Towards Intelligent Decision Making for Charging Scheduling in Rechargeable Wireless Sensor Networks

Abstract

Wireless energy transfer (WET) technology has been proven to mitigate the energy shortage challenge faced by the Internet of Things (IoT), which encompasses sensor networks. Exploiting a Mobile Charger (MC) to energize critical sensors provides a new dimension to maintain continual network operations. Still, existing solutions are not robust as they suffer from high charging delays at the sensor end due to inefficient scheduling. Moreover, charging efficiency is degraded in those schemes due to fixed charging thresholds and ignoring scheduling feasibility conditions. Thus, intelligent scheduling for an MC is needed based on decision-making through multiple network performance-affecting attributes, but blending multiple attributes together for wise scheduling decision-making remains challenging, which is overlooked in previous research. Fortunately, Multi-Criteria Decision Making (MCDM) is best-fit herein for considering numerous attributes and picking the most suitable sensor node to charge next. To this end, we have proposed solving the scheduling problem by combining two MCDM techniques, i.e., Combinative Distance Based Assessment (CODAS) and the Best Worst Method (BWM). The attributes used for the decision are the distance to MC, energy consumption rate, the remaining energy of nodes, and neighborhood criticality. The relative weights of all considered network attributes are calculated by BWM, which is followed by CODAS to select the most appropriate node to be charged next. To make the scheme more realistic and practical in time-critical applications, the dynamic threshold of nodes is calculated along with formulation scheduling feasibility conditions. Simulation results demonstrate the efficiency of the proposed scheme over the competing approaches on various performance parameters.