Graduate School and Research Center in Digital Sciences


Eurecom - Communication systems 
Phd Student ( 2011 - 2015)


Performance Analysis of Mobile Social Networks with Realistic Mobility and Traffic Patterns


Mobile Social Networks (MSNs) consist of mobile nodes (e.g. smartphones, laptops) that can exchange data using local wireless communication (e.g. Bluetooth, WiFi), when they are within transmission range. MSNs are envisioned to support communication in challenging environments, where infrastructure is limited (emergency situations after disasters, rural areas, etc.), or enhance existing cellular or WLAN networks, e.g. by offloading traffic, enabling novel social and location-based applications, or introducing peer-to-peer mobile computing.

Communication performance in MSNs heavily depends on the underlying node mobility and the traffic demand patterns between them. In addition, numerous studies from different disciplines, have shown that mobility/traffic patterns are (a) largely heterogeneous and (b) correlated to nodes social characteristics. To this end, the primary focus of the thesis is on understanding, analytically, to what extent mobility/traffic/social heterogeneity affects mobile social networking. Towards this direction, we propose models that take into account key aspects of real MSN users' characteristics, and analyze the performance of networking mechanisms (e.g. routing protocols or content-delivery schemes). We provide novel results for aspects, like social selfishness and traffic heterogeneity, that have not been studied (analytically) before in MSNs. Finally, based on our results, we propose general design guidelines and useful insights about network protocols and applications.

While our focus is on MSNs, many of the results can have applicability in different contexts, like Online Social (OSNs), P2P or content distribution (CDNs) networks, etc.