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EEL-GA: An Evolutionary Clustering Framework for Energy-Efficient 3D Wireless Sensor Networks in Smart Forestry.

Faryal Batool1, Kamran Ali1, Aboubaker Lasebae1

  • 1Department of Computer Science, Middlesex University, London NW4 4BT, UK.

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Summary

This study introduces EEL-GA, an energy-efficient protocol for Wireless Sensor Networks (WSNs) in 3D environments. EEL-GA optimizes cluster head selection using a genetic algorithm, significantly enhancing network lifetime and data delivery for applications like smart forestry.

Keywords:
3D wireless sensor networksEEL-GALEACHcluster head selectioncoverage optimizationenergy-aware clusteringforest monitoringgenetic algorithmresidual energy

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Area of Science:

  • Computer Science
  • Network Engineering
  • Environmental Monitoring

Background:

  • Wireless Sensor Networks (WSNs) are crucial for monitoring complex 3D environments, demanding high energy efficiency and reliable communication.
  • Existing protocols often struggle with the energy constraints and dynamic conditions inherent in applications like smart forestry.
  • Optimizing cluster head selection is key to prolonging network lifespan and ensuring data integrity.

Purpose of the Study:

  • To introduce and evaluate EEL-GA, an Energy Efficient LEACH-based clustering protocol optimized via a Genetic Algorithm.
  • To enhance cluster head selection using a dual-metric fitness function that considers residual energy and intra-cluster distance.
  • To assess the performance of EEL-GA against other optimization techniques in terms of energy efficiency, packet delivery, and network lifetime.

Main Methods:

  • Developed EEL-GA, a novel clustering protocol for Wireless Sensor Networks (WSNs).
  • Employed a Genetic Algorithm (GA) to optimize the selection of cluster heads based on residual energy and intra-cluster distance.
  • Compared EEL-GA with variants optimized by Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithms.
  • Utilized simulations with real environmental data to evaluate performance metrics.

Main Results:

  • EEL-GA demonstrated superior performance in sustaining network energy compared to PSO, DE, and ABC variants.
  • The protocol achieved significant improvements in packet delivery ratio and reduced communication delay.
  • EEL-GA extended the overall cluster lifetime, indicating enhanced network stability and longevity.
  • Incorporation of environmental dynamics (temperature, humidity) improved the model's real-world applicability.

Conclusions:

  • EEL-GA offers a scalable and adaptive solution for energy-aware 3D WSNs, outperforming existing optimization methods.
  • The genetic algorithm-based optimization effectively addresses energy efficiency and reliability challenges in WSNs.
  • EEL-GA is particularly suitable for mission-critical applications and smart forestry environments requiring robust monitoring.
  • The protocol's adaptability to environmental factors enhances its practical deployment in complex real-world scenarios.