Publisher's Synopsis
Edge and fog analytics are on the rise and expected to continue growing in coming years. The global edge analytics market is expected to see a significant growth rate with a substantial increase in investment aiming to improve the overall edge analytics. Today, edge and fog analytics are not only powered by machine learning (ML) and artificial intelligence (AI) but also by the expansion of cloud-to-edge range of ML/AI chips that are built-in to accommodate high-performance computing (HPC) requirements, in the vicinity of where data is produced.The Internet of Things (IoT) devices with their embedded processing capabilities using new tiny chips designed to run complex models at the edge, are more appealing for ML and AI applications. These chip-enhanced devices are directly connected to a wide range of sensors, such as iPhone sensors, meteorological and hydrological sensors, which are capturing large quantity of input data as streaming data. Edge and fog analytics, at the edges, reduce the latency between data capture and decision-making by acting immediately on streaming data, which may be required in critical remote operations.Both cloud and edge analytics will be supplementing each other in handling large-scale workloads and delivering data insights. The edge analytics handles a subset of data, which can be both processed and analysed at the edge with the results transmitted back to the local area network level, the fog, which in turn transmits the data into the cloud. This period, of the emerging cloud-to-edge ecosystem, is referred to as the
