Cluster based WSN routing with MOGA and LSA optimization

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are crucial for environmental monitoring and industrial automation but face energy limitations.
• Existing clustering and routing methods struggle to balance energy efficiency, network lifetime, and quality of service.

Purpose of the Study:

• To develop a new optimization method for WSNs that overcomes the limitations of current energy-efficiency and routing techniques.
• To improve network lifetime, data delivery rates, and reduce energy consumption in large-scale WSNs.

Main Methods:

• A hybrid optimization approach combining Multi-Objective Genetic Algorithm (MOGA) and Lightning Search Algorithm (LSA).
• MOGA is used for minimizing intra-cluster distances and energy consumption, selecting optimal Cluster Heads (CHs).
• LSA is integrated for efficiently identifying globally optimal routes for data transmission.

Main Results:

• The proposed MOGA-LSA method decreased energy consumption by 48% compared to MOGAGSA, LEACH, and PSO.
• Demonstrated improvements in data delivery rates and extended network lifetime.
• Simulation results show efficient performance in energy consumption, packet loss, and end-to-end delay.

Conclusions:

• Combining metaheuristic algorithms resolves limitations of single algorithms, offering a comprehensive solution for large-scale WSNs.
• The proposed method enhances sustainability and reliability for Internet of Things (IoT) and smart environment applications.