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Spatio-Temporal Coverage Enhancement in Drive-By Sensing Through Utility-Aware Mobile Agent Selection
In recent years, the drive-by sensing paradigm has become increasingly
popular for cost-effective monitoring of urban areas. Drive-by sensing is a
form of crowdsensing wherein sensor-equipped vehicles (aka, mobile agents) are
the primary data gathering agents. Enhancing the efficacy of drive-by sensing
poses many challenges, an important one of which is to select non-dedicated
mobile agents on which a limited number of sensors are to be mounted. This
problem, which we refer to as the mobile-agent selection problem, has a
significant impact on the spatio-temporal coverage of the drive-by sensing
platforms and the resultant datasets. The challenge here is to achieve maximum
spatiotemporal coverage while taking the relative importance levels of
geographical areas into account. In this paper, we address this problem in the
context of the SCOUTS project, the goal of which is to map and analyze the
urban heat island phenomenon accurately.
Our work makes several major technical contributions. First, we delineate a
model for representing the mobile agents selection problem. This model takes
into account the trajectories of the vehicles (public transportation buses in
our case) and the relative importance of the urban regions, and formulates it
as an optimization problem. Second, we provide two algorithms that are based
upon the utility (coverage) values of mobile agents, namely, a hotspot-based
algorithm that limits the search space to important sub-regions and a
utility-aware genetic algorithm that enables the latter algorithm to make
unbiased selections. Third, we design a highly efficient coverage redundancy
minimization algorithm that, at each step, chooses the mobile agent, which
provides maximal improvement to the spatio-temporal coverage. This paper
reports a series of experiments on a real-world dataset from Athens, GA, USA,
to demonstrate the effectiveness of the proposed approaches.Comment: 10 pages, 19 figures, IEEE International Conference on Mobile Data
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