Ambient Backscatter Communication-Assisted Intelligent Resource Management for Green Industrial IoT

As the industrial Internet of Things (IIoT) is being built and promoted, digital and intelligent production methods are advancing rapidly. However, with the increasing number of deployed devices and complicated resource optimization schemes, several inevitable problems, such as excessive energy overhead, are brought. Driven by these issues, we propose and design a green system architecture and optimization scheme that consists of perceptual control, heterogeneous system model, and decision optimization. Based on this aspect, we focus on optimizing energy efficiency in the IIoT by utilizing ambient backscatter communication (AmBC) technology in the perception control layer, and reducing training costs through collective deep reinforcement learning (CDRL) among edge nodes. Simulation results show that our proposed scheme has significant advantages in the area of energy efficiency.