LAUSR

Artificial Intelligence of Things (AIoT) is an emerging area of future Internet of Things (IoT) to support intelligent IoT applications. In AIoT, intelligent edge computing technologies accelerate intelligent services’ processing speed with much lower cost than simple cloud-aided IoT architecture. However, there is still a lack of resource strategy to optimize the energy efficiency of AIoT with intelligent edge computing. Therefore, in this article, we focus on the energy consumption of edge devices and cloud services in processing AIoT tasks and formulate the optimization problem in scheduling tasks in the edge and the cloud. Meanwhile, a novel online method is proposed to solve the optimization problem. We investigate the energy consumption of several typical intelligent edge devices and the cloud service in an intelligent edge computing testbed. Extensive simulation-based performance evaluation shows that the proposed method outperforms other strategies with lower energy consumption.