read data stored in tags embedded in other items. Thread -- Network protocol based on the IEEE 802.15. 4 standard, similar to ZigBee, providing IPv6 addressing. Transport Layer Security -- Network security protocol. Wi - Fi -- technology for local area networking based on the IEEE 802.11 standard, where devices may communicate through a shared access point or directly between individual devices. Z - Wave -- Communication protocol providing short - range, low - latency data transfer at rates and power consumption lower than Wi - Fi. Used primarily for home automation. ZigBee -- Communication protocols for personal area networking based on the IEEE 802.15. 4 standard, providing low power consumption, low data rate, low cost, and high throughput. Medium - range wireless HaLow -- Variant of the Wi - Fi standard providing extended range for low - power communication at a lower data rate. LTE - Advanced -- High - speed communication specification for mobile networks. Provides enhancements to the LTE standard with extended coverage, higher throughput, and lower latency. Long - range wireless Low - power wide - area networking ( LPWAN ) -- Wireless networks designed to allow long - range communication at a low data rate, reducing power and cost for transmission. Available LPWAN technologies and protocols : LoRaWan, Sigfox, NB - IoT, Weightless. Very small aperture terminal ( VSAT ) -- Satellite communication technology using small dish antennas for narrowband and broadband data. Long - range Wi - Fi connectivity Wired Ethernet -- General purpose networking standard using twisted pair and fiber optic links in conjunction with hubs or switches. Multimedia over Coax Alliance ( MoCA ) -- Specification enabling whole - home distribution of high definition video and content over existing coaxial cabling. Power - line communication ( PLC ) -- Communication technology using electrical wiring to carry power and data. Specifications such as HomePlug or G.hn utilize PLC for networking IoT devices. Standards and standards organizations This section needs expansion. You can help by adding to it. ( September 2016 ) This is a list of technical standards for the IoT, most of which are open standards, and the standards organizations that aspire to successfully setting them. Short name Long name Standards under development Other notes Auto - ID Labs Auto Identification Center Networked RFID ( radiofrequency identification ) and emerging sensing technologies EPCglobal Electronic Product code Technology Standards for adoption of EPC ( Electronic Product Code ) technology FDA U.S. Food and Drug Administration UDI ( Unique Device Identification ) system for unique identifiers for medical devices GS1 -- Standards for UIDs ( unique identifiers ) and RFID of fast - moving consumer goods ( consumer packaged goods ), health care supplies, and other things Parent organization comprises member organizations such as GS1 US IEEE Institute of Electrical and Electronics Engineers Underlying communication technology standards such as IEEE 802.15. 4 IETF Internet Engineering Task Force Standards that comprise TCP / IP ( the Internet protocol suite ) MTConnect Institute -- MTConnect is a manufacturing industry standard for data exchange with machine tools and related industrial equipment. It is important to the IIoT subset of the IoT. O - DF Open Data Format O - DF is a standard published by the Internet of Things Work Group of The Open Group in 2014, which specifies a generic information model structure that is meant to be applicable for describing any `` Thing '', as well as for publishing, updating and querying information when used together with O - MI ( Open Messaging Interface ). O - MI Open Messaging Interface O - MI is a standard published by the Internet of Things Work Group of The Open Group in 2014, which specifies a limited set of key operations needed in IoT systems, notably different kinds of subscription mechanisms based on the Observer pattern. OCF Open Connectivity Foundation Standards for simple devices using CoAP ( Constrained Application Protocol ) OCF ( Open Connectivity Foundation ) supersedes OIC ( Open Interconnect Consortium ) OMA Open Mobile Alliance OMA DM and OMA LWM2M for IoT device management, as well as GotAPI, which provides a secure framework for IoT applications XSF XMPP Standards Foundation Protocol extensions of XMPP ( Extensible Messaging and Presence Protocol ), the open standard of instant messaging Politics and civic engagement Some scholars and activists argue that the IoT can be used to create new models of civic engagement if device networks can be open to user control and inter-operable platforms. Philip N. Howard, a professor and author, writes that political life in both democracies and authoritarian regimes will be shaped by the way the IoT will be used for civic engagement. For that to happen, he argues that any connected device should be able to divulge a list of the `` ultimate beneficiaries '' of its sensor data and that individual citizens should be able to add new organizations to the beneficiary list. In addition, he argues that civil society groups need to start developing their IoT strategy for making use of data and engaging with the public. Government regulation on IoT One of the key drivers of the IoT is data. The success of the idea of connecting devices to make them more efficient is dependent upon access to and storage & processing of data. For this purpose, companies working on IoT collect data from multiple sources and store it in their cloud network for further processing. This leaves the door wide open for privacy and security dangers and single point vulnerability of multiple systems. The other issues pertain to consumer choice and ownership of data and how it is used. Though still in their infancy, regulations and governance regarding these issues of privacy, security, and data ownership continue to develop. IoT regulation depends on the country. Some examples of legislation that is relevant to privacy and data collection are : the US Privacy Act of 1974, OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data of 1980, and the EU Directive 95 / 46 / EC of 1995. Current regulatory environment : A report published by the Federal Trade Commission ( FTC ) in January 2015 made the following three recommendations : Data security -- At the time of designing IoT companies should ensure that data collection, storage and processing would be secure at all times. Companies should adopt a `` defence in depth '' approach and encrypt data at each stage. Data consent -- users should have a choice as to what data they share with IoT companies and the users must be informed if their data gets exposed. Data minimization -- IoT companies should collect only the data they need and retain the collected information only for a limited time. However, the FTC stopped at just making recommendations for now. According to an FTC analysis, the existing framework, consisting of the FTC Act, the Fair Credit Reporting Act, and the Children 's Online Privacy Protection Act, along with developing consumer education and business guidance, participation in multi-stakeholder efforts and advocacy to other agencies at the federal, state and local level, is sufficient to protect consumer rights. A resolution passed by the Senate in March 2015, is already being considered by the Congress. This resolution recognized the need for formulating a National Policy on IoT and the matter of privacy, security and spectrum. Furthermore, to provide an impetus to the IoT ecosystem, in March 2016, a bipartisan group of four Senators proposed a bill, The Developing Innovation and Growing the Internet of Things ( DIGIT ) Act, to direct the Federal Communications Commission to assess the need for more spectrum to connect IoT devices. Several standards for the IoT industry are actually being established relating to automobiles because most concerns arising from use of connected cars apply to healthcare devices as well. In fact, the National Highway Traffic Safety Administration ( NHTSA ) is preparing cybersecurity guidelines and a database of best practices to make automotive computer systems more secure. A recent report from the World Bank examines the challenges and opportunities in government adoption of IoT. These include - Still early days for IoT in government Underdeveloped policy and regulatory frameworks Unclear business models, despite strong value proposition Clear institutional and capacity gap in government AND the private sector Inconsistent data valuation and management Infrastructure a major barrier Government as an enabler Most successful pilots share common characteristics ( public - private partnership, local, leadership ) Criticism and controversies Platform fragmentation IoT suffers from platform fragmentation and lack of technical standards a situation where the variety of IoT devices, in terms of both hardware variations and differences in the software running on them, makes the task of developing applications that work consistently between different inconsistent technology ecosystems hard. Customers may be hesitant to bet their IoT future on a proprietary software or hardware devices that uses proprietary protocols that may fade or become difficult to customize and interconnect. IoT 's amorphous computing nature is also a problem for security, since patches to bugs found in the core operating system often do not reach users of older and lower - price devices. One set of researchers say that the failure of vendors to support older devices with patches and updates leaves more than 87 % of active Android devices vulnerable. Privacy, autonomy, and control Philip N. Howard, a professor and author, writes that the Internet of things offers immense potential for empowering citizens, making government transparent, and broadening information access. Howard cautions, however, that privacy threats are enormous, as is the potential for social control and political manipulation. Concerns about privacy have led many to consider the possibility that big data infrastructures such as the Internet of things and data mining are inherently incompatible with privacy. Writer Adam Greenfield claims that these technologies are not only an invasion of public space but are also being used to perpetuate normative behavior, citing an instance of billboards with hidden cameras that tracked the demographics of passersby who stopped to read the advertisement. The Internet of Things Council compared the increased prevalence of digital surveillance due to the Internet of things to the conceptual panopticon described by Jeremy Bentham in the 18th Century. The assertion was defended by the works of French philosophers Michel Foucault and Gilles Deleuze. In Discipline and Punish : The Birth of the Prison Foucault asserts that the panopticon was a central element of the discipline society developed during the Industrial Era. Foucault also argued that the discipline systems established in factories and school reflected Bentham 's vision of panopticism. In his 1992 paper `` Postscripts on the Societies of Control, '' Deleuze wrote that the discipline society had transitioned into a control society, with the computer replacing the panopticon as an instrument of discipline and control while still maintaining the qualities similar to that of panopticism. The privacy of households could be compromised by solely analyzing smart home network traffic patterns without dissecting the contents of encrypted application data, yet a synthetic packet injection scheme can be used to safely overcome such invasion of privacy. Peter - Paul Verbeek, a professor of philosophy of technology at the University of Twente, Netherlands, writes that technology already influences our moral decision making, which in turn affects human agency, privacy and autonomy. He cautions against viewing technology merely as a human tool and advocates instead to consider it as an active agent. Justin Brookman, of the Center for Democracy and Technology, expressed concern regarding the impact of IoT on consumer privacy, saying that `` There are some people in the commercial space who say, ' Oh, big data -- well, let 's collect everything, keep it around forever, we 'll pay for somebody to think about security later. ' The question is whether we want to have some sort of policy framework in place to limit that. '' Tim O'Reilly believes that the way companies sell the IoT devices on consumers are misplaced, disputing the notion that the IoT is about gaining efficiency from putting all kinds of devices online and postulating that `` IoT is really about human augmentation. The applications are profoundly different when you have sensors and data driving the decision - making. '' Editorials at WIRED have also expressed concern, one stating `` What you 're about to lose is your privacy. Actually, it 's worse than that. You are n't just going to lose your privacy, you 're going to have to watch the very concept of privacy be rewritten under your nose. '' The American Civil Liberties Union ( ACLU ) expressed concern regarding the ability of IoT to erode people 's control over their own lives. The ACLU wrote that `` There 's simply no way to forecast how these immense powers -- disproportionately accumulating in the hands of corporations seeking financial advantage and governments craving ever more control -- will be used. Chances are big data and the Internet of things will make it harder for us to control our own lives, as we grow increasingly transparent to powerful corporations and government institutions that are becoming more opaque to us. '' In response to rising concerns about privacy and smart technology, in 2007 the British Government stated it would follow formal Privacy by Design principles when implementing their smart metering program. The program would lead to replacement of traditional power meters with smart power meters, which could track and manage energy usage more accurately. However the British Computer Society is doubtful these principles were ever actually implemented. In 2009 the Dutch Parliament rejected a similar smart metering program, basing their decision on privacy concerns. The Dutch program later revised and passed in 2011. Data storage A challenge for producers of IoT applications is to clean, process and interpret the vast amount of data which is gathered by the sensors. There is a solution proposed for the analytics of the information referred to as Wireless Sensor Networks. These networks share data among sensor nodes that are sent to a distributed system for the analytics of the sensory data. Another challenge is the storage of this bulk data. Depending on the application, there could be high data acquisition requirements, which in turn lead to high storage requirements. Currently the Internet is already responsible for 5 % of the total energy generated, and a `` daunting challenge to power '' IoT devices to collect and even store data still remains. Security Concerns have been raised that the IoT is being developed rapidly without appropriate consideration of the profound security challenges involved and the regulatory changes that might be necessary. Most of the technical security concerns are similar to those of conventional servers, workstations and smartphones, but security challenges unique to the IoT continue to develop, including industrial security controls, hybrid systems, IoT - specific business processes, and end nodes. Security is the biggest concern in adopting Internet of things technology. In particular, as the Internet of things spreads widely, cyber attacks are likely to become an increasingly physical ( rather than simply virtual ) threat. In a January 2014 article in Forbes, cyber-security columnist Joseph Steinberg listed many Internet - connected appliances that can already `` spy on people in their own homes '' including televisions, kitchen appliances, cameras, and thermostats. Computer - controlled devices in automobiles such as brakes, engine, locks, hood and trunk releases, horn, heat, and dashboard have been shown to be vulnerable to attackers who have access to the on - board network. In some cases, vehicle computer systems are Internet - connected, allowing them to be exploited remotely. By 2008 security researchers had shown the ability to remotely control pacemakers without authority. Later hackers demonstrated remote control of insulin pumps and implantable cardioverter defibrillators. The U.S. National Intelligence Council in an unclassified report maintains that it would be hard to deny `` access to networks of sensors and remotely - controlled objects by enemies of the United States, criminals, and mischief makers... An open market for aggregated sensor data could serve the interests of commerce and security no less than it helps criminals and spies identify vulnerable targets. Thus, massively parallel sensor fusion may undermine social cohesion, if it proves to be fundamentally incompatible with Fourth - Amendment guarantees against unreasonable search. '' In general, the intelligence community views the Internet of things as a rich source of data. In 2016, a distributed denial of service attack powered by Internet of things devices running the Mirai malware took down a DNS provider and major web sites. The Mirai Botnet had infected roughly 65,000 IoT devices within the first 20 hours. Eventually the infections increased to 200,000 to 300,000 infections. Brazil, Columbia and Vietnam made up of 41.5 % of the infections. The Mirai Botnet had singled out specific IoT devices that consisted of DVRs, IP cameras, routers and printers. Top vendors that contained the most infected devices were identified as Dahua, Huawei, ZTE, Cisco, ZyXEL and MikroTik. In May 2017, Junade Ali, a Computer Scientist at Cloudflare noted that native DDoS vulnerabilities exist in IoT devices due to a poor implementation of the Publish -- subscribe pattern. These sorts of attacks have caused security experts to view IoT as a real threat to Internet services. There have been a range of responces to concerns over security. The Internet of Things Security Foundation ( IoTSF ) was launched on 23 September 2015 with a mission to secure the Internet of things by promoting knowledge and best practice. Its founding board is made from technology providers and telecommunications companies. In addition, large IT companies are continuously developing innovative solutions to ensure the security for IoT devices. In 2017, Mozilla launched Project Things, which allows to route IoT devices through a safe Web of Things gateway.As per the estimates from KBV Research, the overall IoT security market would grow at 27.9 % rate during 2016 -- 2022 as a result of growing infrastructural concerns and diversified usage of Internet of things. Governmental regulation is argued by some to be necessary to secure IoT devices and the wider Internet - as market incentives to secure IoT devices is insufficient. Safety IoT systems are typically controlled by event - driven smart apps that take as input either sensed data, user inputs, or other external triggers ( from the Internet ) and command one or more actuators towards providing different forms of automation. Examples of sensors include smoke detectors, motion sensors, and contact sensors. Examples of actuators include smart locks, smart power outlets, and door controls. Popular control platforms on which third - party developers can build smart apps that interact wirelessly with these sensors and actuators include Samsung 's SmartThings, Apple 's HomeKit, and Amazon 's Alexa, among others. A problem specific to IoT systems is that buggy apps, unforeseen bad app interactions, or device / communication failures, can cause unsafe and dangerous physical states, e.g., `` unlock the entrance door when no one is at home '' or `` turn off the heater when the temperature is below 0 degrees Celcius and people are sleeping at night ''. Detecting flaws that lead to such states, requires a holistic view of installed apps, component devices, their configurations, and more importantly, how they interact. Recently, researchers from the University of California Riverside have proposed IotSan, a novel practical system that uses model checking as a building block to reveal `` interaction - level '' flaws by identifying events that can lead the system to unsafe states. They have evaluated IotSan on the Samsung SmartThings platform. From 76 manually configured systems, IotSan detects 147 vulnerabilities ( i.e., violations of safe physical states / properties ). Design Given widespread recognition of the evolving nature of the design and management of the Internet of things, sustainable and secure deployment of IoT solutions must design for `` anarchic scalability. '' Application of the concept of anarchic scalability can be extended to physical systems ( i.e. controlled real - world objects ), by virtue of those systems being designed to account for uncertain management futures. This hard anarchic scalability thus provides a pathway forward to fully realize the potential of Internet - of - things solutions by selectively constraining physical systems to allow for all management regimes without risking physical failure. Brown University computer scientist Michael Littman has argued that successful execution of the Internet of things requires consideration of the interface 's usability as well as the technology itself. These interfaces need to be not only more user - friendly but also better integrated : `` If users need to learn different interfaces for their vacuums, their locks, their sprinklers, their lights, and their coffeemakers, it 's tough to say that their lives have been made any easier. '' Environmental sustainability impact A concern regarding Internet - of - things technologies pertains to the environmental impacts of the manufacture, use, and eventual disposal of all these semiconductor - rich devices. Modern electronics are replete with a wide variety of heavy metals and rare - earth metals, as well as highly toxic synthetic chemicals. This makes them extremely difficult to properly recycle. Electronic components are often incinerated or placed in regular landfills. Furthermore, the human and environmental cost of mining the rare - earth metals that are integral to modern electronic components continues to grow. This leads to societal questions concerning the environmental impacts of IoT devices over its lifetime. Intentional obsolescence of devices The Electronic Frontier Foundation has raised concerns that companies can use the technologies necessary to support connected devices to intentionally disable or `` brick '' their customers ' devices via a remote software update or by disabling a service necessary to the operation of the device. In one example, home automation devices sold with the promise of a `` Lifetime Subscription '' were rendered useless after Nest Labs acquired Revolv and made the decision to shut down the central servers the Revolv devices had used to operate. As Nest is a company owned by Alphabet ( Google 's parent company ), the EFF argues this sets a `` terrible precedent for a company with ambitions to sell self - driving cars, medical devices, and other high - end gadgets that may be essential to a person 's livelihood or physical safety. '' Owners should be free to point their devices to a different server or collaborate on improved software. But such action violates the United States DMCA section 1201, which only has an exemption for `` local use ''. This forces tinkerers who want to keep using their own equipment into a legal grey area. EFF thinks buyers should refuse electronics and software that prioritize the manufacturer 's wishes above their own. Examples of post-sale manipulations include Google Nest Revolv, disabled privacy settings on Android, Sony disabling Linux on PlayStation 3, enforced EULA on Wii U. Confusing terminology Kevin Lonergan at Information Age, a business - technology magazine, has referred to the terms surrounding IoT as a `` terminology zoo ''. The lack of clear terminology is not `` useful from a practical point of view '' and a `` source of confusion for the end user ''. A company operating in the IoT space could be working in anything related to sensor technology, networking, embedded systems, or analytics. According to Lonergan, the term IoT was coined before smart phones, tablets, and devices as we know them today existed, and there is a long list of terms with varying degrees of overlap and technological convergence : Internet of things, Internet of everything ( IoE ), Internet of Goods ( Supply Chain ), industrial Internet, pervasive computing, pervasive sensing, ubiquitous computing, cyber-physical systems ( CPS ), wireless sensor networks ( WSN ), smart objects, digital twin, cyberobjects or avatars, cooperating objects, machine to machine ( M2M ), ambient intelligence ( AmI ), Operational technology ( OT ), and information technology ( IT ). Regarding IIoT, an industrial sub-field of IoT, the Industrial Internet Consortium 's Vocabulary Task Group has created a `` common and reusable vocabulary of terms '' to ensure `` consistent terminology '' across publications issued by the Industrial Internet Consortium. IoT One has created an IoT Terms Database including a New Term Alert to be notified when a new term is published. As of March 2017, this database aggregates 711 IoT - related terms, while keeping material `` transparent and comprehensive. '' IoT adoption barriers GE Digital CEO William Ruh speaking about GE 's attempts to gain a foothold in the market for IoT services at the first IEEE Computer Society TechIgnite conference. Lack of interoperability and unclear value propositions Despite a shared belief in the potential of IoT, industry leaders and consumers are facing barriers to adopt IoT technology more widely. Mike Farley argued in Forbes that while IoT solutions appeal to early adopters, they either lack interoperability or a clear use case for end - users. A study by Ericsson regarding the adoption of IoT among Danish companies suggests that many struggle `` to pinpoint exactly where the value of IoT lies for them ''. Privacy and security concerns According to a recent study by Noura Aleisa and Karen Renaud at the University of Glasgow, `` the Internet of things ' potential for major privacy invasion is a concern '' with much of research `` disproportionally focused on the security concerns of IoT. '' Among the `` proposed solutions in terms of the techniques they deployed and the extent to which they satisfied core privacy principles '', only very few turned out to be fully satisfactory. Louis Basenese, investment director at Wall Street Daily, has criticized the industry 's lack of attention to security issues : `` Despite high - profile and alarming hacks, device manufacturers remain undeterred, focusing on profitability over security. Consumers need to have ultimate control over collected data, including the option to delete it if they choose... Without privacy assurances, wide - scale consumer adoption simply wo n't happen. '' In a post-Snowden world of global surveillance disclosures, consumers take a more active interest in protecting their privacy and demand IoT devices to be screened for potential security vulnerabilities and privacy violations before purchasing them. According to the 2016 Accenture Digital Consumer Survey, in which 28000 consumers in 28 countries were polled on their use of consumer technology, security `` has moved from being a nagging problem to a top barrier as consumers are now choosing to abandon IoT devices and services over security concerns. '' The survey revealed that `` out of the consumers aware of hacker attacks and owning or planning to own IoT devices in the next five years, 18 percent decided to terminate the use of the services and related services until they get safety guarantees. '' This suggests that consumers increasingly perceive privacy risks and security concerns to outweigh the value propositions of IoT devices and opt to postpone planned purchases or service subscriptions. Traditional governance structures Town of Internet of Things in Hangzhou, China A study issued by Ericsson regarding the adoption of Internet of things among Danish companies identified a `` clash between IoT and companies ' traditional governance structures, as IoT still presents both uncertainties and a lack of historical precedence. '' Among the respondents interviewed, 60 percent stated that they `` do not believe they have the organizational capabilities, and three of four do not believe they have the processes needed, to capture the IoT opportunity. '' This has led to a need to understand organizational culture in order to facilitate organizational design processes and to test new innovation management practices. A lack of digital leadership in the age of digital transformation has also stifled innovation and IoT adoption to a degree that many companies, in the face of uncertainty, `` were waiting for the market dynamics to play out '', or further action in regards to IoT `` was pending competitor moves, customer pull, or regulatory requirements. '' Some of these companies risk being ' kodaked ' -- `` Kodak was a market leader until digital disruption eclipsed film photography with digital photos '' -- failing to `` see the disruptive forces affecting their industry '' and `` to truly embrace the new business models the disruptive change opens up. '' Scott Anthony has written in Harvard Business Review that Kodak `` created a digital camera, invested in the technology, and even understood that photos would be shared online '' but ultimately failed to realize that `` online photo sharing was the new business, not just a way to expand the printing business. '' Business planning and models According to 2018 study, 70 -- 75 % of IoT deployments were stuck in the pilot or prototype stage, unable to reach scale due in part to a lack of business planning. Studies on IoT literature and projects show a disproportionate prominence of technology in the IoT projects, which are often driven by technological interventions rather than business model innovation. See also Home automation Web of things Smart grid Cyber-physical system Edge computing Cloud manufacturing Data Distribution Service Digital object memory Indoor positioning system Open Interconnect Consortium OpenWSN Industry 4.0 5G Digital twin References ^ Jump up to : Brown, Eric ( 13 September 2016 ). `` Who Needs the Internet of Things ? ''. Linux.com. Retrieved 23 October 2016. ^ Jump up to : Brown, Eric ( 20 September 2016 ). `` 21 Open Source Projects for IoT ''. Linux.com. Retrieved 23 October 2016. Jump up ^ `` Internet of Things Global Standards Initiative ''. ITU. 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Technology Innovation Management Review. 4 ( 7 ). ISSN 1927 - 0321. Bibliography Acharjya, D.P. ; Geetha, M.K., ed. ( 2017 ). Internet of Things : Novel Advances and Envisioned Applications. Springer. p. 311. ISBN 9783319534725. CS1 maint : Multiple names : editors list ( link ) Li, S. ; Xu, L.D., ed. ( 2017 ). Securing the Internet of Things. Syngress. p. 154. ISBN 9780128045053. CS1 maint : Multiple names : editors list ( link ) Rowland, C. ; Goodman, E. ; Charlier, M. ; et al., eds. ( 2015 ). Designing Connected Products : UX for the Consumer Internet of Things. O'Reilly Media. p. 726. ISBN 9781449372569. CS1 maint : Explicit use of et al. ( link ) CS1 maint : Multiple names : editors list ( link ) Thomas, Jayant ; Traukina, Alena ( 2018 ). Industrial Internet Application Development : Simplify IIoT development using the elasticity of Public Cloud and Native Cloud Services. Packt Publishing. p. 25. ISBN 978 - 1788298599. External links Wikimedia Commons has media related to Internet of things. Ambient intelligence Concepts Context awareness Internet of Things Object hyperlinking Profiling Spime Supranet Ubiquitous computing Web of Things Wireless sensor networks Technologies 6LoWPAN ANT+ DASH7 IEEE 802.15. 4 Internet 0 Machine to machine Radio - frequency identification Smartdust Tera - play XBee Platforms Arduino Contiki Electric Imp Gadgeteer ioBridge Netduino Raspberry Pi TinyOS Wiring Xively NodeMCU Applications Ambient device CeNSE Connected car Home automation HomeOS Internet refrigerator Nabaztag Smart city Smart TV Smarter Planet Pioneers Kevin Ashton Gaetano Borriello Adam Dunkels Stefano Marzano Don Norman Roel Pieper Josef Preishuber - Pflügl John Seely Brown Bruce Sterling Mark Weiser Other Ambient Devices AmbieSense Ebbits project IPSO Alliance Embedded systems General terms Embedded software Original equipment manufacturer ( OEM ) Embedded database Embedded hypervisor Consumer electronics Microcontroller ASIC FPGA SoC Memory footprint Single - board computer Raspberry pi IoT Board support package Cross compiler Embedded OS Bootloader Firmware and controls Custom firmware Rooting ( Android ) iOS jailbreaking PlayStation 3 Jailbreak Closed platform Vendor lock - in Defective by Design Hacking of consumer electronics Homebrew ( video games ) Crippleware Software libraries uClibc dietlibc Embedded GLIBC musl Development tools Bitbake Buildroot BusyBox Yocto Project Almquist shell Stand - alone shell OpenEmbedded Operating systems Linux on embedded systems Linux for mobile devices Light - weight Linux distribution Windows IoT Win CE Real - time operating system Programming languages Ada Assembly language CAPL Embedded C MISRA C nesC Embedded C++ Embedded Java Lightweight browsers Open - source computing hardware Open - source robotics GND : 7713781 - 4 Retrieved from `` https://en.wikipedia.org/w/index.php?title=Internet_of_things&oldid=866514904 '' Categories : Internet of things Ambient intelligence Emerging technologies Hidden categories : CS1 maint : Multiple names : authors list CS1 maint : Multiple names : editors list CS1 maint : Explicit use of et al. 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Question: when was the word internet of things coined
Answer: in 1999