- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
In the last few years, several real-world mobility traces for opportunistic networks have been collected in order to explore node mobility and evaluate the performance of opportunistic networking protocols. These datasets, often including online social data of the mobile users involved, are increasingly driving the research towards the analysis of user social behavior. Within these challenged infrastructureless networks where connectivity is highly intermittent and contact opportunities are exploited to allow communication, node mobility is basically driven by human sociality. As such, understanding node sociality is of paramount importance, especially for finding suitable relays in message forwarding. This paper presents a detailed analysis of a set of six different mobility traces for opportunistic network environments including nodes’ Facebook friendships. Using a multi-layer social network approach and defining several similarity classes between layers, we analyze egocentric and sociocentric node behaviors on the two-layer social graph constructed on offline mobility and online social data. Results show that online and offline centralities are not significantly correlated on most datasets. Also online and offline community structures are different. On the contrary, most of the offline strong social ties correspond to online social ties and in some cases, online and offline brokerage roles show high similarity
9. Conclusions and future work
In this paper, we have presented a novel and detailed methodology for analyzing a set of real mobility traces for opportunistic networks using a multi-layer network approach. The aim of this study has been to better understand user social behavior in terms of egocentric and sociocentric behaviors that can be derived not only from mobility data (encounters’ social network) but also from the available additional information provided by the social network layer built on Facebook friendships. Our results have shown that online and offline social behavior computed in terms of centrality measures like betweenness, ego betweenness, closeness, degree and eigenvector centrality are not significantly correlated on most datasets. In other words, node popularity changes signifi- cantly on the online and offline social worlds. Also online and of- fline community structures are different. Analyzing the two-layer social network constructed on mobility data and Facebook friendships we have also shown that in some cases, online and offline brokerage roles show high similarity. However, the correlation between online and offline brokerage roles varies significantly from one dataset to another and it is not possible to extract a common trend with respect to this kind of similarity analysis. Finally, we have shown that in five of the six datasets considered, most of the strong ties in the social layer built on wireless encounters correspond to Facebook friendships.