Edge computing brings the intelligent processing of data to the edge of the network. Depending on the use case, application requirements, geography and topology, and many other factors, IoT sensors can transmit massive quantities of data. Examples of IoT sensors that continuously generate data include optics, level, image, pressure, proximity, and seismic sensors. Seismic sensors are a good example of the diversity of IoT use cases. These sensors can be placed in remote locations to detect earthquakes or along physical perimeter boundaries to alert to possible intrusions. Network bandwidth and speed constraints or other factors that are related to cost or location might affect the amount of data that is collected from an IoT device such as a seismic sensor. The intelligence might be analysis of a data model that has been developed by the business or simple operation summary statistics. For example, a properly trained data model analyzing seismic sensor output can distinguish between the vibrations caused by animals and the early-warning vibrations of an earthquake. The speed and accuracy of edge processing can provide more time to react to natural disasters, minimizing damage.
The identification of critical and actionable data is another benefit of edge computing. By pushing analysis to the edge, an organization can limit the amount of data that is transmitted to the core. Edge computing thus enables analytic applications to work within constraints such as network bandwidth and speed to limit costs.
The features and capabilities of edge computing are forecast to experience massive worldwide growth, with market revenues that are expected to increase from 1.47 billion U.S. dollars in 2018 to 28.84 billion in 2025. For more information, see Edge computing market size worldwide in 2018 and 2025.