Radius of Robust Feasibility for Ground Coverage in Aerial Sensor Networks

TL;DR

This paper introduces a robust optimization framework for aerial sensor networks that maximizes ground coverage while ensuring feasibility under spatial uncertainties, using a novel measure called the radius of robust feasibility.

Contribution

It formulates an exact expression for the radius of robust feasibility and integrates it into coverage optimization, enabling scalable and adaptive deployment under uncertainty.

Findings

The model effectively maintains coverage under terrain complexity.

The distributed algorithm adapts sensor orientation to maximize coverage.

Experimental results confirm robustness against positional perturbations.

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.