Optimal and Dynamic Scheduling using Multiple Mobile Chargers in Rechargeable Sensor Networks: An MADM-based Approach

In Wireless Rechargeable Sensor Networks (WRSN), wireless energy transfer procedures are provided to recharge sensor batteries using chargers. WRSN provide energy replacement and prolong network lifetime to the sensors. Most of the earlier work focussed on sensor node’s sensing task instead of charging utilities. So, an efficient partitioning approach is required to lower the cost of network deployment, to reduce the energy consumption rate and to increase the lifespan of network. Initially, to reduce the energy consumption rate, deadline and penalty values of nodes are computed. Then, considering the criticality of sensor nodes accurate number of multiple mobile chargers are deployed in the network in such a way that the network is partitioned into several number of subregions. Then, use of on-demand recharging and multi-node recharging, is the most efficient way of charging numerous sensor nodes at a time which results in the increases in the lifespan of network. Finally, using Multiple Attribute Decision Making (MADM) approach the scheduling of sensor node is achieved. The usefulness of our scheme is then demonstrated through comprehensive simulations. The results show that the proposed technique reduces charging latency by 23.52 percent, increases network lifetime by 72.35 percent and charging efficiency is increased by 80.90 percent.