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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks.

Beom-Su Kim1, Beomkyu Suh1, In Jin Seo2

  • 1Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Q-learning approach to optimize parent node selection in wireless sensor networks, balancing reliability, latency, and energy efficiency for improved network performance.

Keywords:
Q-learningmultiple objectivesreinforcement learningtree-based routingwireless sensor networks (WSNs)

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Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Tree-based routing in wireless sensor networks (WSNs) offers low overhead and high responsiveness.
  • Existing methods struggle to optimize parent node selection due to conflicting metrics like reliability, latency, and energy efficiency.

Purpose of the Study:

  • To develop an optimal parent node selection strategy in WSNs.
  • To balance multiple performance objectives including reliability, latency, and energy efficiency.

Main Methods:

  • Utilized Q-learning to find the optimal parent node in a tree topology.
  • Defined state space, action set, and reward functions based on cognitive metrics.
  • Employed a trial-and-error approach for parent node selection.

Main Results:

  • The proposed Q-learning method effectively balances multiple performance metrics.
  • Achieved superior performance in end-to-end delay, packet delivery ratio, and energy consumption.
  • Demonstrated improved network efficiency compared to existing routing approaches.

Conclusions:

  • Q-learning provides an effective solution for optimal parent node selection in WSNs.
  • The proposed method enhances overall network performance by addressing conflicting objectives.