Cloud-Edge Collaboration with Green Scheduling and Deep Learning for Industrial Internet of Things

Abstract

As a key technology of the sixth generation (6G), cloud-edge collaboration has attracted attention in the industrial Internet of Things (IIoT). However, the delay-sensitive and resource-intensive intelligent services in IIoT not only require a large number of computing resources to reduce the delay cost and energy consumption of devices but also require fast and accurate intelligent decisions to avoid service congestion. In this paper, we design an offloading scheme based on cloud-edge collaboration and edge collaboration, including four computing modes, which jointly consider the delay and energy optimization of devices. We propose a parallel deep learning-driven cooperative offloading (PDCO) algorithm, which weighs the real-time and accuracy of offloading scheme. To deal with the difficulty of obtaining labels, a low-complexity hybrid label processing method is designed to reduce the cost of labeling data, and then multiple parallel deep neural networks (DNNs) are trained to generate the best offloading decision timely. Simulation results show that the proposed algorithm can generate offloading decisions with more than 90% accuracy in 0.1s while considering green scheduling.