Learning-Based Terminal-Edge Collaborative Energy-Efficient Routing Algorithm for Green RWSN

In recent years, wireless smart sensors powered by solar energy have been widely deployed to ensure the green and sustainable operation of remote industrial systems monitoring. Such devices can offload computing tasks locally or using the edge server by transmitting the raw data wirelessly. Since random renewable energy harvesting has a detrimental effect on the energy balance of nodes in these green rechargeable wireless sensor networks (RWSN), the rational synergy between terminal-edge collaborative tasks offloading (TECTO) and network topology optimization (NTO) is of great significance for improving the sustainability. Therefore, this article presents a learning-based terminal-edge collaborative energy-efficient routing algorithm. First, a system model is developed to integrate TECTO and NTO, and the original problem is decoupled into two layers. Then, the NTO layer is aimed at quickly generating an energy-efficient network topology by variable cycle block coordinate descent method based on the greedy strategy. Finally, the TECTO layer adopts deep reinforcement learning based on the dynamic baseline to understand the energy efficiency feedback law of the NTO layer and rationally adjusts the TECTO scheme. The simulation results show that the presented algorithm can reasonably generate the network topology and TECTO scheme according to the node's energy state change and efficiently consume the renewable energy distributed in the green RWSN, which significantly enhances its sustainability.