Opportunistic routing is being investigated to enable the proliferation of low-cost wireless applications. A recent trend is looking at social structures, inferred from the social nature of human mobility, to bring messages close to a destination. To have a better picture of social structures, social-based opportunistic routing solutions should consider the dynamism of users' behavior resulting from their daily routines. We address this challenge by presenting dLife, a routing algorithm able to capture the dynamics of the network represented by time-evolving social ties between pair of nodes. Experimental results based on synthetic mobility models and real human traces show that dLife has better delivery probability, latency, and cost than proposals based on social structures.
Most of the existing opportunistic routing solutions are based on some level of replication @cite_0 . Among these proposals, emerge solutions based on different representations of social similarity: i) labeling users according to their social groups (e.g., @cite_8 ); ii) looking at the importance (i.e., popularity) of nodes (e.g., @cite_4 ); iii) combining the notion of community and centrality (e.g., @cite_9 and @cite_2 ); iv) considering interests that users have in common (e.g., @cite_10 ).
@cite_4 @cite_8 @cite_9 @cite_0 @cite_2 @cite_10
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It is widely believed that identifying communities in an ad hoc mobile communications system, such as a pocket switched network, can reduce the amount of traffic created when forwarding messages, but there has not been any empirical evidence available to support this assumption to date. In this paper, we show through use of real experimental human mobility data, how using a small label, identifying users according to their affiliation, can bring a large improvement in forwarding performance, in term of both delivery ratio and cost 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. Due to the increased capabilities of mobile devices and through wireless opportunistic contacts, users can experience new ways to share and retrieve content anywhere and anytime, even in the presence of link intermittency. Due to the significant number of available routing solutions, it is difficult to understand which one has the best performance, since all of them follow a different evaluation method. This paper proposes an assessment model, based on a new taxonomy, which comprises an evaluation guideline with performance metrics and experimental setup to aid designers in evaluating solutions through fair comparisons. Simulation results based on the proposed model revisit the performance results published by Epidemic, PROPHET, and BubbleRap, showing how they perform under the same set of metrics and scenario. The increasing penetration of smart devices with networking capability form novel networks. Such networks, also referred as pocket switched networks (PSNs), are intermittently connected and represent a paradigm shift of forwarding data in an ad hoc manner. The social structure and interaction of users of such devices dictate the performance of routing protocols in PSNs. To that end, social information is an essential metric for designing forwarding algorithms for such types of networks. Previous methods relied on building and updating routing tables to cope with dynamic network conditions. On the downside, it has been shown that such approaches end up being cost ineffective due to the partial capture of the transient network behavior. A more promising approach would be to capture the intrinsic characteristics of such networks and utilize them in the design of routing algorithms. In this paper, we exploit two social and structural metrics, namely centrality and community, using real human mobility traces. The contributions of this paper are two-fold. First, we design and evaluate BUBBLE, a novel social-based forwarding algorithm, that utilizes the aforementioned metrics to enhance delivery performance. Second, we empirically show that BUBBLE can substantially improve forwarding performance compared to a number of previously proposed algorithms including the benchmarking history-based PROPHET algorithm, and social-based forwarding SimBet algorithm. Applications involving the dissemination of information directly relevant to humans (e.g., service advertising, news spreading, environmental alerts) often rely on publish-subscribe, in which the network delivers a published message only to the nodes whose subscribed interests match it. In principle, publish- subscribe is particularly useful in mobile environments, since it minimizes the coupling among communication parties. However, to the best of our knowledge, none of the (few) works that tackled publish-subscribe in mobile environments has yet addressed intermittently-connected human networks. Socially-related people tend to be co-located quite regularly. This characteristic can be exploited to drive forwarding decisions in the interest-based routing layer supporting the publish-subscribe network, yielding not only improved performance but also the ability to overcome high rates of mobility and long-lasting disconnections. In this paper we propose SocialCast, a routing framework for publish-subscribe that exploits predictions based on metrics of social interaction (e.g., patterns of movements among communities) to identify the best information carriers. We highlight the principles underlying our protocol, illustrate its operation, and evaluate its performance using a mobility model based on a social network validated with real human mobility traces. The evaluation shows that prediction of colocation and node mobility allow for maintaining a very high and steady event delivery with low overhead and latency, despite the variation in density, number of replicas per message or speed.