Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.