{"ID":2850979,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20213","arxiv_id":"2510.20213","title":"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.","short_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 altitu...","url_abs":"https://arxiv.org/abs/2510.20213","url_pdf":"https://arxiv.org/pdf/2510.20213v1","authors":"[\"Vanshika Datta\",\"C. Nahak\"]","published":"2025-10-23T05:02:28Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}