CAF: Community aware framework for large scale mobile opportunistic networks
Computer Communications
Fairness-related challenges in mobile opportunistic networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
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In opportunistic ad-hoc networks, multi-hop data transfer over contemporaneous paths is unlikely since the devices are often disconnected from each other. However, data can still be stored and forwarded over time in an opportunistic hop-by-hop manner. Previous work has considered how the availability of various types of information such as social relationships can be used to guide forwarding algorithm to make better decisions and bring messages closer to the destination. This implicitly assumes that opportunistic contacts relate with the social property of node. However, the impact of such correlation between social and contact properties on social forwarding performances remains largely unexplored. In this paper we argue that the relevance of such social information (social inputs) is as important as designing a new social forwarding algorithms. We examine multiple datasets to determine the impact of correlation, if any, between social information of individuals and their mobility patterns on the forwarding performances. We propose methods which process the social inputs to improve the relevance of such social information to forwarding. We show that our processing methods could improve the success rate performances of many social forwarding algorithms by more than 30%.