Context and resource awareness in opportunistic network data dissemination

Opportunistic networks are challenging mobile ad hoc networks characterised by frequent disconnections and partitioning. In this paper we focus on data-dissemination services, i.e. cases in which data should be disseminated in the network without a priori knowledge about the set of intended destinations. We propose a general autonomic datadissemination framework that exploits information about the users\u27 context and social behaviour, to decide how to replicate and replace data on nodes\u27 buffers. Furthermore, our data-dissemination scheme explicitly takes into account resource constraints, by jointly considering the expected utility of data replication and the associated costs. The results we present show that our solution is able to improve data availability, provide fairness among nodes, and reduce the network load with respect to reference proposals available in the literature

Modelling Data Dissemination in Opportunistic Networks

In opportunistic networks data dissemination is an impor- tant, although not widely explored, topic. Since oppor- tunistic networks topologies are very challenged and un- stable, data-centric approaches are an interesting direction to pursue. Data should be proactively and cooperatively disseminated from sources towards possibly interested re- ceivers, as sources and receivers might not be aware of each other, and never get in touch directly. In this paper we con- sider a utility-based cooperative data dissemination system in which the utility of data is defined based on the social relationships between users. Specifically, we study the per- formance of this system through an analytical model. Our model allows us to completely characterise the data dissem- ination process, as it describes both its stationary and tran- sient regimes. After validating the model, we study the sys- tem\u27s behaviour with respect to key parameters such as the definition of the data utility function, the initial data allo- cation on nodes, the number of users in the system, and the data popularity

The impact of message replication on the performance of opportunistic networks for sensed data collection

Opportunistic networks (OppNets) provide a scalable solution for collecting delay-tolerant data from sensors to their respective gateways. Portable handheld user devices contribute significantly to the scalability of OppNets since their number increases according to user population and they closely follow human movement patterns. Hence, OppNets for sensed data collection are characterised by high node population and degrees of spatial locality inherent to user movement. We study the impact of these characteristics on the performance of existing OppNet message replication techniques. Our findings reveal that the existing replication techniques are not specifically designed to cope with these characteristics. This raises concerns regarding excessive message transmission overhead and throughput degradations due to resource constraints and technological limitations associated with portable handheld user devices. Based on concepts derived from the study, we suggest design guidelines to augment existing message replication techniques. We also follow our design guidelines to propose a message replication technique, namely Locality Aware Replication (LARep). Simulation results show that LARep achieves better network performance under high node population and degrees of spatial locality as compared with existing techniques

Content dissemination in participatory delay tolerant networks

As experience with the Web 2.0 has demonstrated, users have evolved from being only consumers of digital content to producers. Powerful handheld devices have further pushed this trend, enabling users to consume rich media (for example, through high resolution displays), as well as create it on the go by means of peripherals such as built-in cameras. As a result, there is an enormous amount of user-generated content, most of which is relevant only within local communities. For example, students advertising events taking place around campus. For such scenarios, where producers and consumers of content belong to the same local community, networks spontaneously formed on top of colocated user devices can offer a valid platform for sharing and disseminating content. Recently, there has been much research in the field of content dissemination in mobile networks, most of which exploits user mobility prediction in order to deliver messages from the producer to the consumer, via spontaneously formed Delay Tolerant Networks (DTNs). Common to most protocols is the assumption that users are willing to participate in the content distribution network; however, because of the energy restrictions of handheld devices, users’ participation cannot be taken for granted. In this thesis, we design content dissemination protocols that leverage information about user mobility, as well as interest, in order to deliver content, while avoiding overwhelming noninterested users. We explicitly reason about battery consumption of mobile devices to model participation, and achieve fairness in terms of workload distribution. We introduce a dynamic priority scheduling framework, which enables the network to allocate the scarce energy resources available to support the delivery of the most desired messages. We evaluate this work extensively by means of simulation on a variety of real mobility traces and social networks, and draw a comparative evaluation with the major related works in the field

Context and resource awareness in opportunistic network data dissemination

Opportunistic networks are challenging mobile ad hoc networks characterised by frequent disconnections and partitioning. In this paper we focus on data dissemination services, i.e. cases in which data should be disseminated in the network without a priori knowledge about the set of intended destinations. We propose a general autonomic data dissemination framework that exploits information about the userspsila context and social behaviour, to decide how to replicate and replace data on nodespsila buffers. Furthermore, our data dissemination scheme explicitly takes into account resource constraints, by jointly considering the expected utility of data replication and the associated costs. The results we present show that our solution is able to improve data availability, provide fairness among nodes, and reduce the network load, with respect to reference proposals available in the literature