Mobile Social Networks (MSNs) consist of mobile nodes 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, enhance existing cellular or WLAN networks, and enable novel social and location-based applications.
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, we analytically investigate to what extent mobility/traffic/social heterogeneity affects mobile social networking. We propose novel 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).
While the main focus is on MSNs, we provide insights on how our analytical approaches and/or many of our results can have applicability in different contexts and network settings.
Pavlos Sermpezis received his Diploma in Electrical and Computer Engineering, with a specialization in Telecommunications, from the Aristotle University of Thessaloniki, Greece, in 2011. In November of the same year, he joined the Mobile Communications Dept. at EURECOM, Sophia-Antipolis, France, as a PhD student under the supervision of A/Prof. Thrasyvoulos Spyropoulos. He successfully defended his PhD thesis in February 2015.
The main focus of his thesis, titled "Performance Analysis of Mobile Social Networks with Realistic Mobility and Traffic Patterns", was on analytical modeling and performance analysis of mobile-to-mobile communication (mobile social networks, MSNs). His main research interests include: opportunistic networking and computing, mobile networks, mobile data offloading, online and location-based social networks, which he studies using (mainly) analytic tools like:
stochastic processes, random graph theory, network epidemics, complex networks analysis.
