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Synthesized mobility traces have widely been used to evaluate vehicular networks performance.
The mobility traces have been obtained using VanetMobiSim [25] and plugged in the ns-2 network simulator.
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In addition, about 9% of pathways in the late-branching cyanobacteria have mobility traces and/or are located on plasmids and show a deviation in their dinucleotide signature.
Altogether, 126 clusters have mobility traces and/or are located on plasmids, among which 37 clusters showed also an atypical dinucleotide signature.
We used a realistic mobility model generated by MOVE [19], which is built on top of the micro-traffic simulator SUMO [20] that has the most realistic mobility traces for VANETs simulations [21].
Secondly, we have evaluated the uniqueness of GPS mobility traces using three popular datasets.
This dataset has been recently used by Piorkowski et al. [19] to show that certain macroscopic characteristics specific to clustered mobile wireless networks are prevalent in real mobility traces.
In general, a limited number of spatial points is required to uniquely identify individuals, without having the points to be classified as part of the given mobility traces used for training.
In this paper we have introduced a series of techniques for the analysis and identification of GPS mobility traces.
The joint study of mobility and communication networks has also highlighted that their interplay depends on the spatial granularity of the mobility traces and on the covered area.
Sricharan and Vaidehi [37] examine real-time mobility traces and identify key mobility parameters.
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