| Throughout the last decade, increasing penetration of portable devices like smartphones and tablets with their incessant enrichment in strong peer-to-peer networking (e.g., Bluetooth, WiFi Direct) and sensing capabilities, have made opportunistic networks as one of the most auspicious communication methods for the next generation mobile applications. The contemporary decade also experienced the rise of social networks attracting massive interest from scholars of diverse fields. While the internet based social networks has evolved to certain level of maturity, once again it is the mobile devices and their applications that are changing the landscapes in studying social networks. New challenges and promises brought up by the emerging mobile technologies have surfaced a novel field blending the opportunistic network and social network concepts. Opportunistic Mobile Social Network can be described as the platform that provides human mobility aided services via hand held devices for the fostering and maintaining social interactions and connections.;In this dissertation, we first propose a novel framework to reveal the inherent connected virtual backbone in an opportunistic network through the consociation of the neighbors in the network. This backbone can pave the way for designing an architecture for real-time mobile social applications. The backbone may change in terms of time, location and crowd density. Experimenting on real world as well as synthetic human mobility traces and pause times, we first structure the pattern of human halt durations at popular places. Infusing this pattern, we then prove the existence of the intrinsic backbone in those networking environments, where people show regularity in their movements. Applying graph-theoretic concepts like Minimum Connected Dominating Set and Unit Node Weighted Steiner Tree we further optimize and ensure the robustness of the backbone. Simulation results show the effectiveness of our approach in exposing a newer dimension in the form of real time interaction prospects in opportunistic networks. Next we propose a novel scheme called HiPCV, which uses a distributed learning approach to capture preferential movement of the individuals, with spatial contexts and directional information and paves the way for mobility history assisted contact volume prediction (i.e., link capacity prediction). Experimenting on real world human mobility traces, HiPCV first learns and structures human walk patterns, along her frequently chosen trails. By creating a Mobility Markov Chain (MMC) out of this pattern and embedding it into HiPCV algorithm, we then devise a decision model for data transmissions during opportunistic contacts. Experimental results show the robustness of HiPCV in terms mobility prediction, reliable opportunistic data transfers and bandwidth saving, at places where people show regularity in their movements. For challenged environments where previous mobility history is scarce, we futher extend the idea of contact volume prediction and propose a energy efficient framework called EPCV. EPCV re-introduces a form of localization approach, aware of the communication technology diversity across the portable devices. Experimental results on real traces confirm that EPCV can help determining the encounter-triggered opportunistic link capacities and exploit it in mobile social network paradigm, keeping the energy usage minimal. (Abstract shortened by UMI.). |
