SWIPT-Based Energy Scheduling for Solar-Powered WSN in Full-Duplex Mode

Abstract

Solar harvesting, as the most mature environmental energy harvesting technology, can provide a convenient, low-carbon, and off-grid solution for energy-constrained WSN in remote environmental monitoring. However, the stochastic and intermittent nature of solar energy seriously affects network energy efficiency. In this paper, we jointly consider solar harvest deployment and scheduling for clustered WSN collaborating with SWIPT, where the solar-powered cluster (SC) coordinates SWIPT to periodically broadcast energy to a set of wireless charging nodes in full-duplex mode, each member node exhausts the recharged energy for data sensing and forwarding. To maximize network energy efficiency, we formulate a Solar harvesting collaborative SWIPT Energy Scheduling(SS-CoES) optimization problem, which jointly optimizes the deployment of SCs, the energy broadcast power with time allocation, and data sensing size under the constraint of the self-sustaining demand for network energy. Given the combinational nature of the energy deployment with its strong coupling with energy scheduling, we propose a multi-layer iteration decoupling optimization algorithm based on the Dinkelbach method, which decouples the problem into three subproblems and proposes the Hybrid discrete Firefly algorithm, Lagrangian dual subgradient algorithm, and bisection search method to obtain near-optimal feasible solutions. Simulation results show that the proposed SS-CoES can effectively maximize network energy efficiency and eliminate the dependence on the grid and move chargers, achieving rechargeable wireless sensor networks (RWSN) energy self-sustainability in remote areas.