Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing

Abstract

Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, forwarding decisions are generally made using locally collected knowledge about node behavior (e.g., past contacts between nodes) to predict future contact opportunities. The use of complex network analysis has been recently suggested to perform this prediction task and improve the performance of DTN routing. Contacts seen in the past are aggregated to a social graph, and a variety of metrics (e.g., centrality and similarity) or algorithms (e.g., community detection) have been proposed to assess the utility of a node to deliver a content or bring it closer to the destination.In this paper, we argue that it is not so much the choice or sophistication of social metrics and algorithms that bears the most weight on performance, but rather the mapping from the mobility process generating contacts to the aggregated social graph. We first study two well-known DTN routing algorithms -SimBet and BubbleRap -that rely on such complex network analysis, and show that their performance heavily depends on how the mapping (contact aggregation) is performed. What is more, for a range of synthetic mobility models and real traces, we show that improved performances (up to a factor of 4 in terms of delivery ratio) are consistently achieved for a relatively narrow range of aggregation levels only, where the aggregated graph most closely reflects the underlying mobility structure. To this end, we propose an online algorithm that uses concepts from unsupervised learning and spectral graph theory to infer this "correct" graph structure; this algorithm allows each node to locally identify and adjust to the optimal operating point, and achieves good performance in all scenarios considered.

Is it possible to find social properties in vehicular networks?

“…Thus, with the data in each slot we build a temporal graph. The temporal graph at the time t is an undirected, and can be formally defined as a graph G(t)=( V, E),w h e r e V represents the set with all vehicles v i and E represents the set of edges e ij .I nG(t),a ne d g ee ij (t) exists between the vehicles v i and v j during time t,w i t hi̸ =j.E a c hm e t r i c is evaluated hourly, considering the temporal graph G(t) that takes into account all the encounters in the period t.W eaggregate the encounters that happen during the slot window, and build the graph using the Growing Time Window technique [9]. For each trace, 24 graphs G(t) are generated describing the vehicles encounters during the day.…”

“…These are important parameters to define in the analysis when the focus is evaluate the encounter probability in the Vehicular Network. In the work [9], the authors present a discussion about how create the social graph with the contacts in a Delay Tolerant Network. They argue how the selection of the parameters and the contact aggregation can affect to the social graph.…”

Social closeness based clone attack detection for mobile healthcare system

“…However, the complexity and different characters of the social mobile network make it difficult to take a formal analysis on choosing a suitable T . Thus, in this section, we propose a contact model by considering users belonging to multiple communities, which is inspired by the work in [21] and the Watts and Strogatz model. However, [21] …”

“…Thus, in this section, we propose a contact model by considering users belonging to multiple communities, which is inspired by the work in [21] and the Watts and Strogatz model. However, [21] …”

An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

“…Community detection can help us to uncover and understand local community structure in both offline mobile trace analysis and online applications, and it is helpful in decreasing forwarding time as well as the storage capacity of nodes. Since the relationships between nodes usually seem to be stable and less volatile than node mobility, forwarding schemes based on community [2][3][4][5][6]outperform traditional approaches [7,8]. Overlapping community detection, one of the most interesting research of community detection, is the primary focus of this paper.…”