Guest Editor's Introduction: Special Section on Edge AI as a Service

In the last decade there has been a strong move towards mobile computing and the proliferation of the IoT (Internet of Things). A huge number of devices have been connected to the Internet and created zettabytes of data items. To extract value from such massive data volumes, processing power offered by cloud computing is often utilized. However, streaming data to the cloud exposes some limitations related to increased communication and data transfer, which introduces delays and consumes network bandwidth. Another limitation that cloud-based computing for IoT poses is a limited or no network connectivity. Other problems with cloud-based processing of IoT generated data regard the sensitivity of the information, because sending and storing so much information in the cloud involves privacy and security challenges, related to the protection of personally identifiable information, storing it in compliance with privacy laws, securing stored information, and preventing from being stolen, or accessed and shared illegally. The use of AI in edge processing resulted in a new interdisciplinary field that enables distributed intelligence with edge devices and is known as edge AI or edge intelligence. However, research on edge AI is still relatively new, and thus models, techniques, and protocols supporting intelligent management, querying and mining of large-scale amounts of data produced at the edge are required. A lot of challenges related to providing edge intelligence include training edge devices, so they can become more and more smart. There is also a need for the presentation of the most recent outcome of research of distributed intelligence. The papers in this special issue address many of the challenges we have outlined, and are briefly summarized.