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A Comparison of Opportunistic Connection Datasets

A Comparison of Opportunistic Connection Datasets

Pedro Vieira (Department of Informatics/Centro Algoritmi, School of Engineering, University of Minho, Braga, Portugal), António Costa (Department of Informatics/Centro Algoritmi, School of Engineering, University of Minho, Braga, Portugal), and Joaquim Macedo (Department of Informatics/Centro Algoritmi, School of Engineering, University of Minho, Braga, Portugal)
Copyright: © 2013 |Pages: 16
DOI: 10.4018/jdst.2013070103
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Abstract

Opportunistic networking differs from conventional architectures in the lack of existing network infrastructure, which can cause intermittent connectivity or increased communication delay between nodes. From a message routing perspective, solving these problems require a different set of techniques than those used in more traditional network schemes. Forwarding algorithms in these scenarios aim to improve performance metrics such as message delivery ratio and message delay time, while trying to keep the number of message copies small. A common approach used for testing the performance of opportunistic protocols relies on existing opportunistic contact traces. These datasets are widely available on the Internet, and provide a convenient way of simulating realistic usage scenarios. As such, studying the contact patterns between nodes can lead to useful observations to take into account in future experiments. This paper presents the results of a study on four different datasets. First, the authors describe the main characteristics of each trace. Then, they propose a graphical representation of the contact behavior for each pair of nodes. Further analysis of the results in terms of connectivity distribution among nodes reveals that contacts follow a roughly lognormal distribution and that there is a small group of nodes in each set which is seemingly much more popular than the rest. Finally, the authors introduce a temporal analysis that was made over the duration of each collection experiment. It was noticeable that individual nodes have repetitive contact patterns over time, apart from some observed cyclic variation over time (namely on weekends). By modeling the data traces as time-varying graphs, a performance decrease was observed with the absence of the most popular nodes.
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Several different approaches have been made with regards to opportunistic dataset analysis and visualization. Belblidia et al. (2010) propose the surround indicator metric, which describes the spatial dimension of a contact in a wireless network; in other words, it indicates the density of nearby nodes in a given network. This metric could be used in conjunction with the more widely used temporal dimension metric as information for opportunistic routing protocols.

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