Mobile devices integrating wireless short-range communication technologies make possible new applications for spontaneous communication, interaction and collaboration. An interesting approach is to use collaboration to facilitate communication when mobile devices are not able to establish direct communication paths. Opportunistic networks, formed when mobile devices communicate with each other while users are in close proximity, can help applications still exchange data in such cases. In opportunistic networks routes are built dynamically, as each mobile device acts according to the store-carry-and-forward paradigm. Thus, contacts between mobile devices are seen as opportunities to move data towards destination. In such networks data dissemination is done using forwarding and is usually based on a publish subscribe model. Opportunistic data dissemination also raises questions concerning user privacy and incentives. Such problems are addressed dierently by various opportunistic data dissemination techniques. In this we analyze existing relevant work in the area of data dissemination in opportunistic networks. We present the categories of a proposed taxonomy that capture the capabilities of data dissemination techniques used in such networks. Moreover, we survey relevant data dissemination techniques and analyze them using the proposed taxonomy. In this we analyze existing work in the area of data dissemination in opportunistic networks. We analyze dif- ferent collaboration-based communication solutions, em- phasizing their capabilities to opportunistically dissemi- nate 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 net- works. Using the categories of the proposed taxonomy, we also present a critical analysis of four opportunistic data dissemination solutions. To our knowledge, a classication of data dissemination techniques has never been previously proposed.
Several papers exclusively treat the problem of data dissemination in opportunistic networks. The Epidemic approach is presented in @cite_5 . In @cite_10 , a dissemination technique based on publish subscribe communication and communities is described, while @cite_11 and @cite_6 propose a wireless ad hoc podcasting system based on opportunistic networks. A multicast distribution method is presented in @cite_3 , while ContentPlace, a system that exploits dynamically learned information about users' social relationships to decide where to place data objects in order to optimize content availability, is presented in @cite_0 . These methods are further analyzed in Section 4. To compare them we apply the categories of the proposed taxonomy. The obtained analysis highlights their advantages and disadvantages and further differentiates between their capabilities.
@cite_3 @cite_6 @cite_0 @cite_5 @cite_10 @cite_11
2111269948 2112263843 2249121382 2139352451
This paper presents a publish subscribe-based multicast distribution infrastructure for DTN-based opportunistic networking environments. The distribution approach is designed to combine an effective distribution of content to interested nodes in the presence of resource constraints, mobility and unstable connectivity. By considering local resource constraints such as limited storage space and limited available bandwidth at opportunistic contacts as well as knowledge about interest for content in the network environment, nodes make local decisions about resource utilization and DTN bundle prioritization. Without further coordination, this approach uses the overall available network resources more effectively compared to other approaches such as epidemic forwarding. We have implemented this approach and have performed a series of measurements in mobile opportunistic networking scenarios under different configurations. In this paper we present and evaluate ContentPlace, a data dissemination system for opportunistic networks, i.e., mobile networks in which stable simultaneous multi-hop paths between communication endpoints cannot be provided. We consider a scenario in which users both produce and consume data objects. ContentPlace takes care of moving and replicating data objects in the network such that interested users receive them despite possible long disconnections, partitions, etc. Thanks to ContentPlace, data producers and consumers are completely decoupled, and might be never connected to the network at the same point in time. The key feature of ContentPlace is learning and exploiting information about the social behaviour of the users to drive the data dissemination process. This allows ContentPlace to be more efficient both in terms of data delivery and in terms of resource usage with respect to reference alternative solutions. The performance of ContentPlace is thoroughly investigated both through simulation and analytical models. We introduce autonomous gossiping (A G), a new genre epidemic algorithm for selective dissemination of information in contrast to previous usage of epidemic algorithms which flood the whole network. A G is a paradigm which suits well in a mobile ad-hoc networking (MANET) environment because it does not require any infrastructure or middleware like multicast tree and (un)subscription maintenance for publish subscribe, but uses ecological and economic principles in a self-organizing manner in order to achieve any arbitrary selectivity (flexible casting). The trade-off of using a stateless self-organizing mechanism like A G is that it does not guarantee completeness deterministically as is one of the original objectives of alternate selective dissemination schemes like publish subscribe. We argue that such incompleteness is not a problem in many non-critical real-life civilian application scenarios and realistic node mobility patterns, where the overhead of infrastructure maintenance may outweigh the benefits of completeness, more over, at present there exists no mechanism to realize publish subscribe or other paradigms for selective dissemination in MANET environments. Epidemic routing has been proposed to reduce the data transmission delay in disruption tolerant wireless networks, in which data can be replicated along multiple opportunistic paths as different nodes move within each other's communication range. With the advent of network coding, it is intuitive that data can not only be replicated, but also coded, when the transmission opportunity arises. However, will opportunistic communication with network coding perform any better than simple replications? In this paper, we present a stochastic analytical framework to study the performance of epidemic routing using network coding in opportunistic networks, as compared to the use of replication. We analytically show that network coding is superior when bandwidth and node buffers are limited, reflecting more realistic scenarios. Our analytical study is able to provide further insights towards future designs of efficient data communication protocols using network coding. As an example, we propose a priority based coding protocol, with which the destination can decode a high priority subset of the data much earlier than it can decode any data without the use of priorities. The correctness of our analytical results has also been confirmed by our extensive simulations.