A Reinforcement Learning-Based Sleep Scheduling Algorithm for Desired Area Coverage in Solar-Powered Wireless Sensor Networks

In this paper, a reinforcement learning-based sleep scheduling for coverage (RLSSC) algorithm is proposed for sustainable time-slotted operation in rechargeable sensor networks. RLSSC is a two-stage sleep scheduling algorithm. It includes the precedence operator-based group formation algorithm and the Q learning-based active node selection algorithm. First, a precedence operator is designed in the group formation algorithm to ensure the desired area coverage. All the nodes are formed into groups. Then, Q learning algorithm is expanded into a multi-sensor cooperation Q learning group model. The learning and action selection strategy are designed in a group to direct nodes in collaborative learning of working modes selection while adapting to the dynamic environment. Through the role changes of active nodes, the algorithm accomplishes the entire team learning of sleep scheduling while scheduling others into sleep modes. Experiments of RLSSC on a solar-powered wireless sensor network for area surveillance tasks are presented. Compared with LEACH and a random algorithm, the results show that RLSSC can effectively adjust the working modes of nodes in a group by perceiving the environment. In addition, it achieves the energy consumption balance between nodes so as to prolong the network lifetime while maintaining the desired coverage.