Efficient node deployment for enhancing coverage and connectivity in Wireless Sensor Networks

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) require efficient deployment for optimal performance.
• Traditional deployment methods struggle with heterogeneous nodes and dynamic environments.
• Key performance metrics include coverage, connectivity, energy efficiency, and network longevity.

Purpose of the Study:

• To propose a novel Particle Swarm Optimization (PSO)-based deployment framework for heterogeneous WSNs.
• To improve coverage quality, minimize energy consumption, and enhance fault tolerance.
• To adapt to dynamic environmental conditions and heterogeneous node capabilities.

Main Methods:

• Development of a PSO-based deployment framework.
• Incorporation of intelligent role assignment and metaheuristic optimization.
• Inclusion of adaptive maintenance phases for network resilience.
• Simulation-based evaluation against nine traditional deployment strategies.

Main Results:

• Achieved superior performance with 91.4% ± 1.8% average coverage.
• Extended operational lifetime beyond 3,400 rounds.
• Demonstrated improved connectivity robustness, scalability, and resilience to node failures.

Conclusions:

• The proposed PSO-based deployment framework is a viable solution for heterogeneous WSNs.
• It offers significant improvements in performance metrics for real-world applications.
• The framework is suitable for large-scale and energy-constrained WSN deployments.