14,860 research outputs found
Structural Changes in Data Communication in Wireless Sensor Networks
Wireless sensor networks are an important technology for making distributed
autonomous measures in hostile or inaccessible environments. Among the
challenges they pose, the way data travel among them is a relevant issue since
their structure is quite dynamic. The operational topology of such devices can
often be described by complex networks. In this work, we assess the variation
of measures commonly employed in the complex networks literature applied to
wireless sensor networks. Four data communication strategies were considered:
geometric, random, small-world, and scale-free models, along with the shortest
path length measure. The sensitivity of this measure was analyzed with respect
to the following perturbations: insertion and removal of nodes in the geometric
strategy; and insertion, removal and rewiring of links in the other models. The
assessment was performed using the normalized Kullback-Leibler divergence and
Hellinger distance quantifiers, both deriving from the Information Theory
framework. The results reveal that the shortest path length is sensitive to
perturbations.Comment: 12 pages, 4 figures, Central European Journal of Physic
CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network
Mobile phone data have recently become an attractive source of information
about mobility behavior. Since cell phone data can be captured in a passive way
for a large user population, they can be harnessed to collect well-sampled
mobility information. In this paper, we propose CT-Mapper, an unsupervised
algorithm that enables the mapping of mobile phone traces over a multimodal
transport network. One of the main strengths of CT-Mapper is its capability to
map noisy sparse cellular multimodal trajectories over a multilayer
transportation network where the layers have different physical properties and
not only to map trajectories associated with a single layer. Such a network is
modeled by a large multilayer graph in which the nodes correspond to
metro/train stations or road intersections and edges correspond to connections
between them. The mapping problem is modeled by an unsupervised HMM where the
observations correspond to sparse user mobile trajectories and the hidden
states to the multilayer graph nodes. The HMM is unsupervised as the transition
and emission probabilities are inferred using respectively the physical
transportation properties and the information on the spatial coverage of
antenna base stations. To evaluate CT-Mapper we collected cellular traces with
their corresponding GPS trajectories for a group of volunteer users in Paris
and vicinity (France). We show that CT-Mapper is able to accurately retrieve
the real cell phone user paths despite the sparsity of the observed trace
trajectories. Furthermore our transition probability model is up to 20% more
accurate than other naive models.Comment: Under revision in Computer Communication Journa
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