| Abstract: |
Opportunistic networks use human mobility and consequent wireless contacts between mobile devices, to disseminate data in a peer-to-peer manner. To grasp the potential and limitations of such
networks, as well as to design appropriate algorithms and protocols, it is key to understand the statistics of contacts. To date, contact analysis has mainly focused on statistics such as
inter-contact and contact distributions. While these pair-wise properties are important, we argue that structural properties of contacts need more thorough analysis. For example, communities of
tightly connected nodes, have a great impact on performance of opportunistic networks and the design of algorithms and protocols. In this paper, we propose a methodology to represent a mobility
scenario (i.e., measured contacts) as a weighted contact graph, where tie strength represents how long and often a pair of nodes is in contact. This allows us to analyze the structure of a
scenario using tools from social network analysis and graph theory (e.g., community detection, connectivity metrics). We consider four mobility scenarios of different origins and sizes. Across
all scenarios, we find that mobility shows typical small- world characteristics (short path lengths, and high clustering coefficient). Using state-of-the-art community detection, we also find
that mobility is strongly modular. However, communities are not homogenous entities. Instead, the distribution of weights and degrees within a community is similar to the global distribution of
weights, implying that communities have also a rather intricate internal structure. To the best of our knowledge, this is the most comprehensive study of structural properties of wireless
contacts, in terms of the number of nodes in our datasets, and the variety of metrics we consider. Finally, we discuss the primary importance of our findings for mobility modeling and especially
for the design of opportunistic network solutions. |
