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Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network.

Haolin Liu1,2,3, Qingyong Deng4, Shujuan Tian5

  • 1College of Information Engineering, Xiangtan University, Xiangtan 411105, China. liuhaolin@xtu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a new algorithm for mobile chargers in wireless rechargeable sensor networks (WRSNs) to optimize charging routes. The method prioritizes nodes based on connectivity and energy needs, reducing data loss and improving network performance.

Keywords:
criticality indexheuristic algorithmmobile chargerrecharge schedulewireless rechargeable sensor network

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Area of Science:

  • Wireless communication networks
  • Sensor networks
  • Energy harvesting

Background:

  • Wireless Power Transfer (WPT) enables perpetual operation of Wireless Rechargeable Sensor Networks (WRSNs).
  • Mobile chargers in WRSNs face routing challenges due to limited travel distance and speed, potentially leading to network service delays.
  • Efficient mobile charger scheduling is critical for maintaining network Quality of Service (QoS) and preventing data loss.

Purpose of the Study:

  • To propose a mobile charger scheduling algorithm for WRSNs that mitigates data loss.
  • To optimize charging routes by considering node criticality in terms of connectivity and energy status.
  • To maximize the total weighted criticality index of nodes within the mobile charger's travel constraints.

Main Methods:

  • Introduced a 'criticality index' based on neighbor dissimilarity to measure connectivity contribution.
  • Developed an 'energy criticality' indicator (normalized consumed energy) to weigh the criticality index.
  • Formulated an optimization problem to maximize weighted criticality indexes, subject to travel distance constraints.
  • Proposed a heuristic algorithm involving spanning tree growing, tour construction, and tour improvement to solve the NP-hard problem.

Main Results:

  • The proposed heuristic algorithm effectively constructs an optimized charging tour for the mobile charger.
  • The algorithm considers both node connectivity importance and energy demand for scheduling.
  • Comparative analysis shows the proposed algorithm is a promising solution compared to existing methods.

Conclusions:

  • The developed scheduling algorithm enhances WRSN performance by prioritizing critical nodes for wireless charging.
  • The criticality index and energy criticality metrics provide a robust approach to mobile charger routing.
  • This research offers a valuable method for improving QoS and reducing data loss in WRSNs.