Accelerating the Industrial Internet of Things
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54 use cases
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Additive Manufacturing (AM) | 3D Printing
Additive manufacturing (AM) is one use case for 3D printing technology. The other primary use case is rapid prototyping. Both use cases refer to the processes of synthesizing a three-dimensional object from successive layers of material. These objects can be of almost any shape or geometry and are produced from a 3D model or other data source.Additive manufacturing is currently much less common than rapid prototyping due to the higher standards for quality and cost competitiveness. It is relatively expensive and time consumptive to produce a prototype using traditional manufacturing technologies. 3D printers can dramatically cut both the cost and time in many cases. And the quality of a prototype can generally be below that of a finished product. In contrast, traditional manufacturing technology is excellent at mass producing finished products. For this reason, additive manufacturing is currently used primarily to produce customized products, small batches of replacement components, and designs that are particularly challenging for traditional manufacturing processes. 
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Advanced Metering Infrastructure (AMI)
Advanced Metering Infrastructure (AMI) is an integrated network of sensors, smart meters, and software that empower end users to monitor and control utilities such as water, gas, and electricity. AMI systems enable the measurement and visualization of time-specific data in real-time which, combined with remote control capabilities, can help companies and households reduce overhead costs.The application of AMI must be complemented by the utilization of advanced security systems to ensure that data and control capabilities cannot be tampered with. This is important because both direct billing and operational decisions are often determined by the data provides by an AMI system.AMI is also commonly known as Advanced Metering Reading (AMR).  
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Agricultural Drones
Agricultural drones are a class of unmanned aerial vehicle (UAV). Agriculture monitoring is among the most mature use cases for drones. The use case's value proposition is rooted in the high labor cost of monitoring a wide, rural expanse of agricultural land using traditional ground-based vehicles. In many environments, drones can cover 10 times more land than a ground-based observer in the same amount of time due to their sky-to-earth perspective and ability to fly over barriers. Drones can also be automated for routine assessments, negating entirely the need for human operation.The flight of agriculture drones may be controlled with various degrees of autonomy, ranging from the remote control by an operator located in the vicinity to fully autonomous flight coordinated by onboard computers.Drones are most often deployed in large farm holdings with varying topographic climates. They are also useful in situations where issues related to bacteria fungus, or pests are difficult to manage and require regular monitoring. In addition to cropland, drones are also deployed to monitor aquaculture and forests, as well as poultry, cattle, and other livestock.
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Asset Health Management (AHM)
Asset Health Management refers to the process of analyzing the health of an asset as determined by operational requirements. The health of an asset in itself relates to the asset's utility, its need to be replaced, and its need for maintenance. It can be broken down into three key components:Monitoring: Tracking the current operating status of the asset.Diagnostic Analysis: Comparing real-time data to historical data in order to detect anomalies.Prognostic Analysis: Identifying and prioritizing specific actions to maximize the remaining useful life of the asset based on analysis of real-time and historical data.
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Asset Lifecycle Management (ALM)
The objective of Asset Lifecycle Management (ALM) is to optimize the profit generated by assets over the course of their lifecycle. ALM integrates processes and technologies in order to manage asset portfolios, execute projects, and facilitate efficient asset management practices. Whereas Asset Health Management (AHM) deals with monitoring and optimizing the health the asset in real time, ALM extends across the lifecycle of the asset from design, to procurement, commissioning, operations, maintenance, and decommissioning. IoT technologies enable superior visibility, forecasting, and feedback loops across the ALM process.  
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Augmented Reality
Augmented Reality or enhanced virtual reality, is a technology that seamlessly integrates real-world information and virtual world information. The real environment and virtual objects can be superimposed on the same picture or space in real time. Augmented reality technology can incorporate virtual information (objects, pictures, videos, sounds, etc.) into the real environment, enrich the real world and build a more comprehensive surrounding.Industry sector can benefit from AR by facilitating the equipment maintenance, guiding the production and manufacturing process of commodities, and improving the marketing champions.      
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Autonomous Robots
Autonomous robots are intelligent machines capable of performing tasks in the world independently of either direct human control or fixed programming. Examples range from autonomous drones, to industrial production robots, to your robotic vacuum cleaner. They combine expertise from the fields of artificial intelligence, robotics, and information science.The autonomous robot must have the ability to perceive its environment, analyze situational data in order to make decisions based on what it perceives, and then modify its actions based on these decisions. For example, the scope of autonomy could include starting, stopping, maneuvering around obstacles, communicating to obstacles, and using appendages to manipulate obstacles. There are few autonomous robots in operation today. Even most sophisticated, dynamic robots such as those used in an automotive factory perform according to static programming. And most "autonomous robots" are only semi-autonomous and will likely remain so even as more fundamental autonomy becomes technically feasible. For example, the Roomba vacuum cleaner does not move according to a pre-programmed route and can modify its route dynamically as its environment changes. However, it has a very limited degree of freedom that is determined by its programming. 
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Autonomous Transport Systems
Autonomous transport systems provide unmanned, autonomous transfer of equipment, baggage, people, information or resources from point-to-point with minimal intervention. They can include the full range of transport vehicles, including trucks, buses, trains, metros, ships, and airplanes. They are most commonly deployed in controlled industries zones but are expected to soon be deployed in public areas with varying degrees of autonomy. We differentiate autonomous transport systems from autonomous vehicles. Whereas autonomous vehicles serve individual passengers (who may or may not own the vehicle), autonomous transport systems are interconnected fleets of vehicles owned by a business to service a particular need systematically. When discussing autonomous transport systems, the focus is on the interaction among vehicles in a sophisticated system that interfaces with ERP, MES, and other enterprise data management systems. The autonomy of the vehicle is one component of a larger interconnected system of autonomous and semi-autonomous activity with the objective of achieving business or organizational objectives, such as delivering the mail or moving soil from a mine to a processing facility. 
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Autonomous Vehicles
Autonomous vehicles are cars or trucks that perform functions to support dependent on connecting devices with intelligence such as lights, radars, steering etc. to situation awareness and planning. The fusion of components and intelligence is what makes AV different from regular vehicles. We differentiate autonomous vehicles from autonomous transport systems. Whereas autonomous transport systems are interconnected fleets of vehicles owned by a business to service a particular need systematically, autonomous vehicles serve individual passengers (who may or may not own the vehicle). Autonomous vehicles are widely divided into five degrees of autonomy. The movement towards greater autonomy is impacted by technical, environmental, and regulatory or legal factors. A given vehicle may be technically capable of an advanced level of autonomy but be unable to perform to that level in a highly chaotic environment or may be prevented by regulatory prohibition or legal risk. Level Zero – No AutomationAt level zero, the operator performs all tasks. The vehicle has no autonomy.Level One – Driver AssistanceAt level one, the vehicle can assist with specific functions such as applying modest breaking force when the vehicle approaches too close to an obstacle. However, the vehicle operator is responsible for accelerating, braking, and monitoring of the surrounding environment. Level Two – Partial AutomationAt level two, the vehicle can assist with steering or acceleration functions and allow the operator to disengage from some of their tasks for a limited duration. However, the operator must always be ready to take control of the vehicle and is responsible for safety-critical functions and monitoring of the environment. Many vehicle manufacturers are developing vehicles at this level.Level Three – Conditional AutomationAt level three, the vehicle controls all monitoring of the environment using sensors such as LiDAR. The operator's attention remains critical but the operator can disengage from “safety critical” functions like braking and expect the vehicle to navigate safely under normal conditions. In the case of trucks, many level three vehicles require no human attention to the road at speeds under 37 miles per hour. Level Four – High AutomationAt level four, the vehicle is capable of steering, braking, accelerating, and monitoring the vehicle and environment, and responding to unexpected events in most driving conditions. At level four, the vehicle notified the driven when conditions are safe for autonomous transportation. The vehicle is then expected to be able to operate as well as a typical human operator. However, the vehicle may request to transfer control back to the human operator under highly dynamic circumstances.Level Five – Complete AutomationAt level five, no human attention is required. Level five vehicles do not require space for an operator. Likewise, there is no need for pedals, brakes, a steering wheel or other manual controls. The autonomous vehicle system controls all critical tasks, monitoring of the environment and identification of unique operating conditions.As noted above, it is significantly easier to reach level five automation in a controlled environment such as a mine or metro track than in a highly dynamic environment such as a city road.  
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Building Automation and Controls (BAC)
Building Automation and Controls (BAC) are a combination of hardware and software that control a building’s power systems; lighting and illumination; electric power and control; security, observation and magnetic card access; heating, ventilation and air-conditioning systems (HVAC); outdoor controls; lift, elevator and escalator controls; entertainment and BMS (Building Management Systems).BAC systems provide efficient control of internal comfort conditions, individual room control, increased staff productivity, effective use of energy, improved building reliability and life, quick and effective responses to HVAC problems, and save time and money. The systems also provide information on problems in the building, allow for computerized maintenance scheduling, are easy and effective for employees to use, and easily detect problems.Building management systems are most commonly implemented in large projects with extensive mechanical, HVAC, electrical, and plumbing systems. Systems linked to a BMS typically represent 40% of a building's energy usage; if lighting is included, this number approaches to 70%. BMS systems are a critical component of managing energy demand. Improperly configured BMS systems are believed to account for 20% of building energy usage, or approximately 8% of total energy usage in the United States.In addition to controlling the building's internal environment, BMS systems are sometimes linked to access control (turnstiles and access doors controlling who is allowed access and egress to the building) or other security systems such as closed-circuit television (CCTV) and motion detectors. Fire alarm systems and elevators are also sometimes linked to a BMS, for monitoring. In case a fire is detected then only the fire alarm panel could shut off dampers in the ventilation system to stop smoke spreading and send all the elevators to the ground floor and park them to prevent people from using them. 
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Building Energy Management System (BEMS)
Building Energy Management Systems (BEMS) are computer-based systems that help to manage, control and monitor building technical services (HVAC, lighting etc.) and the energy consumption of devices used by the building. They provide the information and the tools that building managers need both to understand the energy usage of their buildings and to control and improve their buildings’ energy performance.HVAC (heating, ventilating, and air conditioning) is the technology of indoor environmental control. Its goal is to manage air temperature, humidity, and quality to meet the needs of both people and industrial processes. With low-cost sensors, wireless connectivity and more powerful data processors, HVAC companies are now able to collect real-time performance data and monitor the condition of their equipment. Remote monitoring solutions can monitor hard to reach areas and sites in order to alert staff to breakage and heat or water damage in a cable. IoT can help the HVAC industry improve overall equipment effectiveness and save money by minimizing equipment failure and optimizing energy usage and performance levels.  
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Collaborative Robotics
A flexible form of human-machine interaction where the user is in direct contact with the robot while he is guiding and training it. A collaborative robot, or "cobot," is a robot that can safely and effectively interact with human workers while performing simple industrial tasks. However, end-effectors and other environmental conditions may create hazards, and as such risk assessments should be done before using any industrial motion-control application. 
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Computer Vision
The purpose of computer vision is to program a computer to "understand" a scene or features in an image. it seeks to automate tasks that the human visual system can do. Computer vision systems are used increasingly to solve problems of industrial inspection, allowing for complete automation of the inspection process and to increase its accuracy and efficiency.One of the most common applications of computer vision is the inspection of the products such as microprocessors, cars, food, and pharmaceuticals. It also can be applied in detection, segmentation, localization, and recognition of certain objects in images (e.g., human faces). 
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Continuous Emission Monitoring Systems (CEMS)
Continuous emission monitoring systems (CEMS) are used to monitor flue gas for oxygen, carbon monoxide and carbon dioxide to provide information for combustion control in industrial settings.For example, operators use gas detection devices to monitor and prevent gas leaks. Detection of gas levels and leakages in industrial environments, surroundings of chemical factories and inside mines involves significant costs and is of crucial importance for operating safety. While preventive maintenance could provide for another IIoT solution, at remote sites it is costly and sometimes ineffective. 
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Cybersecurity
Cybersecurity refers to the protection practice for the hardware, software, and data from being destroyed, altered or leaked by accidental or malicious reasons to ensure the system runs continuously and the network service is not interrupted. An effective cybersecurity methodology has multiple levels of protection spread across the computers, networks, programs, and data that one intends to remain secure.  For an effective defense from cyber-attacks, the people, processes, and technology in any organization should complement one another.The cybersecurity can be divided into physical security and logical security. Physical safety refers to the physical protection of system equipment and related facilities from damage and loss. Logical security includes integrity, confidentiality, and availability of information.
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Digital Twin
A digital twin works as a bridge to connect the physical and the virtual world for a process, product or service. It is a digital replica of a living or non-living physical entity. With the development of IoT, a digital twin can constantly accumulate data and mutually transfer the information with the physical body during the life cycle of the system.The object of the digital twin is to analyze data and monitor systems in order to head off problems before they occur, prevent downtime, develop new opportunities and even plan for the future.
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Edge Computing | Edge Intelligence
Edge (fog) computing shifts computing applications, data, and services away from centralized servers to the extremes of a network. This enables analytics and knowledge generation to occur at the source of the data. Industrial IoT companies face challenges turning machine data into business intelligence. 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 accessing large amounts of machine-generated data by processing data at the edge of the network and converting it into actionable, useful business information. In an Intelligent Industrial Fog, 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, reduce running costs by reducing power consumption during off-cycles, or even detect imminent failures and notify 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. Import IoT applications include remote cloud services, distributed data storage and retrieval, and self-healing networks. 
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Factory Operations Visibility & Intelligence
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.  
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Fleet Management (FM)
Fleet management is an administrative approach that allows companies to organize and coordinate work vehicles with the aim to improve efficiency, reduce costs, and provide compliance with government regulations. While most commonly used for vehicle tracking, fleet management includes following and recording mechanical diagnostics and driver behavior.Automated Fleet Management solutions to connect vehicles and monitor driver activities, allowing managers to gain an unprecedented level of insight into fleet performance and driver behavior. This enables them to know where vehicles and drivers are at all times, identify potential problems much sooner and mitigate risks before they become larger issues that can jeopardize client satisfaction, impact driver safety or increase costs. 
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Fog Computing
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. 
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Geofencing
Geofencing is a technology that uses GPS, RFID, or other 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.  
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Indoor Air Quality Monitoring (IAQ)
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. 
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Indoor Positioning System (IPS)
Indoor Positioning Systems (IPS) are used to locate persons or objects inside buildings, as opposed to GPS which works outdoors. IPSs impact asset monitoring and automation at the enterprise level. The technology is expected to bring in integration capabilities of analytical software tools with the existing maps and navigation software. 
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Industrial Digital Thread
The digital thread refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time.”The Industrial Digital Thread (IDT) testbed drives efficiency, speed, and flexibility through digitization and automation of manufacturing processes and procedures. It collects information in the design, manufacturing, service and supply-chain setup, and provides access to intelligent analytics for industrial manufacturing and performance data.The Digital Thread integrates design, engineering, manufacturing, and MRO (maintenance, repair, and overhaul) systems, establishing a seamless flow of information. This type of integration employs data and analytics across the complete product life cycle, optimizing efficiency, from design to manufacturing, operations, and maintenance to service in a closed loop. 
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Industrial Wearables
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.
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Intelligent Urban Water Supply Management
Intelligent Urban Water Supply Management refers to a cloud service that provides a fully integrated multi-function, multi-service, multi-role, and multi-tenant solution.Water, after air, is the second most critical natural resource our lives depend upon. Maintaining adequate clean and safe water supply to urban residents has become ever-challenging. This is especially so under the pressure from the rapid urbanization of the populations in developing countries and increasingly severe constraints of available water resources in many parts of the world. The situation is exacerbated by the inadequate and aging equipment deployed in the water supply infrastructure and the ineffectiveness in the management of the operations of the equipment. The consequence of these conditions impacts the health and quality of lives of millions of urban residents. 
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Intrusion Detection Systems (IDS)
Intrusion Detection Systems (IDS) are devices or software applications that monitor network or system activities for malicious activities or policy violations and send reports to a management station.ID systems are being developed in response to the increasing number of attacks on major sites and networks, including those of the Pentagon, the White House, NATO, and the U.S. Defense Department. 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. 
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Inventory Management
Inventory Management is the management of inventory and stock. As an element of supply chain management, inventory management includes aspects such as controlling and overseeing ordering inventory, storage of inventory, and controlling the amount of product for sale.The role of IIoT in inventory management boils down to turning the data fetched by RFID readers into meaningful insights about inventory items’ location, statuses, movements, etc., and giving users a corresponding output.Moreover, inventory management solutions based on Industrial IoT can be integrated with other systems like an ERP  and share data with other enterprise’s departments.   
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Last Mile Delivery
The last mile refers to the delivery of goods from the transportation hub to the final destination. Based on the customer segmentation, it can be divided into residential delivery (2C), commercial delivery (2B), and self-service terminals. Based on the commodities types, the last kilometer can be divided into specialized distribution and general distribution. Specialized distribution commodities generally have unique requirements on distribution conditions and time, such as cold chain distribution, large-scale distribution of furniture and household appliances, etc.Due to the expansion of omnichannel in the retailer, the last mile delivery has become a critical point in the supply chain to achieve time and cost efficiency. Furthermore, Last Mile Delivery is also vital for retaining customer loyalty for e-commerce retailers since it is the mere opportunity for fact-to fact interaction with clients. 
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Machine Condition Monitoring
Early predictions on equipment malfunctions and service maintenance can be automatically scheduled ahead of an actual part failure by installing sensors inside equipment to monitor and send reports.Machine condition monitoring is used to determine the condition of a machine with the intent to forecast mechanical wear and failure. The predicted data provides health information about the machine and helps to predict machinery failure. The monitoring equipment tracks changes in temperature, vibration, and output of machines in order to detect an imbalance, corrosion, wear, misalignment, sediment build-up, or poorly lubricated parts.Condition monitoring has gained importance in line with increased company focus on productivity and asset utilization. 
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