Radu Ioan Ciobanu

Social aspects to support opportunistic networks in an academic environment

As wireless and 3G networks become more crowded, users with mobile devices have difficulties in accessing the network. Opportunistic networks, created between mobile phones using local peer-to-peer connections, have the potential to solve such problems by dispersing some of the traffic to neighboring smartphones. Recently various opportunistic routing or dissemination algorithms were proposed and evaluated in different scenarios emulating real-world phenomena as close as possible. In this paper we present an experiment performed at the Politehnica University of Bucharest in which we collected social and mobiltity data to evaluate opportunistic routing and dissemination algorithms. We present an analysis of our findings, highlighting key social and mobility behavior factors that can influence such opportunistic solutions. Most importantly, we show that by adding knowledge such as social links between participants in an opportunistic network routing and dissemination algorithms can be greatly improved.

SPRINT: Social prediction-based opportunistic routing

Opportunistic networks are mobile networks that rely on the store-carry-and-forward paradigm, using contacts between nodes to opportunistically transfer data. For this reason, traditional routing mechanisms are no longer suitable. To increase the probability of successfull message delivery, we propose SPRINT, an opportunistic routing algorithm that introduces an additional routing criterion: online social information about nodes. Furthermore, previous results show that, for particular environments, contacts between devices in opportunistic networks are highly predictable. When users follow rare events-based mobility patterns, we show that human mobility can be approximated as a Poisson distribution. Based on this result, we add an additional prediction component into our routing algorithm. Our solution delivers better results compared to traditional social-based routing approaches, for different real-world and synthetic mobility scenarios.

Reducing Congestion for Routing Algorithms in Opportunistic Networks with Socially-Aware Node Behavior Prediction

Since mobile devices nowadays have become ubiquitous, several types of networks formed over such devices have been proposed. One such approach is opportunistic networking, which is based on a store-carry-and-forward paradigm, where nodes store data and carry it until they reach a suitable node for forwarding. The problem in such networks is how to decide which the next hop will be, since nodes do not have a global view of the network. An inefficient opportunistic routing algorithm can lead to the congestion of a network, because same groups of nodes send messages between each other, without the destination actually receiving the data (or receiving it with a high delay). We describe here a routing algorithm for opportunistic networks that avoids congestion and the overcrowding of nodes, by routing messages only to nodes that have a high chance of reaching a messages destination. This is performed with the use of social networks and node behavior prediction. We show that our algorithm outperforms existing algorithms such as BUBBLE Rap in terms of delivery cost and hit rate, as well as the rate of congestion introduced in the networks.

SPRINT-SELF: Social-Based Routing and Selfish Node Detection in Opportunistic Networks

Since mobile devices nowadays have become ubiquitous, several types of networks formed over such devices have been proposed. One such approach is represented by opportunistic networking, which is based on a store-carry-and-forward paradigm, where nodes store data and carry it until they reach a suitable node for forwarding. The problem in such networks is how to decide what the next hop will be, since nodes do not have a global view of the network. We propose using the social network information of a node when performing routing, since a node is more likely to encounter members of its own social community than other nodes. In addition, we approximate a node’s contact as a Poisson distribution and show that we can predict its future behavior based on the contact history. Furthermore, since opportunistic network nodes may be selfish, we improve our solution by adding a selfish node detection and avoidance mechanism, which can help reduce the number of unnecessary messages sent in the network, and thus avoid congestion and decrease battery consumption. We show that our algorithm outperforms existing solutions such as BUBBLE Rap and Epidemic in terms of delivery cost and hit rate, as well as the rate of congestion introduced in the network, by testing in various realistic scenarios.

Social Aspects for Opportunistic Communication

As wireless and 3G networks become more crowded, users with mobile devices experience difficulties in accessing the network. Opportunistic networks, created between mobile phones using local peer-to-peer connections, have the potential to solve such problems by dispersing some of the traffic to neighbouring smart phones. Recently various opportunistic routing and dissemination algorithms were proposed and evaluated in various scenarios emulating real-world phenomena as close as possible. Such algorithms generally rely on mobility patterns of users and the context of communication. In this we investigate the addition of social data to improve the performance of communication algorithms and data transmission schema. When the routing decision is influenced by the chance of a particular user being able to successfully carry the data to the next hop, we believe that opportunistic communication algorithms could greatly benefit not only from learning the behaviour of users, but also their history of contacts coupled with the online social familiarity patterns between them. We believe users tend to be in contact more with familiar sets of users, with whom they share common interests. We investigate our approach using two real-world traces collected in two different environments. We first investigate our hypothesis using mobility data collected in an indoor academic environment. We then evaluate our assumptions in an outdoor urban scenario. We present an analysis of our findings, highlighting key social and mobility behaviour factors that can influence such opportunistic solutions. Most importantly, we show that by adding knowledge such as social links between participants in an opportunistic network routing and dissemination algorithms can be greatly improved.

Total order in opportunistic networks

Opportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.

Opportunistic Networks: A Taxonomy of Data Dissemination Techniques

When mobile devices are unable to establish direct communication, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to facilitate communication through opportunistic networks. These types of networks, formed when mobile devices communicate only using short-range transmission protocols, usually when users are close, can help applications still exchange data. Routes are built dynamically, since each mobile device is acting according to the store-carry-and-forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks data dissemination is usually based on a publish/subscribe model. Opportunistic data dissemination also raises questions concerning user privacy and incentives. In this the authors present a motivation of using opportunistic networks in various real life use cases, and then analyze existing relevant work in the area of data dissemination. The authors present the categories of a proposed taxonomy that captures the capabilities of data dissemination techniques used in opportunistic networks. Moreover, the authors survey relevant techniques and analyze them using the proposed taxonomy.

Social-awareness in opportunistic networking

Since uninterrupted connectivity has become such an important part of everyday life, the amount of energy consumed by the various devices used has increased considerably. We propose a way to limit this consumption by employing opportunistic networks, which are mainly composed of mobile devices that have no need for a static network infrastructure. They communicate when in range, using a store-carry-and-forward paradigm. We believe that opportunistic networks can be deployed in closed environments if there is a certainty that messages sent in the network eventually reach their destination. Therefore, we propose the addition of social data to existing opportunistic routing algorithms. We investigate our approach using two traces collected in different environments and we present an analysis of our findings. Most importantly, we show that by adding knowledge such as social links between participants, the performance of the opportunistic network can be improved.