Opportunistic mobile ad hoc networks consist of human-carried mobile devices that communicate with each other in a "store-carry-forward" fashion, without any infrastructure. They present distinct challenges compared to classical networks, such as the Internet, that assumes the availability of a contemporaneous, reasonably low propagation delay, low packet loss rate path between the two end points that communicate. In opportunistic networks, disconnections and highly variable delays caused by human mobility are the norm. Another major challenge in opportunistic communications arises from the small form factor of mobile devices which introduces resource limitations compared to static computing systems. Moreover, implementation and deployment of actual opportunistic mobile networks, systems and applications is challenging, very often expensive and time-consuming. Hence, the research community has mainly relied on simulations and analytical modeling, or on simple proof-of-concept prototypes to demonstrate the feasibility of these systems.
In this dissertation we take an experimental approach to the analysis and design of opportunistic mobile networks. We explore in particular the impact of social interactions on the structure of these networks. Real-life experimentation is important as opportunistic mobile networking is still a relatively new research area. Very little is known about the operational conditions that are complex and difficult to model or simulate. The behavior of users, which is a key feature of our system design, has to be observed in situ.
This dissertation makes several original contributions. First, we study Bluetooth based opportunistic communications in detail using smartphones in both controlled and real-life settings. We find that despite the practical limitations of Bluetooth, opportunistic ad-hoc communication is an effective and attractive communication paradigm. Second, we design and implement MobiClique, a communication middleware for opportunistic mobile networking. MobiClique takes advantage of user mobility and social relationships to forward messages in an opportunistic manner. Third, we perform a large scale experiment with 80 people, where we collect social network information (i.e. their Facebook profile), and ad-hoc contact and communication traces. Using the collected data, we propose a methodology to analyze temporal community structures of the opportunistic network. Furthermore, we show how temporal community structures and social interaction characterize the epidemic content dissemination paths.