165 research outputs found
Hardness and Approximation of Submodular Minimum Linear Ordering Problems
The minimum linear ordering problem (MLOP) generalizes well-known
combinatorial optimization problems such as minimum linear arrangement and
minimum sum set cover. MLOP seeks to minimize an aggregated cost due
to an ordering of the items (say ), i.e., , where is the set of items
mapped by to indices . Despite an extensive literature on MLOP
variants and approximations for these, it was unclear whether the graphic
matroid MLOP was NP-hard. We settle this question through non-trivial
reductions from mininimum latency vertex cover and minimum sum vertex cover
problems. We further propose a new combinatorial algorithm for approximating
monotone submodular MLOP, using the theory of principal partitions. This is in
contrast to the rounding algorithm by Iwata, Tetali, and Tripathi [ITT2012],
using Lov\'asz extension of submodular functions. We show a
-approximation for monotone submodular MLOP where
satisfies . Our theory provides new approximation bounds for special cases of the
problem, in particular a -approximation for the
matroid MLOP, where is the rank function of a matroid. We further show
that minimum latency vertex cover (MLVC) is -approximable, by
which we also lower bound the integrality gap of its natural LP relaxation,
which might be of independent interest
Efficient Data Collection in Multimedia Vehicular Sensing Platforms
Vehicles provide an ideal platform for urban sensing applications, as they
can be equipped with all kinds of sensing devices that can continuously monitor
the environment around the travelling vehicle. In this work we are particularly
concerned with the use of vehicles as building blocks of a multimedia mobile
sensor system able to capture camera snapshots of the streets to support
traffic monitoring and urban surveillance tasks. However, cameras are high
data-rate sensors while wireless infrastructures used for vehicular
communications may face performance constraints. Thus, data redundancy
mitigation is of paramount importance in such systems. To address this issue in
this paper we exploit sub-modular optimisation techniques to design efficient
and robust data collection schemes for multimedia vehicular sensor networks. We
also explore an alternative approach for data collection that operates on
longer time scales and relies only on localised decisions rather than
centralised computations. We use network simulations with realistic vehicular
mobility patterns to verify the performance gains of our proposed schemes
compared to a baseline solution that ignores data redundancy. Simulation
results show that our data collection techniques can ensure a more accurate
coverage of the road network while significantly reducing the amount of
transferred data
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