Industrial IoT

This paper addresses the problem of joint sensor activation and mobile charging vehicle scheduling for wireless rechargeable sensor networks in industrial Internet of Things (IIoT). The goal is to optimize the system energy consumption while meeting quality-of-monitoring requirements and sensor charging deadlines. The paper proposes a novel scheme that combines reinforcement learning and approximation algorithms to solve the problem efficiently. Simulation results demonstrate the feasibility and superiority of the proposed scheme over existing approaches.

This paper focuses on the joint optimization of sensor activation and mobile charging scheduling in industrial wireless rechargeable sensor networks (IWRSNs). The goal is to minimize energy consumption while meeting task requirements, sensor charging deadlines, and the mobile charger vehicle’s energy capacity. The proposed solution combines deep reinforcement learning and a marginal product-based approximation algorithm. Simulation results show the superiority of this solution compared to other methods.