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A Multi-Strategy Improved Sparrow Search Algorithm for Coverage Optimization in a WSN.

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  • 1School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China.

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Summary
This summary is machine-generated.

A new algorithm, IM-DTSSA, enhances wireless sensor network (WSN) coverage by optimizing node placement. This improves monitoring area coverage and reduces node movement, balancing efficiency and energy consumption.

Keywords:
coverage optimizationnon-dominated sortingsparrow search algorithmtwo-sample learning strategywireless sensor network

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Area of Science:

  • Computer Science
  • Network Engineering
  • Optimization Algorithms

Background:

  • Wireless Sensor Networks (WSNs) face challenges with limited monitoring area coverage.
  • Inefficient node deployment leads to excessive node movement and energy waste.

Purpose of the Study:

  • To propose an improved sparrow search algorithm (IM-DTSSA) for WSN coverage optimization.
  • To enhance both the coverage rate and minimize node movement distance.

Main Methods:

  • Utilized Delaunay triangulation for initial population optimization and identifying uncovered areas.
  • Applied non-dominated sorting algorithm to refine the explorer population for better global search.
  • Implemented a two-sample learning strategy to improve follower position updates and escape local optima.

Main Results:

  • IM-DTSSA increased coverage rate by 6.74%, 5.04%, and 3.42% compared to other algorithms.
  • Reduced average node moving distance by 7.93 m, 3.97 m, and 3.09 m respectively.
  • Demonstrated superior performance in balancing coverage and node mobility.

Conclusions:

  • The IM-DTSSA algorithm effectively addresses WSN coverage optimization challenges.
  • Achieves a significant improvement in monitoring area coverage while minimizing node movement.
  • Offers a balanced and efficient solution for WSN deployment.