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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Since users move around based on social relationships and interests, their movement patterns represent how nodes are socially connected (i.e., nodes with strong social ties, nodes that meet occasionally by sharing the same working environment). This means that social interactions reflect personal relationships (e.g., family, friends, co-workers, and passers-by) that may be translated into statistical contact opportunities within and between social groups over time. Such contact opportunities may be exploited to ensure good data dissemination and retrieval, even in the presence of intermittent connectivity. Thus, in the last years, a new routing trend based on social similarity emerged where social relationships, interests, popularity, and among other social characteristics are used to improve opportunistic routing (i.e., routing able to take advantage on intermittent contacts). In this chapter, the reader will learn about the different approaches related to opportunistic routing, focusing on social-aware approaches, and how such approaches make use of social information derived from opportunistic contacts to improve data forwarding. Additionally, a brief overview on the existing taxonomies for opportunistic routing as well as a new one, based on the new social trend, are provided along with a set of experiments in scenarios based on synthetic mobility models and human traces to show the potential of social-aware solutions. 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. 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. In opportunistic networks, end-to-end paths between two communicating nodes are rarely available. In such situations, the nodes might still copy and forward messages to nodes that are more likely to meet the destination. The question is which forwarding algorithm offers the best trade off between cost (number of message replicas) and rate of successful message delivery. We address this challenge by developing the PeopleRank approach in which nodes are ranked using a tunable weighted social information. Similar to the PageRank idea, PeopleRank gives higher weight to nodes if they are socially connected to important other nodes of the network. We develop centralized and distributed variants for the computation of PeopleRank. We present an evaluation using real mobility traces of nodes and their social interactions to show that PeopleRank manages to deliver messages with near optimal success rate (close to Epidemic Routing) while reducing the number of message retransmissions by 50 compared to Epidemic Routing.