Data dissemination in opportunistic networks poses a series of challenges, since there is no central entity aware of all the nodes' subscriptions. Each individual node is only aware of its own interests and those of a node that it is contact with, if any. Thus, dissemination is generally performed using epidemic algorithms that flood the network, but they have the disadvantage that the network overhead and congestion are very high. In this paper, we propose ONSIDE, an algorithm that leverages a node's online social connections (i.e. friends on social networks such as Facebook or Google+), its interests and the history of contacts, in order to decrease congestion and required bandwidth, while not affecting the overall network's hit rate and the delivery latency. We present the results of testing our algorithm using an opportunistic network emulator and three mobility traces taken in different environments.
Recent endeavors in mobile computing concentrate on analyzing the predictability of human behavior by means of mobility models synthesized from real mobile user traces. Currently, the main focus of such studies is physical location: discovering travel patterns, estimating real user movements and anticipating the whereabouts and dynamics of individuals. In this paper, we propose to widen the analyzed context as to take into account a more natural activity in human behavior, namely interaction. As such, we explore the predictability of user synergy based on tracing data collected from mobile phone users in academic and office environments. We take into account interactions over Bluetooth and over wirelesss networks and, by measuring the entropy of interacting both with peers and wireless access points, we discover a remarkable invariability in synergic patterns.
Opportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this chapter, we examine how causal and total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination algorithms, we investigate if causal order can be efficiently achieved in terms of hit rate and latency compared to not using any order. Afterwards, we propose a Commutative Replicated Data Type algorithm based on Logoot that uses the nature of opportunistic networks to its advantage. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces and chat traces.
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