Revolutionizing Wireless Rechargeable Sensor Networks: Speed Optimization-based Charging Scheduling Scheme (SOCSS) for efficient multi-node energy transfer

Abstract

Benefiting from the breakthrough of Wireless Energy Transfer (WET) technology, scheduling multiple Mobile Chargers (MCs) to charge sensor nodes can significantly prolong the lifetime of Wireless Rechargeable Sensor Networks (WRSNs). While previous studies have primarily focused on on-demand recharging within WRSNs, more consideration must be given to utilizing multi-node energy transfer with optimal charging locations to devise efficient charging schedules for requesting sensor nodes. Moreover, existing approaches assume a constant travel speed for MCs and utilize omnidirectional WET, leading to increased energy consumption for MCs and consequently affecting overall charging efficiency. To address these challenges, we propose a novel Speed Optimization-based Charging Scheduling Scheme (SOCSS) for multiple MCs in WRSNs. The initial phase of SOCSS involves clique-based network partitioning to identify minimum cliques and determine optimal charging locations to perform efficient multi-node energy transfer for sensor nodes. The subsequent phase encompasses scheduling and path planning, where the charging schedule is established using efficient Quantum-inspired Particle Swarm Optimization. By integrating speed optimization with the charging schedule, the energy consumption of the MCs is minimized, leading to cost-effective planning of the charging path for energy-constrained MCs. Extensive simulations are conducted to showcase the supremacy of SOCSS across a range of network parameters compared to prior art. In particular, SOCSS has achieved an impressive average reduction of 36.2% in the number of stopping points for MCs, a remarkable 38.9% decrease in the total travel distance, and a 15.7% reduction in the charging delay.