The digital twin is a digital replica of a living or non-living physical entity. Digital twin refers to a digital replica of potential and actual physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an object operates throughout its lifecycle. Definitions of digital twin technology emphasize two important characteristics. Firstly, each definition emphasizes the connection between the physical model and the corresponding virtual model. Secondly, this connection is established by generating real-time data using sensors to provide an evolving perspective into the current status of the physical object. The concept of the digital twin can be compared to other concepts such as cross-reality environments or co-spaces and mirror models, which aim to, by and large, synchronise part of the physical world with its cyber representation.

Disease Tracking use cases use unified reporting platforms to provides management with a holistic overview of population health conditions across multiple sites and over time. They can be deployed across the general population or for a specific population such as a factory's employees. Disease Tracking enables the remote monitoring and supervision of health conditions, as the integration of data inputs from multiple sources, such as IoT devices, applications, corporate policy, and government regulations. Platform typically integrate diagnostic inputs, such as temperature monitoring, with travel history and records from existing systems. Contact tracing may also be used in an epidemic. Dynamic indicators are displayed to inform management and regulators of potential health concerns. The main technologies used are IoT devices (data inputs), cloud-based platforms, APIs that enable integration with existing systems, and end user applications.

Driver performance monitoring uses sensors to measure driver safety, fuel efficiency, and compliance with traffic regulations. Eight indicators associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time are commonly used. Additional useful data can be acquired from the car diagnostic port, such as Accelerometer sensors and GPS modules. Driver behaviour and status can also be monitored. For example, cameras can detect instances of texting while driving, and smart hats can identify driver fatigue and issue warnings to advise the driver to rest.

Edge computing and edge intelligence shifts data processing, computing applications, and services away from centralized cloud based servers to the edges of a network. This enables analytics to occur at the source of the data where it can trigger events in real time, without time delays as data moved between cloud servers. Existing cloud-based technologies do not solve problems of data analytics, software deployment, or updates and security for remote devices. Edge or Fog Computing solves the problem of managing large amounts of machine-generated data by processing data at the edge of the network and converting it into actionable, useful business information. Software can be deployed at various points in the network to not only automate monitoring and control, but also to apply embedded intelligent agents that can adjust device behaviors in relation to ongoing performance variables, thereby reducing running costs by reducing power consumption during off-cycles, or even detecting imminent failures and notifying technicians to perform preventative maintenance. Edge Computing also allows remote software deployment and secure M2M communication. Edge Computing leverages resources that are not continuously connected to a network, such as laptops, smartphones, tablets, and sensors. It covers a wide range of technologies, from Wireless Sensor Networks and mobile data acquisition to cooperative distributed peer-to-peer ad hoc networking and processing.

Energy management systems (EMS) enable operators of electric utility grids to monitor, control, and optimize the performance of power generation, storage, and transmission. These systems collect energy measurement data from utility infrastructure and customers to leverage applications that determine the most cost-effective configuration of power production, transmission, and distribution throughout the network, considering the required criteria for system stability, safety, and reliability. An EMS typically provides the information and computation capability to perform real-time network analyses, to provide strategies for controlling system energy flows, and to determine the most economical mix of power generation, power purchases, and sales. Wide area management control is used in digital grids to provide real-time data for applications to provide context-specific insights. It collects data that are visually synchronized with GPS signals and maps, analyzes data based on key criterion, provides real-time evaluation of voltage stabilization and minimum load shutdown of the capital area, and forecasts system issues through real-time analysis. These analysis and information can be used to make decisions and control the system in real time, improving system liability and avoiding blackouts during faults.

Energy storage management systems store and distribute energy from different sources to be integrated for use when it is needed. This is especially useful for energy sources that cannot produce energy on demand, such as solar and wind energy. With energy storage management systems, the energy generated in the day will be made available to use at night, matching demand with supply and providing energy in a more sustainable manner. Energy storage management systems deployed in remote areas can also power local IoT devices without rechargable batteries by storaging and distributing power as needed, increasing the availbility of IoT devices.

Automated experimentation encompasses all aspects of the automation of clusters of high throughput experiments in the fields of biomedicine and chemistry. Automation provides opportunities to conduct traditional experiments more efficiently and provides an alternative to the traditional experimental method in situations in which experimental variables are too numerous or complex to control. The most widely used application of laboratory automation technology is robotics. However, automated experimentation includes a wide range of laboratory instruments, devices such as autosamplers, software algorithms, and methodologies used to expedite and increase the efficiency of scientific research in laboratories.

Facial recognition systems are capable of identifying or verifying a person from a digital image or a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database. It is also described as a biometric Artificial Intelligence based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. It has seen wider uses in recent times on mobile platforms, for access control, public safety, and as a payment method. Facial recognition can be compared to other Biometrics such as fingerprint or eye iris recognition systems.

Visualizing factory operations data is a challenge for many manufacturers today. One of the IIoT initiatives some manufacturers are pursuing today is providing real-time visibility in factory operations and the health of machines. The goal is to improve manufacturing efficiency. The challenge is in combining and correlating diverse data sources that greatly vary in nature, origin, and life cycle. Factory Operations Visibility and Intelligence (FOVI) is designed to collect sensor data generated on the factory floor, production-equipment logs, production plans and statistics, operator information, and to integrate all this and other related information in the cloud. In this way, it can be used to bring visibility to production facilities, analyze and predict outcomes, and support better decisions for improvements.

Farm monitoring and precision farming are farm management concepts that uses sensors, data from external systems, such as weather reports, and network communciation to tailor farming operations to the specific conditions of each field. Farmers generate data via sensors and analyze the information to evaluate current practices and make improvements for greater efficiency and effectiveness. There are a variety of smart farming applications including crop observation, agriculture vehicle Tracking, irrigation management, livestock management, and storage monitoring.

Fleet management is an administrative approach that allows companies to organize and coordinate work vehicles to improve efficiency, reduce costs, and provide compliance with government regulations. While most commonly used for vehicle tracking, fleet management includes other use cases such as mechanical diagnostics and driver behavior. Automated fleet management solutionsto connect vehicles and monitor driver activities, allowing managers to gain insight into fleet performance and driver behavior. This enables managers to know where vehicles and drivers are at all times, identify potential problems and mitigate risks before they become larger issues that can jeopardize client satisfaction, impact driver safety, or increase costs.

A flexible manufacturing system is a production method that is designed to easily adapt to changes in the type and quantity of the product being manufactured. Machines and computerized systems can be configured to manufacture a variety of parts and handle changing levels of production.

Fog computing refers to a decentralized computing structure, where resources, including the data and applications, get placed in logical locations between the data source and the cloud; it also is known by the terms fogging and fog networking. The goal of this is to bring basic analytic services to the network edge, improving performance by positioning computing resources closer to where they are needed, thereby reducing the distance that data needs to be transported on the network, improving overall network efficiency and performance. Fog Computing can also be deployed for security reasons, as it has the ability to segment bandwidth traffic and introduce additional firewalls to a network for higher security.

Food safety management aims to heighten and maintain product quality by monitoring storage conditions of food items and by increasing Traceability. As food storage is generally time and temperature sensitive, maintaining and checking storage conditions is essential to food safety. This includes temperature monitoring, condition checking and Tracking the movement of ingredients and products across the supply chain. Combining sensor data points with predictive analytics can help to predict the growth of pathogens before an outbreak occurs. Companies can also connect harvesting, processing and transportation equipment to the Internet through Smart Sensors. These sensors can detect biochemical and chemical reactions during the harvesting, manufacturing, and transportation stages, allowing identification and removal of molds and bacteria before they move down the supply chain. In addition to ensuring food safety, such measures can also improve the dining experience by enabling farm to table service.

Fraud detection has become increasingly important with the increase in automated and digital transactions. IoT fraud detection systems collect and use big data in real-time to detect fraudulent financial activity, send alerts, and block transactions. Real-time big data processing combined with Machine Learning algorithms can be very effective in Anomaly Detection and the identification of previously unknown issues that may be responsible for quality problems or security threats. This enables service providers to eliminate existing anomalies and prevent future ones, as well as to detect problems more rapidly and solve them proactively. Anomalies can be detected by analyzing device behavior, network dynamics, use across groups of devices owned by one customer, or location patterns.

Geofencing is a technology that uses GPS, RFID, or other location Tracking or object detection technology to define geographical boundaries. It allows administrators to set up triggers such as push notifications, email alerts, or kill switches when a device crosses a “geofence” and enters or exits an area. For example, equipment that exits a construction could be programmed to shut down beyond the borders of a Geofence in order to prevent theft. Likewise, the departure of power tools from a shop floor could trigger a text message alert. The geofencing market is segmented on the basis of components (solution and services), geofencing type, organization size, verticals, and regions. Geofencing services are further segmented into deployment and integration, support and maintenance, consulting and advisory, and API management and testing services. Market growth is driven by the connectivity of mobile assets and industrial campuses.

Immersive analytics builds upon the fields of data visualisation, visual analytics, Virtual Reality, computer graphics, and Human-Computer Interaction. Its goal is to remove barriers between people, their data, and the tools they use for analysis by presenting relevant data as needed in real time. Immersive analytics aims to support data understanding and decision making, both by people working individually and collaboratively. While this may be achieved through the use of immersive virtual environment technologies, multisensory presentation, data physicalisation, natural interfaces, or responsive analytics, the field of immersive analytics is not tied to the use of specific techniques.

Indoor air quality monitoring (IAQ) is carried out to assess the extent of pollution, ensure compliance with national or local legislation, evaluate pollution control options, and provide data for air quality modeling. It is particularly important in chemical plants, mines, and other facilities with potentially harmful concentrations of pollutants. The central objective is to ensure that the location is safe for individuals. As the burden of air quality regulation shifts from publicly-funded monitoring to industry-funded monitoring, businesses have begun to invest more heavily in their own air quality monitoring equipment and processes. An indoor air quality monitor will report on the levels of common pollutants and other air conditions inside the home or office in real-time. The culprit could be anything from excessive dust to high humidity to emissions from household cleaners or building materials. Some indoor air-quality monitors will also track outdoor air quality to provide context for the indoor readings. The measurements are then displayed on a screen on the device itself as well as in a companion app on the mobile device. Most IAQ monitors will alert users to unsafe levels via an indicator light and/or push notifications to the smartphone or tablet.

An indoor positioning system (IPS) is a network of devices used to locate people or objects where GPS and other satellite technologies lack precision or fail entirely, such as inside multistory buildings, airports, parking garages, and underground locations. A large variety of techniques and devices are used to provide indoor positioning ranging from reconfigured devices already deployed such as smartphones, WiFi and Bluetooth antennas, digital cameras, and clocks; to purpose built installations with relays and beacons strategically placed throughout a defined space. IPS has broad applications in commercial, military, retail, and inventory tracking industries. There are several commercial systems on the market, but no standards for an IPS system. Instead each installation is tailored to spatial dimensions, building materials, accuracy needs, and budget constraints. Lights, radio waves, magnetic fields, acoustic signals, and behavioral analytics are all used in IPS networks. Indoor positioning systems use different technologies, including distance measurement to nearby anchor nodes (nodes with known fixed positions, e.g. WiFi / LiFi access points, Bluetooth beacons or Ultra-Wideband beacons), magnetic positioning, and dead reckoning. They either actively locate mobile devices and tags or provide ambient location or environmental context for devices.

Industrial wearable devices are tools designed to improve workplace productivity, safety, and efficiency in sectors such as manufacturing, logistics, and mining. These devices collect data in real time, track activities, provide alerts, and provide customized experiences depending on the users' needs and organizational objectives. They are typically designed for specific situations or industry verticals, as opposed to consumer Wearables which are often general in function. Industrial Wearables can be designed to aid a worker in performing specific tasks or to measure health parameters for working in dangerous environments. In addition to performing a specific function for their wearer, the devices can be linked to enterprise systems. For example, linking Wearables worn by employees in hazardous environments to employee welfare programs can be used to track and provide evidence of the wellbeing of employees, thereby reducing health insurance costs.

Infrastructure inspection aims to automate or remotely perform inspection on infrastructure that would have otherwise been manually done in a safer and more cost-efficient manner. These inspections can be part of routine inspections or repairs. Using drones equiped with cameras, inspections can be done more cheaply and more safely than having trained repair teams go to the site to conduct manual inspections, while coordinating with shut down schedules. Machine vision, LIDAR, and other image gathering and image recognition technologies are used to generate models of the infrastructure. Auto-navigation and guiding systems are used guide the unmanned vehicles, especially under bridges or in tunnels where GPS coverage can be limited. Usually infrastructure inspection is combined with condition monitoring systems that send alerts when a specific fault is detected.

In-process Traceability helps companies to maintain complete visibility not only of work in progress components and assemblies, but also of carriers, tools, human resources, and other production resources benefiting from enhanced visibility and Traceability. It is usually achieved with RFID, UWB or other technologies that track parts and resources as they pass through the assembly line. The difference between in-process Traceability and product Traceability is that in-process Traceability focuses on the work in progress items on a manufacturing line while product Traceability focuses on goods movement across different points in the supply chain.

Intelligent packaging consists of packaging that includes some type of identification tag that can be automatically tracked as the packaging move across the supply chain. Commonly used technologies include RFID, QR codes, Near Field Communication (NFC), and cloud based applications. The objective in connecting boxes, pallets, and batches is to reduce costs, supply chain inefficiencies, and mistakes, while improving understanding how where and when products are sold to end customers. Intelligence is added to the supply chain where previously there was guesswork. Goods can be tracked from A to B; perishables can be monitored and kept in optimum conditions; and counterfeit products can be sorted from genuine ones before they hit the shelves. Manufacturing processes can also benefit by receiving information about problems related to product batches that passed through their manufacturing lines. At the retail level, intelligence packaging can enhance consumer experiences through interactive or technology-driven features, often via a mobile application, improving consumer engagement with products.

Intelligent Urban Water Supply Management uses IoT gateways to securely connect the water supply asset (e.g. pressurizing pumps) to the cloud service platform where advanced analytics are applied to the operational data communicated from the assets. The operational insight obtained from the analytics are used to drive the water supply domain applications to monitor and provide advanced maintenance capability, monitor water quality, detect water leakages, reduce energy consumption of pressurizing pumps and ensure equitable water distributions to the points of consumption during water peak usage hours and water supply shortages.

Intrusion detection systems monitor network traffic and search for suspicious activity and known threats, sending alerts when suspicious activity is identified. The overall purpose of an intrusion detection system is to inform IT personnel that a network intrusion has or may be taking place. Alerts will generally include information about the source address of the intrusion, the target/victim address, and the type of attack that is suspected. Each IDS is programmed to analyze traffic and identify patterns in that traffic that may indicate a cyberattack of various sorts. ID systems are being developed in response to the increasing number of attacks on major infrastructure and commercial sites and networks, including those of the Pentagon, the White House, NATO, ports, and electrical grids. The safeguarding of security is becoming increasingly difficult because the possible technologies of attack are becoming ever more sophisticated; at the same time, less technical ability is required for the novice attacker, because proven past methods are easily accessed through the Web. The adoption of IoT technologies also provides new avenues of attack through end points, such as connected devices, that often have weak security features.

Inventory management solutions aim to automate the inventory management process and increase accuracy and reliability. Every individual inventory item that is to be tracked receives an RFID tag or other similar Tracking technology. Each tag has a unique identification number that contains encoded digital data about an inventory item, for example the model and batch number. Tags are scanned by RFID or other readers. Upon scanning, a reader extracts the tag's ID and transmits it to the cloud for processing. Along with the tag's ID, the cloud receives data about the reader’s location and the time of the reading. Based on this data, an application states the location of the item with the corresponding ID, visualizes the findings and displays real-time updates about inventory items’ movements to the solution users, allowing them to monitor the inventory using a smartphone or a laptop from anywhere, in real time. There are also secondary benefits of inventory management. For example, Machine Learning can forecast the amount of raw materials needed for the upcoming production cycle based on the data about the inventory quantity and location, and reorder them as needed. It can also help in matching demand with supply more accurately as inventory movement is also a representation of demand.

Last mile delivery combines data from Tracking sensors, delivery systems, maps, and vehicles or drones to increase the efficiency of short distance deliveries, typically between a warehouse and a consumer. Last mile delivery is categorized by the delivery of small packages to large numbers of customers, which results in complex routes and low labor efficiency. The use of IoT in last mile delivery yields benefits that include improved operational efficiency, lower costs, security, and reliability, and improved customer service and delivery speed. Drones are not yet commonplace but could significantly decrease labor costs. Cargo drones can drop online shopping deliveries in back yards or at central delivery points in buildings. They are alreeady delivering medicines to inaccessible rural locations and urban parcel delivery drones can overcome physical barriers, such as traffic, poor roads or bodies of water. The use of drones, in combination with smart routing systems can overcome the complexity by designing and utilizing routes without considering traditional delivery barriers such as road restrictions and driver availability.

Leakage and flood monitoring systems consist of ultra-low-power sensor nodes designed for use in rugged environments or hard-to-access locations and may also include a Data Aggregation and modelling platform. They are used to detect the presence of water and to estimate damage to the water supply infrastructure and potential risks to public health or the environmental. Local water leaks typically go undetected or are responded to only after the event. And the scale of floods is often not understood until significant damage has occured. IoT enables identification of the presence, volume, and flow rate of water to improve control over water resources data, thus allowing efficient management of water utilities, and more rapid response of infrastructure managers. Smart water management systems can make a fast and significant improvement to the cost and reliability of water supplies, especially in urban areas and in agriculture.

Leasing finance automation uses sensors to detect assets usage and bill customers based on the usage. The asset usage lease or loan contract can be monitored from origination to end of life on a single, secure platform. All parties, including customers, vendors, and intermediaries, can be provided with access to the information they require to fulfill contracts or make decisions. In addition to automating information transfers and billing processes, other processes such as asset management, document collection, and underwriting can be managed in a paperless and transparent environment, resulting in lower operating costs and higher deal volume. Leasing finance automation is usually embedded within a larger asset leasing software product consisting of contract management, reporting, and application management.