A hybrid Spatial Ant Lion optimization and utilitarian data transmission approach for intelligent optimization for...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) face challenges in energy consumption, delay, and limited network lifetime.
• Optimization techniques are crucial for balancing WSN parameters and prolonging network operational duration.
• Existing schemes like Particle Swarm Optimization (PSO), Ant Lion Optimization (ALO), and Simulated Annealing-Adaptive Opposition (SA-AOA) have limitations.

Purpose of the Study:

• To propose a new optimization scheme for wireless sensor networks.
• To enhance network lifetime, throughput, and energy efficiency while minimizing delay.
• To introduce a two-phase approach: optimal cluster head selection and effective data transmission.

Main Methods:

• Phase 1: Optimal Cluster Head Selection using Spatial Ant Lion Optimization (SALO) with nodes categorized as 'ants' and 'antlions'.
• Fitness evaluation for antlions based on retained energy, distance to sink, and distance from ants.
• Phase 2: Utilitarian data transmission algorithm employing single-hop or two-hop transmission based on cluster head to sink distance.

Main Results:

• The proposed SALO scheme achieved a 1.86% increase in throughput compared to existing methods.
• A 2.56% reduction in network delay was observed with the new approach.
• Demonstrated a significant 3.18% improvement in energy efficiency, extending network lifetime.

Conclusions:

• The Spatial Ant Lion optimization scheme effectively balances WSN parameters for improved performance.
• The two-phase approach enhances both cluster head selection and data transmission efficiency.
• The proposed model offers a superior alternative to existing optimization techniques for WSNs.