An energy aware routing protocol using mayfly optimization (ERPMO) and TDMA scheduling in wireless sensor networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are vital for data collection but face energy constraints.
• Limited node energy restricts the operational duration of WSNs.
• Efficient routing protocols are essential for sustainable WSN deployment.

Purpose of the Study:

• To propose a novel Energy-Aware Routing Protocol using Mayfly Optimization (ERPMO) for WSNs.
• To enhance network lifetime, energy efficiency, and data delivery in resource-constrained environments.
• To address the challenge of limited energy capacity in sensor nodes.

Main Methods:

• K-means clustering for efficient spatial cluster formation.
• Mayfly Optimization Algorithm (MOA) for optimal cluster head selection based on residual energy, distance, consumption rate, and node density.
• Dynamic sub-cluster formation and Time Division Multiple Access (TDMA) scheduling for load balancing and collision minimization.

Main Results:

• ERPMO achieved a network lifetime of 1285 rounds.
• Packet delivery ratio reached 96.3% with a residual energy of 0.32 J.
• Cluster head selection accuracy was 91.2%, and the fairness index was 0.79.
• ERPMO outperformed existing LEACH, PSO, and GA-based protocols.

Conclusions:

• The ERPMO model offers a compact, energy-efficient, and collision-free routing framework.
• It significantly improves the sustainability and operational longevity of WSNs.
• The integration of MOA, K-means, and TDMA provides a robust solution for WSN energy management.