A Context-Aware Framework for Sensor Placement Using PSO and Voronoi in Dynamic Scenarios

When we deploy sensors, we face severe problems to achieve resilience and robust coverage. It happens more in stochastic and dynamic environments. In this paper, we propose a hybrid framework which is a combination of Particle Swarm Optimization (PSO) and Voronoi Diagram method with context aware adaptation to fix the challenges. The proposed framework balances initial distribution of sensors and iterative optimization to enhance coverage, maintain k-coverage (k=3) and adapt to evolving scenarios even under critical situations like sensor failures. Experimental results highlight the performance of the proposed framework across the key scenarios: comparing coverage with or without context aware optimization quantifying the impact of random node failures, tracking cumulative recovery attempts and evaluating final coverage percentage. The results reveal that this hybrid framework maximizes the coverage and provides resilience to sensor failures. It also reallocates resources dynamically to maintain optimal deployment. This framework is suitable for getting more efficiency and minimal iterations of recovery while enhancing the robustness of the network. It also enlightens the adaptability in sensor performance and becomes very popular in modern applications.