A Coverage Optimization Approach for Wireless Sensor Networks Using Swarm Intelligence Optimization.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) face coverage optimization challenges, including local optima in traditional algorithms and slow convergence in dynamic scenarios.
• Existing methods struggle with 'coverage holes' and real-time maintenance of high coverage due to imbalanced node energy consumption.

Purpose of the Study:

• To enhance the Flamingo Search Optimization Algorithm (FSA) for improved WSN coverage optimization.
• To address limitations of traditional algorithms, such as susceptibility to local optima and slow convergence in dynamic WSN environments.

Main Methods:

• Integration of an elite opposition-based learning strategy and stagewise step-size control into the FSA.
• Introduction of a cosine variation factor with stagewise step-size control to escape local optima during later iterations.
• Application of the improved FSA to optimize sensing node deployment using coverage rate as the fitness function and chaotic sequences for initialization.

Main Results:

• The improved FSA demonstrated coverage rate increases of 7.48% (100 iterations) and 5.68% (200 iterations) compared to the original FSA.
• The enhanced algorithm effectively breaks free from local optima, especially in later iteration stages.
• Optimized node deployment using the improved FSA significantly boosts overall sensor network coverage.

Conclusions:

• The enhanced FSA offers a superior approach to WSN coverage optimization, outperforming the original FSA and benchmark algorithms.
• The proposed strategies effectively improve algorithm performance, convergence speed, and the ability to avoid local optima.
• This research provides a robust method for optimizing sensing node deployment to achieve higher network coverage rates in WSNs.