Minimizing the energy depletion in wireless rechargeable sensor networks using bi-level metaheuristic charging schemes

TL;DR

This paper presents a bi-level metaheuristic charging scheme for wireless rechargeable sensor networks that optimizes both charging path and time to reduce energy depletion and improve performance.

Contribution

It introduces a novel partial charging approach with a bi-level optimization framework, combining multiple algorithms to enhance charging efficiency in WRSNs.

Findings

Proposed algorithms outperform existing methods in various scenarios.

Bi-level optimization effectively balances charging path and time.

Partial charging reduces sensor energy depletion.

Abstract

Recently, Wireless Rechargeable Sensor Networks (WRSNs) that leveraged the advantage of wireless energy transfer technology have opened a promising opportunity in solving the limited energy issue. However, an ineffective charging strategy may reduce the charging performance. Although many practical charging algorithms have been introduced, these studies mainly focus on optimizing the charging path with a fully charging approach. This approach may lead to the death of a series of sensors due to their extended charging latency. This paper introduces a novel partial charging approach that follows a bi-level optimized scheme to minimize energy depletion in WRSNs. We aim at optimizing simultaneously two factors: the charging path and time. To accomplish this, we first formulate a mathematical model of the investigated problem. We then propose two approximate algorithms in which the optimization of the charging path and the charging time are considered as the upper and lower level, respectively. The first algorithm combines a Multi-start Local Search method and a Genetic Algorithm to find a solution. The second algorithm adopts a nested approach that utilizes the advantages of the Multitasking and Covariance Matrix Adaptation Evolutionary Strategies. Experimental validations on various network scenarios demonstrate that our proposed algorithms outperform the existing works.