Social network analysis for routing in disconnected delay-tolerant MANETs

Abstract

Message delivery in sparse Mobile Ad hoc Networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low end-to-end delay in a disconnected network graph where nodes may move freely. This paper presents a multidisciplinary solution based on the consideration of the socalled small world dynamics which have been proposed for economy and social studies and have recently revealed to be a successful approach to be exploited for characterising information propagation in wireless networks. To this purpose, some bridge nodes are identified based on their centrality characteristics, i.e., on their capability to broker information exchange among otherwise disconnected nodes. Due to the complexity of the centrality metrics in populated networks the concept of ego networks is exploited where nodes are not required to exchange information about the entire network topology, but only locally available information is considered. Then SimBet Routing is proposed which exploits the exchange of pre-estimated 'betweenness' centrality metrics and locally determined social 'similarity' to the destination node. We present simulations using real trace data to demonstrate that SimBet Routing results in delivery performance close to Epidemic Routing but with significantly reduced overhead. Additionally, we show that SimBet Routing outperforms PRoPHET Routing, particularly when the sending and receiving nodes have low connectivity.

An Improved SimBet Routing Algorithm for Human Mobility Based DTN

“…As social characteristics of a network are less volatile and long-term SimBet uses two characteristics among them is Similarity and Betweenness Centrality [8], [12]. Here Node Similarity is used to find whether nodes reside to same cluster and centrality is used to identify ties between two clusters.…”

“…SimBet algorithm used new Egocentric Betweenness metric based on ego network analysis to locally determine a node's centrality within the network [12]. In the SimBet algorithm, locally calculated centrality matrix used instead of globally calculated centrality matrix for the network.…”

“…Betweenness Centrality is measure number of shortest path via certain node [8]. Here, node calculates Betweenness value locally based on equation [12] and within cluster node with higher Betweenness value is considered as the central node. The number of common nodes between individual within the cluster defines similarity, similarity shows the degree of separation [8].…”

“…The SimBet utility value is between 0 and 1 [12]. For transmitting message to any destination node d, Similarity utility and Betweenness utility of node n compare to node m is given below equation [12]:…”

“…The SimBet utility of node n for delivering message to node d is normalize weighted combination of Similarity utility and Betweenness utility [12]:…”