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Petri-Net-Based Charging Scheduling Optimization in Rechargeable Sensor Networks.

Huaiyu Qin1, Wei Ding1, Lei Xu1

  • 1School of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China.

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|October 16, 2024
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Summary
This summary is machine-generated.

A new Mobile Transition Sequence Hybrid Ant Colony Optimization (MTS-HACO) method improves wireless rechargeable sensor network charging benefits. This approach enhances energy flow management and mobile vehicle charging efficiency, significantly boosting network performance.

Keywords:
Petri netant colony algorithmcharging benefitprecision agriculturewireless rechargeable sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless rechargeable sensor networks (WRSNs) face challenges in energy management and mobile vehicle (MV) charging optimization.
  • Accurate expression of energy, motion, and control flow is crucial for efficient WRSN operation.
  • Maximizing charging benefits from MVs requires sophisticated optimization strategies.

Purpose of the Study:

  • To propose an optimized method for managing energy flow and maximizing charging benefits in WRSNs using mobile vehicles.
  • To develop a novel optimization algorithm that enhances the efficiency of wireless charging in sensor networks.
  • To ensure mobile vehicles have sufficient energy for return to the base station after charging.

Main Methods:

  • A Mobile Transition Sequence Hybrid Ant Colony Optimization (MTS-HACO) is proposed, grouping nodes by energy consumption firing time.
  • The Firing Sequence Optimization of Mobile Charging Transition (FSOMCT) problem is formulated with constraints on MV capacity and travel/charging weights.
  • The ant colony algorithm is enhanced with an elite strategy and Max-Min system, and an improved Fireworks Algorithm (FWA) optimizes ant-constructed paths.

Main Results:

  • The MTS-HACO method achieves optimal mobile charging transition firing sequences and charging times.
  • Simulation results demonstrate significant improvements in charging benefit compared to existing algorithms.
  • The proposed method improves charging benefit by approximately 48.7% over the periodic algorithm and 26.3% over the PE-FWA algorithm.

Conclusions:

  • The MTS-HACO algorithm effectively enhances charging benefits and energy flow management in WRSNs.
  • The proposed optimization strategy ensures reliable operation by guaranteeing MVs can return to the base station.
  • This research offers a significant advancement in optimizing mobile charging for wireless sensor networks.