Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Internet of Things (IoT) integration in wireless sensor networks (WSNs) significantly increases energy consumption, challenging network longevity.
• Conventional algorithms focus on energy-efficient routing protocols at the network layer to enhance WSN performance and extend network lifetime.
• Clustering is a key strategy for grouping sensor nodes, with minimizing energy consumption being crucial for maximizing network lifetime.

Purpose of the Study:

• To propose a multi-objective optimization approach for selecting optimal data transmission routes in WSNs.
• To enhance the energy efficiency and prolong the network lifetime of IoT-enabled WSNs.
• To introduce a novel hybrid algorithm, PSOGA, combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for efficient routing.

Main Methods:

• A two-step approach involving a trust model for cluster head (CH) selection.
• Development of a hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA) for optimal route determination.
• Simulation-based validation comparing PSOGA against LEACH and PSO with random routing.

Main Results:

• The proposed PSOGA algorithm demonstrates superior performance over existing methods.
• Achieved significant improvements in energy efficiency and network throughput.
• Showcased a high packet delivery rate and maintained high residual energy throughout simulations.

Conclusions:

• The PSOGA hybrid algorithm effectively enhances energy efficiency and network lifetime in IoT-enabled WSNs.
• The multi-objective optimization approach provides a robust solution for energy-constrained wireless networks.
• The study validates the efficacy of combining PSO and GA for optimizing WSN routing protocols.