FOG: Fairness in mobile opportunistic networking

The fundamental challenge in opportunistic networking, regardless of the application, is when and how to forward a message. Rank-based forwarding techniques currently represent one of the most promising methods for addressing this message forwarding challenge. While these techniques have demonstrated great efficiency in performance, they do not address the rising concern of fairness amongst various nodes in the network. Higher ranked nodes typically carry the largest burden in delivering messages, which creates a high potential of dissatisfaction amongst them. In this paper, we adopt a real-trace driven approach to study and analyze the tradeoff between the efficiency and fairness of rank-based forwarding techniques in mobile opportunistic networks. Our work comprises three major contributions. First, we quantitatively analyze the tradeoffs between fair and efficient environments. Second, we demonstrate how fairness coupled with efficiency can be achieved based on real mobility traces. Third, we propose FOG, a real-time distributed framework to ensure efficiency-fairness tradeoff using local information. Our data-driven experiment and analysis show that mobile opportunistic communication between users may fail with the absence of fairness in participating high-ranked nodes, and an absolute fair treatment of all users yields inefficient communication performance. Finally our analysis show that FOG ensures relative equality in the distribution of resource usage among neighbor nodes while keeping the success rate and cost performance near optimal.