Our use case database tracks 130 use cases in the global IoT Ecosystem.
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10 use cases
Advanced Metering Infrastructure
Advanced metering infrastructure (AMI) is an integrated network of sensors, smart meters, and software that empowers 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 and more precisely track resource consumption. 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.
Building Automation & Control
Building automation and control (BAC) systems involve a combination of hardware and software that control aspects of a building’s systems, potentially including power, lighting and illumination, access and security, heating, ventilation and air-conditioning systems (HVAC), environmental sensors, elevators and escalators, and entertainment. Benefits of building automation and control systems can include efficient control of environmental conditions, individual room control, increased staff productivity, effective use of energy, improved equipment reliability, and preventative maintenance. For example, systems can provide information on problems with building equipment, allowing for computerized maintenance scheduling as opposed to reactive identification and management of issues. Building management systems are most commonly implemented in large projects with extensive mechanical, HVAC, electrical, and plumbing systems. Building management systems (BMS) are central to BAC use cases. Systems linked to a BMS typically represent 40% of a building's energy usage; if lighting is included, this number approaches 70% on average. BMS systems are thus critical components for managing energy demand. Improperly configured BMS systems are believed to result in the wastage of 20% of a typical building's energy usage, or approximately 8% of total energy usage in the United States.
Building Energy Management
Building energy management systems (BEMS) provide real-time remote monitoring and integrated control of a wide range of connected systems, allowing modes of operation, energy use, and environmental conditions to be monitored and modified based on hours of operation, occupancy, or other variables to optimise efficiency and comfort. Building energy management systems can also trigger alarms, in some cases predicting problems and informing maintenance programmes. They maintain records of historical performance to enable benchmarking of performance against other buildings or across time and may help automate report writing. BEMS are often integrated with building automation and control (BAC) systems, which have a broaded scope of operations.
Continuous Emission Monitoring Systems
Continuous emission monitoring systems (CEMS) measure airflow, dust, the concentration of air pollutants (such as SO2, NOx, CO, etc.), and other parameters related to emissions. Required parameters depend on the type of stationary source and local regulations. A standard CEMS consists of a sample probe, filter, sample line (umbilical), gas conditioning system, calibration gas system, and a series of gas analyzers that reflect the parameters being monitored. Typically monitored emissions include: sulfur dioxide, nitrogen oxides, carbon monoxide, carbon dioxide, hydrogen chloride, airborne particulate matter, mercury, volatile organic compounds, and oxygen. CEMS can also measure airflow, flue gas opacity, and moisture.
Edge Computing & Edge Intelligence
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.
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.
Indoor Air Quality Monitoring
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.
A microgrid is a localized group of electricity sources and loads that normally operates connected to and synchronous with the traditional wide area synchronous grid, but can also disconnect to island mode and function autonomously as physical or economic conditions dictate. In this way, a microgrid can effectively integrate various sources of distributed generation (DG), especially Renewable Energy Sources (RES) - renewable electricity, and can supply emergency power, changing between the island and connected modes.Microgrids are typically supported by generators or renewable wind and solar energy resources and are often used to provide backup power or supplement the main power grid during periods of heavy demand. A microgrid strategy that integrates local wind or solar resources can provide redundancy for essential services and make the main grid less susceptible to localized disaster.
Predictive maintenance is a technique that uses condition-monitoring sensors and machine learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on predictive maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.
Smart City Operations
Smart city operations include the range of solutions required to enable smart city concepts by integrating information and communication technology with senors and connected devices to optimize the efficiency of city operations and services. Smart city technology allows city officials to interact with both community members and city infrastructure and to monitor situation in the city in real time. Benefits for city managers include tracking events in the city in real time, managing congestion, improving operational efficiency, reducing emergency response times, and enabling remote management. Modern solutions will aim to integrate all city data into a single dashboard. Both historical and current KPIs are measured to conduct performance reviews and gap analysis, and to plan future infrastucture and service investments.