Internet of Things (IoT)

The Internet of things (IoT) describes the network of physical objects, so known as, "things" — that are embedded with sensors, software, and other technologies that is used for the purpose of connecting and exchanging data with other devices and systems over the Internet.

Things have evolved due to the convergence of multiple technologies, real-time analytics, machine learning, ubiquitous computing, commodity sensors, and embedded systems. Traditional fields of embedded system, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of things. In the consumer market, IoT technology is most synonymous with products pertaining to the concept of the "smart home" including devices and appliances (such as lighting fixtures, thermostats, home security system and cameras, and other home appliances) that support one or more common ecosystems, and can be controlled via devices associated with that ecosystem, such as smartphones and smart speakers. The IoT can also be used in healthcare systems.

There are a number of serious concerns about dangers in the growth of the IoT, especially in the areas of privacy and security, and consequently industry and governmental moves to address these concerns have begun including the development of international standards.

 

The main concept of a network of smart devices was discussed as early as 1982, with a modified Coca-Cola vending machine becoming the first ARPANET-connected appliance, able to report its inventory and whether newly loaded drinks were cold or not. 

Organizational applications

Medical and healthcare

The Internet of Medical Things (IoMT) is an application of the IoT for medical and health related purposes, data collection and analysis for research, and monitoring. The IoMT has been referenced as "Smart Healthcare", as the technology for creating a digitized healthcare system, connecting available medical resources and healthcare services.

IoT devices can be used to enable remote health monitoring and emergency notification sysytem. 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." oreover, the use of mobile devices to support medical follow-up led to the creation of 'm-health', used analyzed health statistics."

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 analyze 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.

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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, such as guardians of patients, nurses, families, and similar, 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 patient 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 behavior. This can lead to more accurate underwriting and new pricing models.

The application of the IoT in healthcare plays a fundamental role in managing chronic diseases and in disease prevention and control. Remote monitoring is made possible through the connection of powerful wireless solutions. The connectivity enables health practitioners to capture patient's data and applying complex algorithms in health data analysis.

 

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 be used in future applications.

Industrial applications

 

Also known as IIoT, industrial IoT devices acquire and analyze data from connected equipment, operational technology (OT), locations, and people. Combined with operational technology (OT) monitoring devices, IIoT helps regulate and monitor industrial systems. Also, the same implementation can be carried out for automated record updates of asset placement in industrial storage units as the size of the assets can vary from a small screw to the whole motor spare part, and misplacement of such assets can cause a percentile loss of manpower time and money.

Manufacturing

The IoT can connect various manufacturing devices equipped with sensing, identification, processing, communication, actuation, and networking capabilities. Network control and management of manufacturing equipment, asset and situation management, or manufacturing process control allow IoT to be used for industrial applications and smart manufacturing. IoT intelligent systems enable rapid manufacturing and optimization of new products, and rapid response to product demands.

Digital Control  system automate process controls, operator tools and service information systems to optimize plant safety and security are within the purview of the IIoT. IoT can also be applied to asset management via predictive maintenance, stastistical evolution, and measurements to maximize reliability. Industrial management systems can be integrated with smart grid, enabling energy optimization. Measurements, automated controls, plant optimization, health and safety management, and other functions are provided by networked sensors.

In addition to general manufacturing, IoT is also used for processes in the industrialization of construction.

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. The overall goal is that data from sensors, coupled with the farmer’s knowledge and intuition about his or her farm, can help increase farm productivity, and also help reduce costs.

In August 2018, Toyata Tasuso began a partnership with Microsoft to create fish farming tools using the Microsoft Azura 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 conveyar belt, analyze the number of fish, and deduce the effectiveness of water flow from the data the fish provide. The Farm Beats project from Microsoft Research that uses TV white space to connect farms is also a part of the Azure Marketplace now.

Food[edit]

The utilisation of IoT-based applications for improving food supply chain activities has been extensively investigated in recent years.^[72] The RFID technology adoption in the grocery supply chain has led to the real-time visibility of stocks and its movement, automated proof of delivery, increased the efficiency in logistics of short shelf life products, environmental, livestock and cold chain monitoring, and effective traceability.^[73] Researchers at the Loughborough University based on IoT technology designed an innovative digital food waste tracking system which supported the decision making in real-time to combat and reduce the food waste issues in food manufacturing.^[72] They further developed a fully automated system based on image processing to track potato wastes in a potato packing factory.^[74] IoT is currently being deployed in the food industry to increase the food safety, improve the logistics, enhance the supply chain transparency and wastage reduction.

Maritime

IoT devices are in use monitoring the environments and systems of boats and yachts.^[76] Many pleasure boats are left unattended for days in summer, and months in winter so such devices provide valuable early alert of boat flooding, fire, and deep discharge of batteries. The use of global internet data networks such as Sigfox, combined with long-life batteries, and microelectronics allows the engine rooms, bilge, and batteries to be constantly monitored and reported to a connected Android & Apple applications for example.

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. The 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 services, 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 currently undergoing a 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.Using its RPMA (Random Phase Multiple Access) technology, San Diego-based Ingenu has built a nationwide public network for low-bandwidth data transmissions using the same unlicensed 2.4 gigahertz spectrum as Wi-Fi. Ingenu's "Machine Network" covers more than a third of the US population across 35 major cities including San Diego and Dallas. French company, Sigfox, commenced building an Ultra Narrowband wireless data network in the San Fransico Narrow 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. Cisco also participates in smart cities projects. Cisco has started deploying technologies for Smart Wi-Fi, Smart Safety & Security, Smart Lighting, Smart Parking, Smart Transports, Smart Bus Stops, Smart Kiosks, Remote Expert for Government Services (REGS) and Smart Education in the five km area in the city of Vijaywada.

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.

Internet of Battlefield Things

The Internet of Battlefield Things (IoBT) is a project initiated and executed by the U.S. Army Research Laboratory that focuses on the basic science related to the IoT that enhance the capabilities of Army soldiers. In 2017, ARL launched the Internet of Battlefield things Collaborative Research Alliance , establishing a working collaboration between industry, university, and Army researchers to advance the theoretical foundations of IoT technologies and their applications to Army operations.

Ocean of Things

The Ocean of Things project is a DARPA-led program designed to establish an Internet of Things across large ocean areas for the purposes of collecting, monitoring, and analyzing environmental and vessel activity data. The project entails the deployment of about 50,000 floats that house a passive sensor suite that autonomously detect and track military and commercial vessels as part of a cloud-based network.

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 the IoT and autonomous control, with initial outcomes towards this direction considering objects as the driving force for autonomous IoT. A promising approach in this context is deep reinforcement learning where most of IoT systems provide a dynamic and interactive environment. Training an agent (i.e., IoT device) to behave smartly in such an environment cannot be addressed by conventional machine learning algorithms such as supervised learning. By reinforcement learning approach, a learning agent can sense the environment's state (e.g., sensing home temperature), perform actions (e.g., turn HVAC on or off) and learn through the maximizing accumulated rewards it receives in long term.

IoT intelligence can be offered at three levels: IoT devices, Edge Nodes, and Cloud Computing. The need for intelligent control and decision at each level depends on the time sensitiveness of the IoT application. For example, an autonomous vehicle's camera needs to make real-time obstacle detection to avoid an accident. This fast decision making would not be possible through transferring data from the vehicle to cloud instances and return the predictions back to the vehicle. Instead, all the operation should be performed locally in the vehicle. Integrating advanced machine learning algorithms including deep learning into IoT devices is an active research area to make smart objects closer to reality. Moreover, it is possible to get the most value out of IoT deployments through analyzing IoT data, extracting hidden information, and predicting control decisions. A wide variety of machine learning techniques have been used in IoT domain ranging from traditional methods such as regression, support vector machine and random forest to advanced ones such as convolutional neural networks, LSTM, and variation autoencoder.

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 contex-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.

 

Network architecture

The Internet of things requires huge scalability in the network space to handle the surge of devices. IETF 6LoWPNwould be used to connect devices to IP networks. With billions of device. 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 a large burst of data flow through the Internet. The edge devices' computation power to analyse and process data is extremely limited. Limited processing power is a key attribute of IoT devices as their purpose is to supply data about physical objects while remaining autonomous. Heavy processing requirements use more battery power harming IoT's ability to operate. Scalability is easy because IoT devices simply supply data through the internet to a server with sufficient processing power.

Decentralized IoT

Decentralized Internet of Things or Decentralized IoT is a modified IoT. It utilizes Fog computing to handle and balance requests of connected IoT devices in order to reduce loading on the cloud servers, and improve responsiveness for latency-sensitive IoT applications like vital signs monitoring of patients, vehicle-to-vehicle communication of autonomous driving, and critical failure detection of industrial devices that may cause injury and loss of life. Implementation of fog computing in IoT is beneficial for efficient network resource utilization by distributing secondary importance computational process of data to the edge. There are two types of decentralization of IoT as per below:

How decentralized IoT differs from other IoT architecture

Original IoT is connected via a mesh network but led by a major head node (centralized controller). The head node decides everything including how a data being created, stored, and transmitted. In short, head node is the final decision maker of its entire IoT system. In contrast, Decentralized IoT breaks the original IoT system into smaller divisions. The head node authorizes partial decision making power to lower level sub-nodes under mutual agreed policy. The performance and flexibility is greatly improved especially for huge IoT systems with millions of nodes. Decentralized IoT is the product of Blockchain and the Original IoT which serves as the foundation of the Internet-of-Everything that revolutionized how devices connecting with each other.

Significance of Decentralized IoT

By leveraging lightweight blockchain into IoT, the problem with limited bandwidth and hashing capacity of battery-powered or wireless IoT devices was solved for enhanced cybersecurity, access control, web of trust, and system authentication. Decentralized IoT subsequently reduces the size of identity metadata in almost four times and security overhead up to five times. Another experiment shows that decentralized IoT is able to detect up to 99% accuracy even in the presence of 40% adversaries in federated machine learning. Decentralized IoT is no more a conceptual idea or solely a theory, as many blockchain-based decentralized IoT platforms have already being developed for real life applications.

Decentralized IoT Network Security

Cyberattack identification can be done fast and accurately through early detection and mitigation at the edge nodes with more efficient traffic monitoring and evaluation. The internet world is huge, it took longer and longer time transmitting data from an end to another. Hence, it is good to distribute important task to be processed in the middle points in between ends.

Decentralized IoT Sensor Data Storage

IoT sensor data stored nearer to the edge nodes for more responsive queries by reducing the data transmission frequency between IoT sensors and core servers at the cloud while maintaining privacy of users by eliminating the trust issues raising during the data exchange mediated by any third party node between cloud and users.

Complexity

In semi-open or closed loops (i.e. value chains, whenever a global finality can be settled) the 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 a 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 expected to grow to 193 million devices by 2020.

The figure of online capable devices grew 31% from 2016 to 2017 to reach 8.4 billion.

Space consideration

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 neighbour 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, geo-spatial standards will play a key role in the Internet of things.

A solution to "basket of remotes"

Many IoT devices have the potential to take a piece of this market. Jean-Luise Gassee (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 don'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.

Social Internet of Things

Social Internet of Things (SIoT) is a new kind of IoT that focuses the importance of social interaction and relationship between IoT devices. SIoT is a pattern of how cross-domain IoT devices enabling application to application communication and collaboration without human intervention in order to serve their owners with autonomous services, and this only can be realized when gained low-level architecture support from both IoT software and hardware engineering.

Social Network for IoT Devices (Not Human)

IoT defines a device with an identity like a citizen in a community, and connect them to the internet to provide services to its users. SIoT defines a social network for IoT devices only to interact with each other for different goals that to serve human.

How SIoT different from IoT?

SIoT is different from the original IoT in terms of the collaboration characteristics. IoT is passive, it was set to serve for dedicated purposes with existing IoT devices in predetermined system. SIoT is active, it was programmed and managed by AI to serve for unplanned purposes with mix and match of potential IoT devices from different systems that beneficial its users.