Description
IIoT applications are generating more data than ever before. In many industrial applications, especially in highly distributed systems located in remote areas, a large amount of raw data may not be continuously sent to the central server. In order to reduce latency, reduce data communication and storage costs, and improve network availability, enterprises are moving artificial intelligence and machine learning to the edge for real-time decision-making and action in the field. These applications that deploy AI capabilities on the IoT infrastructure are called “AIoT”. Although users still need to train AI models in the cloud, they can implement data collection and reasoning on the spot by deploying trained AI models on edge computers. This article discusses how to select the appropriate edge computer for industrial AIoT applications, and provides several case studies to help get started. Bring AI into IIoT The emergence of the Industrial Internet of Things (IIoT) enables a wide range of enterprises to collect large amounts of data from previously undeveloped sources and explore new ways to improve productivity. By obtaining performance and environmental data from field equipment and machinery, organizations can now use more information to make informed business decisions. Unfortunately, there are too many IIoT data for humans to process alone, so most of the information is not analyzed and used. Therefore, it is no wonder that enterprises and industry experts are turning to artificial intelligence and machine learning solutions for IIoT applications to obtain an overall view and make more informed decisions faster.
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