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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm.

Junfeng Chen1, Samson H Sackey1, James Adu Ansere1

  • 1College of Internet of Things Engineering, Hohai University, Changzhou 213022, China.

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This summary is machine-generated.

Accurate sensor localization in wireless sensor networks (WSNs) is improved using a novel neighborhood grid cluster approach. This method enhances energy efficiency, node lifetime, and overall network coverage.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Algorithm Design

Background:

  • Accurate sensor localization is critical for wireless sensor networks (WSNs), especially over large geographical areas.
  • Existing clustering techniques often face challenges in scalability and efficiency for wide-region deployments.
  • Evolutionary algorithms offer potential for optimizing complex network problems like localization.

Purpose of the Study:

  • To develop an efficient and accurate localization method for wireless sensor networks (WSNs).
  • To introduce a neighborhood grid cluster approach combined with a genetic algorithm for improved localization.
  • To enhance network performance metrics including energy consumption, connectivity, and node longevity.

Main Methods:

  • Implementation of a neighborhood grid cluster structure with designated cluster centers within each grid.
  • Development of a localization algorithm utilizing the genetic algorithm to estimate target areas based on minimal distances.
  • Definition of fitness functions incorporating energy, connectivity, and distance metrics to evaluate the localization accuracy.

Main Results:

  • The proposed neighborhood grid cluster approach significantly enhances localization accuracy.
  • Improved energy utilization and extended node lifetime were observed in simulation results.
  • The genetic algorithm-based localization demonstrated superior performance in terms of coverage and efficiency.

Conclusions:

  • The neighborhood grid cluster and genetic algorithm-based localization method is a viable solution for WSNs.
  • This approach effectively addresses the challenge of sensor localization in wide-region networks.
  • The study confirms significant improvements in key performance indicators for WSNs.