Scholarlink

<p>Wireless rechargeable sensor networks (WRSNs) play a pivotal role in enabling long run operations of sensor networks through controlled mobile charging vehicles (MCVs). However, achieving energy efficient and reliable energy restoration remains demanding issues due to dynamic network conditions and heterogeneous energy demands of sensor nodes (SNs). In this paper, a novel energy efficient adaptive multimode scheduling scheme for MCVs has been proposed to recharge the SNs in WRSNs with the help of social group optimization (SGO) technique. The proposed scheduling framework introduces a socially inspired cooperative learning mechanism integrated with dynamic charging mode selection, enabling improved charger scheduling efficiency and balanced energy utilization as compared to existing approaches such as EDF, Heuristic Online, ETLBO, GSA, and DMCP. Extensive simulations have been performed in MATLAB 2020 under varying network conditions and compared with existing approaches. Experimental results demonstrate that the proposed method improves total energy received by approximately 25.62%, 20.85% and 18.70% as compared to EDF, Heuristic, and ETLBO approaches, respectively. Moreover, the proposed framework significantly reduces MCV working time, decreases starvation ratio by about 32–47%, and maintains a high charging success rate under varying network densities. The proposed scheme also reduces MCV moving distance by approximately 6–17% and improves charging utility by nearly 5–13% as compared to existing methods. Furthermore, confidence interval (CI) analysis under repeated simulation runs confirms the statistical stability and reliability of the proposed scheduling scheme.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Energy-Efficient Adaptive Multimode Scheduling for Mobile Chargers in Wireless Rechargeable Sensor Networks Using Novel Social Group Optimization Technique

  • 期刊论文

  • 发表日期：

    2026年7月17日

查看全文
  • Md. Kamaruzzaman,
  • Abhijit Chandra

摘要

Wireless rechargeable sensor networks (WRSNs) play a pivotal role in enabling long run operations of sensor networks through controlled mobile charging vehicles (MCVs). However, achieving energy efficient and reliable energy restoration remains demanding issues due to dynamic network conditions and heterogeneous energy demands of sensor nodes (SNs). In this paper, a novel energy efficient adaptive multimode scheduling scheme for MCVs has been proposed to recharge the SNs in WRSNs with the help of social group optimization (SGO) technique. The proposed scheduling framework introduces a socially inspired cooperative learning mechanism integrated with dynamic charging mode selection, enabling improved charger scheduling efficiency and balanced energy utilization as compared to existing approaches such as EDF, Heuristic Online, ETLBO, GSA, and DMCP. Extensive simulations have been performed in MATLAB 2020 under varying network conditions and compared with existing approaches. Experimental results demonstrate that the proposed method improves total energy received by approximately 25.62%, 20.85% and 18.70% as compared to EDF, Heuristic, and ETLBO approaches, respectively. Moreover, the proposed framework significantly reduces MCV working time, decreases starvation ratio by about 32–47%, and maintains a high charging success rate under varying network densities. The proposed scheme also reduces MCV moving distance by approximately 6–17% and improves charging utility by nearly 5–13% as compared to existing methods. Furthermore, confidence interval (CI) analysis under repeated simulation runs confirms the statistical stability and reliability of the proposed scheduling scheme.