Radius of Robust Feasibility for Ground Coverage in Aerial Sensor Networks

Abstract

Sensors are vital for environmental monitoring, yet their effectiveness diminishes under spatial uncertainty. We propose a robust optimization framework for maximizing the coverage of aerial directional sensors under spatial uncertainty. Each sensor projects a truncated sector on the ground, parameterized by its altitude, field of view, and orientation. To address sensor displacement uncertainty, we introduce the radius of robust feasibility (RRF) as a measure of tolerance against positional perturbations. We formulate an exact expression for the RRF of aerial sensor networks and embed it into the coverage maximization model as a robustness constraint. Our approach guarantees that the optimized configuration remains feasible under bounded uncertainty. A distributed greedy algorithm based on Voronoi partitioning is used for orientation adjustment, ensuring scalable and adaptive deployment toward high-impact regions. Experimental results validate the effectiveness of our model in preserving robust coverage across complex terrain and varying uncertainty conditions.