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

Abstract

SummaryOpportunistic networks are mobile networks that rely on the store‐carry‐and‐forward paradigm, using contacts between nodes to opportunistically transfer data. For this reason, traditional routing mechanisms are no longer suitable. The use of additional routing criterion, such as social information about nodes, can increase the probability of successful message delivery. Popularity of a node, another important routing criterion, can be inferred using the betweenness centrality, meaning the number of times the node is on the shortest path between any other two nodes in the social graph. However, computing the betweenness centrality is impossible in practice, especially when connectivity between individuals is transient, and each node has only a local view of the entire network. We propose a fundamental rethinking, where nodes and not paths are the observation focus. In our approach, we compute the probability of a node to participate in a path formation (e.g., the probability of a node to lead to the next popular path). We present our solution, which takes inspiration from the PageRank approach, and present an algorithm to compute and update the popularity of nodes using the probability of each node to be used as carrier for random messages traversing the network. We demonstrate that this approach is highly robust, numerical insensitive to errors, and converges fast, meaning it can be easily adopted in resource‐constraint environments formed between highly mobile wireless devices. Our experimental results sustain our empirical observations for various case studies. Copyright © 2013 John Wiley & Sons, Ltd.