Sensor Node Deployment Optimization for Continuous Coverage in WSNs.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Optimizing sensor node coverage is a critical challenge in wireless sensor networks (WSNs).
• Traditional optimization methods often suffer from premature convergence and suboptimal solutions, leading to coverage gaps and uneven node distribution.
• Existing approaches struggle to efficiently balance exploration and exploitation for effective WSN deployment.

Purpose of the Study:

• To introduce a novel optimization algorithm, the velocity-scaled adaptive search factor particle swarm optimization (VASF-PSO).
• To enhance population diversity and guide the search process more effectively in WSN coverage optimization.
• To reduce uncovered areas and improve overall network coverage performance.

Main Methods:

• Development of the velocity-scaled adaptive search factor particle swarm optimization (VASF-PSO) algorithm.
• Integration of dynamic mechanisms within the VASF-PSO to improve population diversity and search guidance.
• Extensive simulations evaluating VASF-PSO across diverse WSN deployment scenarios (varying node densities, sensing ranges, and area sizes).

Main Results:

• The VASF-PSO algorithm demonstrates superior performance compared to several widely used metaheuristic algorithms.
• Achieved faster convergence rates and better global exploration capabilities.
• Significantly improved coverage performance, yielding up to 14.71% higher coverage rates on average compared to baseline techniques.

Conclusions:

• The proposed VASF-PSO algorithm offers a robust and efficient solution for optimizing sensor node coverage in WSNs.
• The dynamic mechanisms integrated into VASF-PSO effectively address premature convergence and suboptimal solutions.
• The algorithm's scalability and improved coverage performance make it suitable for practical WSN deployments.