MCVs-Facilitated Target-Oriented Efficient Wireless Charging for WRSN

With the development of the fifth generation (5G) communication technology, Wireless Rechargeable Sensor Network (WRSN) receives remarkable attentions. WRSN empowers various Internet of Things (IoT) application scenarios, such as the environment monitoring, target tracking and retail manufacturing. However, in these scenarios, WRSN will largely consume traditional on-grid power. In order to save on-grid energy and protect environment, in this work, we propose an efficient charging target selection approach by leveraging green energy mobile charging vehicles (MCVs) to power the sensor nodes (SNs). In this approach, Green Base Station (GBS) first harvests green energy and then powers these MCVs. Afterwards, the MCVs reach and charge the surrounding SNs when the SNs request energy replenishment. In order to power the SNs in WRSN with high energy efficiency, the MCVs' wireless charging energy efficiency maximization problem is formulated. Since this optimization problem is proved as an NP-hard problem, the Dynamic Shortest Path Selection (DSPS) algorithm is further proposed to efficiently solve this problem. Finally, extensive simulations validate that: 1) the DSPS algorithm leads to a highest energy efficiency and a short MCVs' traveling distance; 2) most of the SNs are able to be replenished by enough energy to sustain the lifetime of local WRSN.