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Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network.

Sundararaj Suganthi1, Nagappan Umapathi2, Miroslav Mahdal3

  • 1Department of Computer and Communication, Sri Sairam Institute of Technology, Chennai 600044, India.

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|March 10, 2022
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Summary

This study introduces an MSO-Tabu approach for energy-efficient data gathering in Wireless Sensor Networks (WSNs). The method optimizes cluster head selection, improving network lifetime and reducing packet loss.

Keywords:
cluster head (CH)energy consumptionmetaheuristicsparticle swarm optimization (PSO)wireless energy transfer

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Area of Science:

  • Computer Science
  • Network Engineering
  • Algorithm Optimization

Background:

  • Wireless Sensor Networks (WSNs) face significant challenges in energy efficiency due to limited sensor node power.
  • Effective data gathering algorithms are crucial for extending WSN lifespan and performance.
  • Clustering-based techniques and cluster head optimization are key areas for energy saving in WSNs.

Purpose of the Study:

  • To develop an energy-efficient data gathering algorithm for large-scale WSNs.
  • To optimize the selection of cluster heads (CHs) using a novel hybrid approach.
  • To enhance network lifespan and reduce packet loss through improved routing.

Main Methods:

  • The proposed technique combines Multi Swarm Optimization (MSO), specifically multi-Particle Swarm Optimization (multi-PSO), with Tabu Search (TS).
  • This hybrid MSO-Tabu approach is designed to efficiently select optimal cluster heads.
  • The method focuses on optimizing routing paths to conserve energy.

Main Results:

  • The MSO-Tabu approach resulted in a higher number of clusters formed compared to GA, DE, Tabu, and MSO methods.
  • It demonstrated a significantly lower average packet loss rate across comparisons.
  • The MSO-Tabu approach showed substantial improvements in network lifetime computation and average dissipated energy.

Conclusions:

  • The MSO-Tabu approach is an efficient method for energy-efficient data gathering in WSNs.
  • It effectively enhances cluster formation, network lifetime, and energy dissipation.
  • The method successfully reduces mean packet loss and end-to-end delay.