Mobile Charging Sequence Scheduling in Wireless Rechargeable Sensor Networks using Extended Particle Swarm Optimization

Wireless Sensor Networks (WSNs) are extremely popular nowadays in monitoring the physical environment over a large area. However, in conventional WSNs, the sensor nodes have limited energy. Wireless Rechargeable Sensor Networks (WRSNs) is a modified WSNs utilizing rechargeable sensor nodes. This modification addresses the aforesaid energy constraint. Nevertheless, sensors will discharge over time in the normal course of operation, which will be recharged by a Mobile Charger Vehicle (MCV). Since these sensors may deplete in any chronological order, they must be recharged in an optimal sequence. This problem is known as Mobile Charger Sequence Scheduling (MCSS). This paper proposes an Extended Particle Swarm Optimization (EPSO) to design a charging sequence schedule for WRSN. The proposed approach schedules charging sequence based on residual energy, location, and density of sensor nodes in the monitoring field. The proposed EPSO algorithm improves the lifetime of the network and also enhances the coverage of the monitoring area. The simulation results show that the proposed EPSO algorithm outperforms existing state-of-the-art algorithms and consistently maintains a higher coverage ratio.