A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model

Abstract

This paper presents a distributed gradient-based deployment strategy to maximize coverage in hybrid wireless sensor networks (WSNs) with probabilistic sensing. Leveraging Voronoi partitioning, the overall coverage is reformulated as a sum of local contributions, enabling mobile sensors to optimize their positions using only local information. The strategy adopts the Elfes model to capture detection uncertainty and introduces a dynamic step size based on the gradient of the local coverage, ensuring movements adaptive to regional importance. Obstacle awareness is integrated via visibility constraints, projecting sensor positions to unobstructed paths. A threshold-based decision rule ensures movement occurs only for sufficiently large coverage gains, with convergence achieved when all sensors and their neighbors stop at a local maximum configuration. Simulations demonstrate improved coverage over static deployments, highlighting scalability and practicality for real-world applications.