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

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.