Adaptive online mobile charging for node failure avoidance in wireless rechargeable sensor networks

Recent breakthrough progress of wireless energy transfer technology and rechargeable lithium battery technology emerge the Wireless Rechargeable Sensor Networks (WRSNs). To avoid sensor node failure, how to schedule the Mobile Charger (MC) to recharge sensor nodes in WRSNs is very challenging. Previous works that predetermining the charging path of MC cannot adapt to the diversity and dynamic energy consumption of sensors in actual environment and may lead to problematic schedules. Many online charging schemes are proposed to overcome the challenges, but they still get performance limitation due to leaving out of consideration about the energy depletion issue resulted from not timely and/or unfair charging response. Particularly, the node failure problem will be worse when there are a large number of charging requirements in the network. In this paper, we address the node failure avoidance mobile charing for WRSNs, which aims to minimize the number of invalid nodes due to sensor node energy depletion in the charging process. We first consider the dynamic energy consumption rate of the node based on both its history statistics and real time energy consumption. Then we propose two efficient online charging algorithms named PA and INMA, respectively. PA selects the next charging node according to the charging probability of the requesting nodes, whereas INMA always chooses the nodes which make the least number of other requesting nodes suffer from energy depletion as the charging candidates. Furthermore, to achieve high charging efficiency, the node with the shortest time to finish the charging will be selected as the next charging node if there are multiple nodes in the candidate set. Simulation results demonstrate that the proposed algorithms can effectively solve the node energy depletion problem with lower charging latency and charging cost in comparison with other current existing online charging schemes.