An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization...

Area of Science:

• Computer Science
• Wireless Sensor Networks
• Optimization Algorithms

Background:

• Directional sensor networks (DSNs) face challenges with random node deployment, leading to coverage blind spots and overlaps.
• Limited node energy and premature node death significantly degrade DSN service quality.

Purpose of the Study:

• To propose an optimized approach for area coverage and energy consumption in DSNs.
• To enhance coverage ratio and minimize redundancy using sensing direction rotation.
• To achieve balanced energy consumption across sensor nodes through effective clustering.

Main Methods:

• Developed a multi-objective optimization model for area coverage, focusing on coverage and redundancy ratios.
• Implemented a clustering network strategy for even energy distribution.
• Designed a cluster head selection model considering residual energy and balance.
• Proposed a node-centric cluster formation algorithm using a weight function.
• Utilized an improved adaptive particle swarm optimization (IAPSO) to solve the optimization models.

Main Results:

• The proposed IAPSO approach effectively improves the coverage ratio.
• Redundancy in coverage areas is significantly reduced.
• Energy consumption is balanced across the sensor nodes, extending network lifetime.
• Simulation results confirm superior performance compared to existing methods.

Conclusions:

• The IAPSO-based approach offers a robust solution for optimizing coverage and energy consumption in DSNs.
• This method addresses key limitations of random deployment and energy depletion in sensor networks.
• The findings indicate a significant advancement in DSN performance and reliability.