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.

中文翻译：

---

客座编辑介绍：EdgeAI as a Service 特刊

在过去的十年中，移动计算和物联网（物联网）的扩散有了很大的发展。大量设备已连接到 Internet，并创建了 zettabytes 的数据项。为了从如此庞大的数据量中提取价值，通常会利用云计算提供的处理能力。但是，将数据流式传输到云会带来一些与通信和数据传输增加相关的限制，这会引入延迟并消耗网络带宽。基于云的物联网计算的另一个限制是网络连接有限或没有网络连接。基于云的物联网生成数据处理的其他问题涉及信息的敏感性，因为在云中发送和存储如此多的信息涉及隐私和安全挑战，与保护个人身份信息、按照隐私法存储信息、保护存储信息以及防止被盗或非法访问和共享有关。人工智能在边缘处理中的使用催生了一个新的跨学科领域，可以通过边缘设备实现分布式智能，被称为边缘人工智能或边缘智能。然而，边缘人工智能的研究还相对较新，因此需要支持对边缘产生的海量数据进行智能管理、查询和挖掘的模型、技术和协议。与提供边缘智能相关的许多挑战包括训练边缘设备，因此它们可以变得越来越智能。还需要介绍分布式智能研究的最新成果。