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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Coverage optimization of wireless sensor network utilizing an improved CS with multi-strategies.

Sen-Yu Yang1, Chang-Feng Chen2, Kai-Qing Zhou3

  • 1School of Communication and Electronic Engineering, Jishou University, Jishou, 416000, China.

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|August 13, 2025
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Summary
This summary is machine-generated.

This study introduces an improved Cuckoo Search algorithm (ICS-MS) to optimize wireless sensor network (WSN) coverage, reducing costs and improving efficiency. The new algorithm enhances node distribution and coverage rates effectively.

Keywords:
CoverageCuckoo search algorithmMarkov chainOptimizationWireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Wireless Sensor Networks (WSNs) face coverage challenges due to high deployment costs and uneven node distribution.
  • Standard Cuckoo Search (CS) algorithm shows promise for optimization but requires enhancements for complex WSN coverage problems.

Purpose of the Study:

  • To propose an improved Cuckoo Search algorithm with multi-strategies (ICS-MS) for enhanced WSN coverage optimization.
  • To address limitations in WSN coverage by improving node distribution and reducing blind zones.

Main Methods:

  • Developed an improved Cuckoo Search algorithm (ICS-MS) incorporating multi-strategies based on Markov chain analysis of standard CS.
  • Evaluated ICS-MS through numerical comparisons with standard CS and its variations on function optimization.
  • Conducted WSN coverage optimization experiments using ICS-MS across various scenarios and node deployments.

Main Results:

  • ICS-MS demonstrated significant improvements in both function optimization and WSN coverage applications.
  • The algorithm showed enhanced robustness, faster convergence, and higher accuracy in high-dimensional optimization.
  • WSN coverage increased by up to 22.21% with ICS-MS, outperforming comparative algorithms and improving node distribution.

Conclusions:

  • The proposed ICS-MS algorithm effectively optimizes WSN coverage, leading to higher coverage rates and reduced deployment costs.
  • ICS-MS offers improved stability, more uniform node distribution, and less randomness in optimization compared to standard algorithms.
  • The multi-strategy approach in ICS-MS proves beneficial for complex optimization tasks in WSNs.