Probabilistic Coverage Methods in People-Centric Sensing

Abstract

Aiming to achieve sensing coverage for given Areas of Interest (AoI) over time at low cost in a People-Centric Sensing manner, we propose a concept of (α, T)-coverage of a target field where each point in the field is sensed by at least one mobile node with the probability of at least α during time period T. Our goal is to achieve (α, T)-coverage of a given AoI by a minimal set of mobile nodes. In this paper, we propose two algorithms: inter-location algorithm that selects a minimal number of mobile nodes from nodes inside the AoI considering the distance between them and inter-meeting-time algorithm that selects nodes regarding the expected meeting time between the nodes. To cope with the case that there is an insufficient number of nodes inside the AoI, we propose an extended algorithm which regards nodes inside and outside the AoI. To improve the accuracy of the proposed algorithms, we also propose an updating mechanism which adapts the number of selected nodes based on their latest locations during the time period T. In our simulation-based performance evaluation, our algorithms achieved (α, T)-coverage with good accuracy for various values of α, T, AoI size, and moving probability.