Adaptive method to support social-based mobile networks using a pagerank approach

摘要

Opportunistic networks are mobile networks that rely on the store-carry-and-forward paradigm, usingrncontacts between nodes to opportunistically transfer data. For this reason, traditional routing mechanismsrnare no longer suitable. The use of additional routing criterion, such as social information about nodes, canrnincrease the probability of successful message delivery. Popularity of a node, another important routing criterion,rncan be inferred using the betweenness centrality, meaning the number of times the node is on thernshortest path between any other two nodes in the social graph. However, computing the betweenness centralityrnis impossible in practice, especially when connectivity between individuals is transient, and each nodernhas only a local view of the entire network.We propose a fundamental rethinking, where nodes and not pathsrnare the observation focus. In our approach, we compute the probability of a node to participate in a path formationrn(e.g., the probability of a node to lead to the next popular path).We present our solution, which takesrninspiration from the PageRank approach, and present an algorithm to compute and update the popularityrnof nodes using the probability of each node to be used as carrier for random messages traversing the network.rnWe demonstrate that this approach is highly robust, numerical insensitive to errors, and converges fast,rnmeaning it can be easily adopted in resource-constraint environments formed between highly mobile wirelessrndevices. Our experimental results sustain our empirical observations for various case studies.