A Radius of Robust Feasibility Approach to Directional Sensors in Uncertain Terrain

TL;DR

This paper introduces a novel method to enhance coverage and robustness of directional sensor networks in uncertain terrains by calculating a radius of robust feasibility and employing a distributed greedy algorithm.

Contribution

It provides an exact formula for the radius of robust feasibility and integrates it with sensor orientation strategies to improve coverage under uncertainty.

Findings

The proposed model effectively maximizes coverage in uncertain environments.

The distributed greedy algorithm adapts well to dynamic conditions.

Experimental results show improved robustness and efficiency.

Abstract

A sensor has the ability to probe its surroundings. However, uncertainties in its exact location can significantly compromise its sensing performance. The radius of robust feasibility defines the maximum range within which robust feasibility is ensured. This work introduces a novel approach integrating it with the directional sensor networks to enhance coverage using a distributed greedy algorithm. In particular, we provide an exact formula for the radius of robust feasibility of sensors in a directional sensor network. The proposed model strategically orients the sensors in regions with high coverage potential, accounting for robustness in the face of uncertainty. We analyze the algorithm's adaptability in dynamic environments, demonstrating its ability to enhance efficiency and robustness. Experimental results validate its efficacy in maximizing coverage and optimizing sensor orientations, highlighting its practical advantages for real-world scenarios.