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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning.

Phi Le Nguyen1, Van Quan La1, Anh Duy Nguyen1

  • 1The School of Information and Communication Technology, Hanoi University of Science and Technology, Ha Noi 11615, Vietnam.

Sensors (Basel, Switzerland)
|August 28, 2021
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Summary

This study introduces Fuzzy Q-charging, an algorithm for wireless rechargeable sensor networks (WRSNs). It optimizes mobile charger routes and energy delivery to significantly extend network operational time.

Keywords:
Q-learningWRSNconnectivityon-demand charging algorithmtarget coverage

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communication

Background:

  • Wireless rechargeable sensor networks (WRSNs) require efficient energy management for mobile chargers (MCs).
  • Optimizing MC charging strategies is crucial for prolonging network lifetime, defined by continuous target monitoring.

Purpose of the Study:

  • To introduce Fuzzy Q-charging, a novel on-demand algorithm for MCs in WRSNs.
  • To maximize network lifetime by optimizing MC charging time and location.
  • To enhance sensor node energy compensation and ensure continuous monitoring.

Main Methods:

  • Fuzzy logic is employed to determine optimal charging energy amounts for sensors.
  • A method is proposed to find the optimal charging time at each location.
  • Q-learning is utilized to select the next charging location, prioritizing sensor roles.

Main Results:

  • Fuzzy Q-charging guarantees infinite network lifetime in WRSNs with sufficient sensors or targets.
  • In other scenarios, it extends monitoring time by an average of 6.8x and up to 33.9x compared to existing algorithms.

Conclusions:

  • Fuzzy Q-charging significantly outperforms existing algorithms in prolonging WRSN operational duration.
  • The algorithm offers a robust solution for energy management and network lifetime extension in WRSNs.