A multi-attribute decision making approach for on-demand charging scheduling in wireless rechargeable sensor networks

Abstract

Mobile charging in wireless rechargeable sensor networks is a well-referenced research problem. Numerous studies have been carried out to determine an efficient charging schedule for mobile charger (MC). However, the problem still remains challenging as it requires a wise scheduling decision based on the evaluation of various attributes that impact on network performance. In this regard, multi-attribute decision making (MADM) may be an effective approach which has shown great potential to solve complex decision making problems by coordinating multiple attributes, but has not been explored by existing mobile charging schemes till date. To this end, this paper proposes a novel charging scheme which integrates two popular MADM methods to determine charging schedule by evaluating various network attributes, namely residual energy, distance to MC, energy consumption rate, and neighborhood energy weightage. We take into account both MC’s limited energy and nodes’ uneven energy consumption rates in order to formulate feasibility conditions for scheduling the nodes effectively for further improvement of charging performance. Extensive simulations are performed to illustrate the effectiveness of the proposed scheme. When compared with relevant state-of-the-art methods, the results signify that the proposed scheme boosts charging performance in terms of various performance metrics.