Probabilistic Methods for Spatio-Temporal Coverage in People-Centric Sensing

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People-Centric Sensing (PCS) is a new paradigm for gathering environmental information of a specified region in the urban district. This paradigm relies on uncontrolled mobility of people to achieve sensing coverage for a given areas of Interest (AoI) over time at low cost. In this paper, we propose a concept of (α, T)-coverage of the target field where each point in the field is sensed by at least one node at probability of at least α during time period T. Our goal is to achieve (α, T)-coverage by as small number of mobile sensor nodes as possible for a given AoI, coverage ratio α, and time period T. We model an urban sensing scenario with pedestrians as mobile sensor nodes moving according to a discrete Markov model. Based on this model, we propose two heuristic algorithms: inter-location and inter-meetingtime based algorithms, to meet a coverage ratio α and time period T. These algorithms estimate the expected coverage of the specified AoI for a set of selected nodes. The former algorithm selects some of the mobile sensor nodes inside the AoI taking into account the distance between them. The latter selects some of the nodes taking into account the expected meeting time between them. We conduct a simulation study to evaluate the performance of proposed algorithms for various parameter settings. Through simulation experiments, we confirmed that our algorithms achieve (α, T)-coverage with good accuracy for various values of α, T, and AoI size.
2010-08-26

Probabilistic Methods for Spatio-Temporal Coverage in People-Centric Sensing

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