Energy efficient multi hop clustering using Artificial Bee Colony metaheuristic in WSN

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) face significant energy constraints due to limited battery life.
• Efficient data gathering and transmission are crucial for WSN applications like environmental monitoring and surveillance.
• Existing protocols struggle with energy efficiency and network longevity.

Purpose of the Study:

• To propose EEM-LEACH-ABC, a novel energy-efficient clustering and routing protocol for WSNs.
• To enhance the network lifetime and performance of WSNs.
• To address energy imbalance and improve data delivery.

Main Methods:

• Developed EEM-LEACH-ABC protocol integrating region-based energy-aware clustering, optimized multi-hop paths, and hierarchical data aggregation.
• Utilized the Artificial Bee Colony (ABC) algorithm for dynamic selection of Cluster Heads (CHs) and routing paths.
• Optimized parameters including residual energy, transmission distance, and multi-objective weighting coefficients.

Main Results:

• EEM-LEACH-ABC demonstrated superior performance over existing protocols (MHCRP, SBOA, HChOA) in simulations.
• Achieved up to 216% improvement in First Node Death (FND) and a 29% increase in Packet Delivery Ratio (PDR).
• Showcased robustness and scalability by adapting to interference, node failures, and mobile nodes.

Conclusions:

• EEM-LEACH-ABC significantly extends WSN network lifetime and improves energy efficiency.
• The protocol offers a robust and scalable solution for real-world WSN deployments.
• Dynamic optimization using ABC effectively minimizes energy imbalance and maximizes network longevity.