Joint Charging and Data Collection Strategy for Mobile Vehicles in Large-Scale Wireless Rechargeable Sensor Networks

Sensing data collection and energy replenishment are key issues of wireless rechargeable sensor network (WRSN). However, due to the limited battery capacity and cache size of mobile charging vehicle (MCV), using a single MCV to collect data and supplement energy for large-scale WRSNs is impossible. Therefore, we propose a joint mobile charging and data collection strategy of multiple MCVs for large-scale WRSNs. In this strategy, a periodic charging and data collection model is established to ensure that the nodes work permanently, and the sensing data are collected promptly by MCVs. We formulate the problem of reducing the time of charging and data collection of multiple MCVs and balancing the energy consumption of each MCV into a multi-objective optimization problem. To address the problem, a multi-objective Monkey King evolutionary (MOMKE) algorithm based on Pareto dominance is proposed. The influence of the parametric setting of the MOMKE is analyzed, while also verifying the feasibility of the algorithm. The experiment results indicate that MOMKE outperforms the three existing methods in terms of charging efficiency and data collection efficiency by 10% and 7%, respectively. Moreover, MOMKE is competitive in terms of the completion of the shortest moving path and maximum data collection delay.