Performance of data forwarding in Delay Tolerant Networks (DTNs) benefits considerably if one can make use of human mobility in terms of social structures. However, it is difficult and time-consuming to calculate the centrality and similarity of nodes by using solutions for traditional social networks, this is mainly because of the transient node contact and the intermittently connected environment. In this work, we are interested in the following question: Can we explore some other stable social attributes to quantify the centrality and similarity of nodes? Taking GPS traces of human walks from the real world, we find that there exist two known phenomena. One is public hotspot, the other is personal hotspot. Motivated by this observation, we present Hoten (hotspot and entropy), a novel routing metric to improve routing performance in DTNs. First, we use the relative entropy between the public hotspots and the personal hotspots to compute the centrality of nodes. Then we utilize the inverse symmetrized entropy of the personal hotspots between two nodes to compute the similarity between them. Third, we exploit the entropy of personal hotspots of a node to estimate its personality. Besides, we propose a method to ascertain the optimized size of hotspot. Finally, we compare our routing strategy with other state-of-the-art routing schemes through extensive trace-driven simulations, the results show that Hoten largely outperforms other solutions, especially in terms of combined overhead packet delivery ratio and the average number of hops per message.
Note that most aforementioned schemes do not take the social structures into account. However, with the recent popularization of personal hand-held mobile devices, human walks gradually play a critical role in the network performance, since devices may fail to connect with each other when people move around. Recently, a few works attempt to uncover the underlying stable network structure in real traces by using social networks analysis technology @cite_17 . For example, SimBet @cite_31 exploited betweenness centrality and social similarity of ego networks @cite_13 to differentiate nodes. Messages will be forwarded to such nodes which have relatively big SimBet values to increase the probability of finding better relays to the final destination. P. @cite_11 proposed BUBBLE, which combined node centrality and community structure to make forwarding decisions. They assumed that each node had a global rank across the whole system and a local rank within its local community. When a message is out of the community of the destination, it is forwarded to the node with a high global rank, when the message enters into the range of the destination community, it is delivered to the node with a high local rank in that community.
@cite_31 @cite_11 @cite_13 @cite_17
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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 so-called 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. In this paper we seek to improve our understanding of human mobility in terms of social structures, and to use these structures in the design of forwarding algorithms for Pocket Switched Networks (PSNs). Taking human mobility traces from the real world, we discover that human interaction is heterogeneous both in terms of hubs (popular individuals) and groups or communities. We propose a social based forwarding algorithm, BUBBLE, which is shown empirically to improve the forwarding efficiency significantly compared to oblivious forwarding schemes and to PROPHET algorithm. We also show how this algorithm can be implemented in a distributed way, which demonstrates that it is applicable in the decentralised environment of PSNs. In this paper, we look at the betweenness centrality of ego in an ego network. We discuss the issue of normalization and develop an efficient and simple algorithm for calculating the betweenness score. We then examine the relationship between the ego betweenness and the betweenness of the actor in the whole network. Whereas, we can show that there is no theoretical link between the two we undertake a simulation study, which indicates that the local ego betweenness is highly correlated with the betweenness of the actor in the complete network. Abstract Egocentric centrality measures (for data on a node’s first-order zone) parallel to Freeman’s [Social Networks 1 (1979) 215] centrality measures for complete (sociocentric) network data are considered. Degree-based centrality is in principle identical for egocentric and sociocentric network data. A closeness measure is uninformative for egocentric data, since all geodesic distances from ego to other nodes in the first-order zone are 1 by definition. The extent to which egocentric and sociocentric versions of Freeman’s betweenness centrality measure correspond is explored empirically. Across seventeen diverse networks, that correspondence is found to be relatively close—though variations in egocentric network composition do lead to some notable differences in egocentric and sociocentric betweennness. The findings suggest that research design has a relatively modest impact on assessing the relative betweenness of nodes, and that a betweenness measure based on egocentric network data could be a reliable substitute for Freeman’s betweenness measure when it is not practical to collect complete network data. However, differences in the research methods used in sociocentric and egocentric studies could lead to additional differences in the respective betweenness centrality measures.