On Balanced k-coverage in Visual Sensor Network

TL;DR

This paper addresses the challenge of achieving balanced k-coverage in visual sensor networks by proposing new optimization formulations and a greedy algorithm to ensure fair coverage of all targets with minimal sensors.

Contribution

It introduces Integer Quadratic and Non-Linear Programming models for balanced k-coverage and a fast greedy algorithm as practical approximations.

Findings

Proposed formulations effectively balance target coverage.

The greedy algorithm provides a computationally efficient approximation.

Simulation results demonstrate the superiority of the methods in coverage balancing.

Abstract

Given a set of directional visual sensors, the $k$-coverage problem determines the orientation of minimal directional sensors so that each target is covered at least $k$ times. As the problem is NP-complete, a number of heuristics have been devised to tackle the issue. However, the existing heuristics provide imbalance coverage of the targets--some targets are covered $k$ times while others are left totally uncovered or singly covered. The coverage imbalance is more serious in under-provisioned networks where there do not exist enough sensors to cover all the targets $k$ times. Therefore, we address the problem of covering each target at least $k$ times in a balanced way using minimum number of sensors. We study the existing Integer Linear Programming (ILP) formulation for single coverage and extend the idea for $k$-coverage. However, the extension does not balance the coverage of the targets. We further propose Integer Quadratic Programming (IQP) and Integer Non-Linear Programming (INLP) formulations that are capable of addressing the coverage balancing. As the proposed formulations are computationally expensive, we devise a faster Centralized Greedy $k$-Coverage Algorithm (CGkCA) to approximate the formulations. Finally, through rigorous simulation experiments we show the efficacy of the proposed formulations and the CGkCA.