Q-learning-based, Optimized On-demand Charging Algorithm in WRSN <Abstract> This paper introduces a novel charging strategy for wireless rechargeable sensor networks (WRSNs), in which a mobile charger (MC) moves and wirelessly transfers the power to the sensor nodes. The first distinct point of this work is designing the MCfs charging algorithm under the consideration of target coverage and connectivity. As a solution, we introduce a novel on-demand charging scheme for WRSNs that optimize the charging time at each MCfs charging location. Moreover, we take advantage of the Q-learning technique (i.e., hence named our algorithms Q-charging) to maximize the number of monitored targets. Q- charging can prioritize the sensor nodes, which play a more critical role in the network. Hence, Q-charging can select a suitable charging location aiming to provide sufficient power for the prioritized sensors. We have evaluated our proposal in comparison to the previous works. The evaluation results show that Q-charging can prolong the time until the first target is not monitored by 5.2 times on the average, and 14.3 times in the best case, compared to existing algorithms. |