Understanding Influencers of Adaptive Social-Aware Opportunistic Forwarding

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Social-aware opportunistic forwarding algorithms are essential in poor network infrastructure environments. However, most of these algorithms are oblivious to users’ interest and their mobile nodes’ limited power resources. Moreover, along their movement, mobile nodes encounter variations in the surrounding context information that change the availability and importance of context information included in ranking nodes for content forwarding. A few proposed forwarding algorithms dynamically change the weights of the ranking attributes based on the current context. This chapter proposes an adaptive ranking framework integrating a set of parameters of the node rank which adapts to the current context. The main proposed parameters of the node rank include opportunistic selection of forwarders, the users’ interest in the forwarded content and their nodes’ power capability, the measure of the level of activeness of socially popular users, and the popularity of the place these users frequent. The forwarding decision making relies mainly on this rank to adapt to the surrounding context information available at the time of evaluating the rank of the node. Four proposed variations of the adaptive ranking framework are detailed in this work. Simulations are conducted on real university wireless connection traces to compare the performance of the proposed adaptive ranking algorithms to some of the state-of-the-art adaptive social-aware forwarding algorithms such as the PeopleRank algorithm, the SCAR algorithm, the Energy-aware Bubble Rap algorithm, and two of the PI-SOFA framework algorithms, namely, the PIPeROp and the PISCAROp algorithms. Performance of the simulated algorithms is evaluated in terms of effectiveness, efficiency, power awareness, and utilization fairness. Results of the simulations reveal how the adaptive ranking versions outperform the other compared algorithms, with up to a 40% increase in f-measure, a 96% reduction in the ratio of contacted uninterested users, a 93% reduction in delay, a 75.6% reduction in cost, and a 10% reduction in power consumption.