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Enhancing sensor duty cycle in environmental wireless sensor networks using Quantum Evolutionary Golden Jackal

Zhonghua Lu1, Min Tian1, Jie Zhou2

  • 1College of mechanical and electrical engineering, Shihezi University, Shihezi 832000, China.

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Summary
This summary is machine-generated.

Environmental wireless sensor networks (EWSNs) face challenges with battery life and complex duty cycle optimization. This study introduces a novel Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA) to efficiently extend EWSN operational lifespan.

Keywords:
Golden Jackal Optimization Algorithmduty cycle optimizationexploration operatorsnetwork lifetime

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Area of Science:

  • Environmental Science
  • Computer Science
  • Engineering

Background:

  • Environmental wireless sensor networks (EWSNs) are crucial for monitoring applications like gas detection and disaster warning.
  • EWSNs face limitations in sensor battery capacity and data range, often leading to redundant nodes and complex duty cycle design challenges.
  • Duty cycle optimization for EWSNs is an NP-Hard problem, increasing exponentially with node count, making traditional algorithms inefficient.

Purpose of the Study:

  • To address the NP-Hard problem of duty cycle optimization in EWSNs.
  • To propose a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), for efficient EWSN duty cycle optimization.
  • To enhance the operational lifespan and deployment efficiency of EWSNs.

Main Methods:

  • Development of the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA) with new quantum exploration and exploitation operators.
  • Design of a new, accurate, and low-complexity sensor duty cycle model.
  • Comparative simulation analysis against Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA), and Simulated Annealing (SA).

Main Results:

  • QEGJOA significantly improves global search capabilities, effectively handling the complexity of EWSN duty cycle optimization.
  • The proposed algorithm quickly obtains deployment solutions for multi-sensor node scenarios.
  • QEGJOA demonstrated performance improvements of 18.69% over GJO, 20.15% over WOA, and 26.55% over SA.

Conclusions:

  • QEGJOA offers an effective solution for EWSN duty cycle optimization, prolonging network lifetime.
  • The algorithm's enhanced global search ability makes it suitable for complex, large-scale EWSN deployments.
  • The novel sensor duty cycle model contributes to improved accuracy and reduced complexity in EWSN management.