On balanced k-coverage in visual sensor networks

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 (CG kCA) to approximate the formulations. Finally, through rigorous simulation experiments we show the efficacy of the proposed formulations and the CG kCA. (C) 2016 Elsevier Ltd. All rights reserved.