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MWCSGA-Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network.

Nader Ajmi1, Abdelhamid Helali1, Pascal Lorenz2

  • 1Micro-Optoelectronic and Nanostructures Laboratory (LR99ES29), Faculty of Sciences of Monastir, University of Monastir, Environment Street, 5019 Monastir, Tunisia.

Sensors (Basel, Switzerland)
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Summary

This study introduces a novel multi-weight chicken swarm-based genetic algorithm for energy-efficient clustering in wireless sensor networks (WSNs). The proposed MWCSGA method enhances network performance by optimizing energy consumption and reducing delays.

Keywords:
chicken swarm optimization (CSO), genetic algorithm (GA), energy efficientwireless sensor networks (WSNs), clustering

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are crucial for smart environments, but battery power is a critical limitation.
  • Clustering is a key technique to improve energy efficiency in WSNs.
  • Existing clustering algorithms face challenges in balancing energy consumption and network performance.

Purpose of the Study:

  • To introduce a novel energy-efficient clustering algorithm for WSNs.
  • To enhance the performance of WSNs by optimizing energy consumption and communication efficiency.
  • To address the limitations of existing clustering methods in terms of energy efficiency and network throughput.

Main Methods:

  • A novel Multi-Weight Chicken Swarm based Genetic Algorithm (MWCSGA) for energy-efficient clustering is proposed.
  • The algorithm integrates Chicken Swarm Optimization (CSO) and Genetic Algorithm (GA) for cluster head selection.
  • The MWCSGA model incorporates multi-weight clustering and optimized inter/intra-cluster communication strategies.

Main Results:

  • The proposed MWCSGA method demonstrates superior performance compared to GA-LEACH, MW-LEACH, and CSOGA.
  • MWCSGA achieves significant improvements in energy efficiency.
  • The algorithm also shows enhanced performance in terms of end-to-end delay, packet drop rate, packet delivery ratio, and network throughput.

Conclusions:

  • The MWCSGA algorithm offers a promising solution for energy-efficient clustering in WSNs.
  • This approach effectively addresses the critical issue of battery power limitation in WSN applications.
  • The proposed method provides a robust framework for optimizing WSN performance in smart environments.