Coverage optimization and node minimization in WSNs: an enhanced hybrid PSO approach with spatial position encoding.

Area of Science:

• Computer Science
• Network Engineering
• Optimization Algorithms

Background:

• Wireless sensor networks (WSNs) are crucial for applications demanding efficient coverage and resource management.
• Optimizing sensor placement and quantity is vital for WSN performance and cost-effectiveness.
• Existing methods often struggle to balance coverage quality with the number of deployed sensors.

Purpose of the Study:

• To develop an enhanced hybrid particle swarm optimization (EHPSO) algorithm for WSN coverage optimization.
• To integrate spatial position encoding (SPE) for improved search guidance and dynamic node adjustment.
• To achieve a balance between maximizing coverage and minimizing sensor deployment in WSNs.

Main Methods:

• Development of the enhanced hybrid particle swarm optimization (EHPSO) algorithm.
• Integration of a spatial position encoding (SPE) strategy within the PSO framework.
• Conducting extensive simulations to compare EHPSO against state-of-the-art algorithms.
• Establishment of a multi-objective optimization model to evaluate performance.

Main Results:

• The EHPSO algorithm demonstrated superior performance in coverage optimization compared to existing methods.
• Significant reduction in the number of deployed sensors was achieved while maintaining high coverage rates.
• The algorithm effectively balanced exploration and exploitation, ensuring robust performance across various scenarios.

Conclusions:

• The proposed EHPSO algorithm offers a more efficient and cost-effective solution for WSN deployment.
• This research provides a valuable contribution to optimizing WSNs, especially in resource-constrained environments.
• The SPE strategy enhances the ability to dynamically adjust node counts for optimal network configuration.