Mobile users are envisioned to exploit direct communication opportunities between their portable devices, in order to enrich the set of services they can access through cellular or WiFi networks. Sharing contents of common interest or providing access to resources or services between peers can enhance a mobile node's capabilities, offload the cellular network, and disseminate information to nodes without Internet access. Interest patterns, i.e. how many nodes are interested in each content or service (popularity), as well as how many users can provide a content or service (availability) impact the performance and feasibility of envisioned applications. In this paper, we establish an analytical framework to study the effects of these factors on the delay and success probability of a content service access request through opportunistic communication. We also apply our framework to the mobile data offloading problem and provide insights for the optimization of its performance. We validate our model and results through realistic simulations, using datasets of real opportunistic networks.
Under a different setting, @cite_24 @cite_8 study content sharing mechanisms with limited resources (e.g. buffer sizes, number of holders). @cite_24 , authors analytically investigate the data dissemination cost-effectiveness tradeoffs, and propose techniques based on contact patterns (i.e. @math ) and nodes interests. Similarly, CEDO @cite_8 aims at maximizing the total content delivery rate: by maintaining a utility per content, nodes make appropriate drop and scheduling decisions.
@cite_24 @cite_8
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Data dissemination is useful for many applications of Disruption Tolerant Networks (DTNs). Current data dissemination schemes are generally network-centric ignoring user interests. In this paper, we propose a novel approach for user-centric data dissemination in DTNs, which considers satisfying user interests and maximizes the cost-effectiveness of data dissemination. Our approach is based on a social centrality metric, which considers the social contact patterns and interests of mobile users simultaneously, and thus ensures effective relay selection. The performance of our approach is evaluated from both theoretical and experimental perspectives. By formal analysis, we show the lower bound on the cost-effectiveness of data dissemination, and analytically investigate the tradeoff between the effectiveness of relay selection and the overhead of maintaining network information. By trace-driven simulations, we show that our approach achieves better cost-effectiveness than existing data dissemination schemes. Emerging challenged networks require new protocols and strategies to cope with a high degree of mobility, high delays and unknown, possibly non-existing routes within the network. Researchers have proposed different store-carry-and-forward protocols for data delivery in challenged networks. These have been complemented with appropriate drop and scheduling policies that deal with the limitations of the nodes' buffers and the limited duration of opportunistic encounters in these networks. Nevertheless, the vast majority of these protocols and strategies are designed for end-to-end transmissions. Yet, a paradigm shift from the traditional way of addressing the endpoints in the network has been occurring towards content-centric networking. To this end, we present CEDO, a content-centric dissemination algorithm for challenged networks. CEDO aims at maximizing the total delivery-rate of distributed content in a setting where a range of contents of different popularity may be requested and stored, but nodes have limited resources. It achieves this by maintaining a delivery-rate utility per content that is proportional to the content miss rate and that is used by the nodes to make appropriate drop and scheduling decisions. This delivery-rate utility can be estimated locally by each node using unbiased estimators fed by sampled information on the mobile network obtained by gossiping. Both simulations and theory suggest that CEDO achieves its set goal, and outperforms a baseline LRU-based policy by 72 , even in relatively small scenarios. The framework followed by CEDO is general enough to be applied to other global performance objectives as well.