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Top1. Introduction
In the past years, mobile devices (such as smartphones, tablets, or netbooks) have become almost ubiquitous, which has lead to the advent of several new types of mobile networks. Such networks are composed almost entirely of mobile devices, and differ considerably from the classic wired networks both in terms of structure, but also in regard to the protocols and algorithms used for routing, forwarding and dissemination. Since there isn't a stable topology, nodes in mobile networks are not aware of a global structure and have no knowledge of their relationship with other nodes (like proximity, connection quality, etc.). Each node is only aware of information about the nodes that it is in contact with at a certain moment of time, and may act as data provider, receiver and transmitter during the time it spends in the network. Thus, a node can produce data, carry it for other nodes and transmit it, or receive it for its own use.
One type of such mobile networks that has been deeply researched in recent years is represented by opportunistic networks (ONs). They are dynamically built when mobile devices collaborate to form communication paths while users are in close proximity. Opportunistic networks are based on a store-carry-and-forward paradigm (Pelusi, Passarella, & Conti, 2006), which means that a node that wants to relay a message begins by storing it, then carries it around the network until the carrier encounters the destination or a node that is more likely to bring the data close to the destination, and then finally forwards it.
One of the main challenges of opportunistic networks is deciding which nodes should the data be relayed to in order for it to reach its destination, and do it as quickly and efficiently as possible. Various types of solutions have been proposed, ranging from disseminating the information to every encountered node in an epidemic fashion (Vahdat & Becker, 2000), to selecting the nodes with the highest social coefficient or centrality (Hui, Crowcroft, & Yoneki, 2011). Prediction methods have also been employed (Ciobanu & Dobre, 2012), based on the knowledge that the mobile nodes from an opportunistic network are devices belonging to humans, which generally have the same movement and interaction patterns that they follow every day. The analysis of contact time (duration of an encounter between two nodes) and inter-contact time (duration between consecutive contacts of the same two nodes) has also been used in deciding a suitable relay node. Aside from selecting the node that the data will be forwarded to, research has also focused on congestion control, privacy, security, or incentive methods for convincing users to altruistically participate in the network.
An important topic in opportunistic networks is represented by data dissemination. In such networks, topologies are unstable. Various authors proposed different data-centric approaches for data dissemination, where data is pro-actively and cooperatively disseminated from sources towards possibly interested receivers, as sources and receivers might not be aware of each other and never get in touch directly. Such data dissemination techniques are usually based on a publish/subscribe model. In this article we analyze existing work in the area of data dissemination in opportunistic networks. We analyze different collaboration-based communication solutions, emphasizing their capabilities to opportunistically disseminate data. We present the advantages and disadvantages of the analyzed solutions. Furthermore, we propose the categories of a taxonomy that captures the capabilities of data dissemination techniques used in opportunistic networks. Using the categories of the proposed taxonomy, we also present a critical analysis of four opportunistic data dissemination solutions. To our knowledge, a classification of data dissemination techniques has never been previously proposed.
The rest of the paper is structured as follows. Section 2 highlights the motivation of our work, along with the significance and applicability of opportunistic networks in real life. Section 3 presents relevant contributions in the research area of opportunistic networks. Section 4 proposes the categories of a taxonomy for analyzing and comparing data dissemination techniques in opportunistic networks. In Section 5 we survey and critically analyze, using the proposed taxonomy, four relevant dissemination techniques. In Section 6 we conclude and present future research directions of our work.