Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments.

中文翻译：

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物联网的人工智能驱动边缘计算专刊

近年来，目睹了移动计算和物联网（IoT）的激增，其中数十亿个移动和IoT设备连接到Internet，并在网络边缘生成数十亿字节的数据。但是，由于网络的带宽限制，云服务的潜在不可控制的延迟以及从物联网设备收集数据时的隐私问题，使用当前的云设备架构来传输和处理数十亿字节的数据具有挑战性且不可行。为了应对这些挑战，边缘计算这一新兴的计算范例已引起了广泛的关注。通过将数据存储，计算和控制推向更靠近网络边缘的位置，边缘计算已被广泛认为是一种有前途的解决方案，可以满足低延迟，高可扩展性，和能源效率，以及减轻网络流量负担。但是，随着各种物联网应用程序的出现（例如，智能城市，工业自动化和互联汽车），边缘计算处理这些异构物联网环境变得越来越具有挑战性。