Charging Scheduling of Clustered Wireless Rechargeable Sensor Networks Considering Dynamic Selection of Cluster Heads

For the wide-coverage application scenarios, wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring, and use the mobile charger (MC) to support the sustainable charging of the network. Many efforts focus on optimizing the cluster head selection and mobile charger scheduling to improve the network energy efficiency and reliability. However, the existing work tends to use fixed triggering mechanism for cluster head (CH) rotation, and may trigger the rotation either too early or too late. Besides, the existing charging triggering mechanisms cannot track the changes in network topology in real time. As a result, both the network energy efficiency and the node failure rate degenerate correspondingly. To solve these problems, this work proposes a dynamic cluster head selection algorithm (DCHSA), which evaluates potential candidate CH sets based on the energy consumption, remaining energy and topological structure, and then select a new CH within this set based on the CH rotation energy consumption and the candidate CH evaluation mechanism. Furthermore, an adaptive dual-threshold selection algorithm based on dynamic energy consumption (ADTSA-DEC) is proposed to determine the set of requiring charging nodes and the trigger time for charging scheduling. The particle swarm optimization is then employed to implement the charging scheduling. Finally, extensive simulations validate that the newly proposed algorithms have outstanding accuracy and robustness in improving overall network energy efficiency and node survivability compared with existing methods.