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

2025-05-22

AI Summary

To address charging delays and premature node failure caused by full-charging policies in wireless rechargeable sensor networks (WRSNs), this paper proposes a two-layer optimized partial-charging strategy. At the upper layer, a hybrid optimization approach combining multi-start local search with genetic algorithms jointly optimizes the charging path. At the lower layer, a nested multi-task cooperative optimization framework integrated with CMA-ES dynamically allocates charging durations per node. This work is the first to embed a partial-charging mechanism within a two-layer metaheuristic architecture, effectively balancing energy supply while significantly mitigating energy-exhaustion risks. Experimental evaluations across diverse network scales and topologies demonstrate that the proposed strategy reduces node energy-depletion rates by 23.6%–41.2%, extends average network lifetime by 37.8%, and improves charging efficiency per unit time relative to state-of-the-art methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Minimizing energy depletion in wireless rechargeable sensor networks
• Optimizing charging path and time via bi-level metaheuristic schemes
• Preventing sensor death with novel partial charging approach

Innovation

• Bi-level optimized partial charging scheme
• Multi-start Local Search with Genetic Algorithm
• Multitasking Covariance Matrix Adaptation Strategy