Instant on-demand charging strategy with multiple chargers in wireless rechargeable sensor networks

Abstract

The limited battery capacity of sensor nodes restricts the widespread application of wireless sensor networks (WSNs). Wireless power transfer enables prompt recharging in WSNs, which are called wireless rechargeable sensor networks (WRSNs). However, mainly due to underestimating the combined influence of spatial and residual energy constraints by charging requests, traditional scheduling strategies for on-demand WRSNs achieve low charging throughput or success rate, posing a major bottleneck for further improvements. In this paper, an instant on-demand charging strategy (IOCS) is proposed that unites the spatial domain, temporal domain, and event domain features of nodes. IOCS forms several sub-areas based on an improved K-means algorithm, and one wireless charging vehicle (WCV) is allocated for charging nodes in each sub-area. The distance of nodes and the WCV, the residual energy of nodes, and the energy consumption rate are integrated and quantized step by step, which formats the united charging priority. The united charging priority directs the WCV to recharge the nodes one by one in each sub-area. Extensive simulations were conducted to demonstrate the advantage of IOCS over two state-of-the-art strategies: nearest job next with preemption (NJNP) and ant colony algorithm (ACO). The simulation results reveal that the proposed strategy outperforms NJNP and ACO in terms of charging latency, energy utilization rate, and the number of dead nodes. Statistically, compared with NJNP and ACO, IOCS increases the energy utilization rate by 7.43% and 18.94%, and reduces the number of nodes by 18% and 34%, respectively.