Quantifying content consistency improvements through opportunistic contacts

Abstract

The sharing and dissemination of online content is one of the main purposes of social network applications, and the amount of content accessed through them, in particular through portable devices such as smartphones and PDAs, is expected to increase. Consumption of online content, however, does not require a continuous online presence. Content can be downloaded, consumed, modified, and uploaded at different times. An opportunity to improve a user's access to up-to-date information from its own social network is to take advantages of opportunistic contacts between mobile devices, \ie without waiting for connectivity to the network infrastructure. In other words, users of a social network application may receive more fresh content with no extra infrastructure deployment, simply by communicating with mobile devices of other users, in a delay-tolerant manner. Assessing the magnitude of this improvement is, however, challenging. For example, the frequency and patterns of such contacts are partly a function of the social connectivity of users, and so will be the availability of relevant information to share and more importantly the willingness to share that information. All these influence in non-trivial ways the gains that can be realized through opportunistic contacts. The paper's main contribution is in providing a quantitative handle through which these gains can be estimated, while accounting for the above factors. ABSTRACTContacts between mobile users provide opportunities for data updates that supplement infrastructure-based mechanisms. While the benefits of such opportunistic sharing are intuitive, quantifying the capacity increase they give rise to is challenging because both contact rates and contact graphs depend on the structure of the social networks users belong to. Furthermore, social connectivity influences not only users' interests, i.e., the content they own, but also their willingness to share data with others. All these factors can have a significant effect on the capacity gains achievable through opportunistic contacts. This paper's main contribution is in developing a tractable model for estimating such gains in a content update system, where content originates from a server along multiple channels, with blocks of information in each channel updated at a certain rate, and users differ in their contact graphs, interests, and willingness to share content, e.g., only to the members of their own social networks. We establish that the added capacity available to improve content consistency through opportunistic sharing can be obtained by solving a convex optimization problem. The resulting optimal policy is evaluated using traces reflecting contact graphs in different social settings and compared to heuristic policies. The evaluation demonstrates the capacity gains achievable through opportunistic sharing, and the impact on those gains of the structure of the underlying social network.

Social similarity as a driver for selfish, cooperative and altruistic behavior

“…In a more abstract setting, the effect that a node's relational position in the group has on content dissemination has been considered in [4]. The importance of designing socially-aware opportunistic networks is also demonstrated in [2], [5], [7]. Finally, for a review of data dissemination in the general context of opportunistic networks, readers are referred to [3].…”

Content distribution and protocol design issue for mobile social networks: a survey

“…The social pattern of users influences both the interest and their willingness called selfish behavior for sharing the content of among the nodes [43,64]. In [44], social pattern of mobile node such as duration of time and frequency of the contact called social tie strength is used to calculate the contact pattern of the communicating nodes to build the required social community for opportunistic contacts.…”

Optimization based Approach for Content Distribution in Hybrid Mobile Social Networks

“…The approaches that mainly based on social network analysis such as degree centralities, tie strength, a distance of the links and the node, and mobility pattern is implemented to distribute the data efficiently among the social nodes of the MSN. The affect of the social patterns not only influence the node's interest but their willingness as well (i.e., selfish behavior) for sharing their content [10]. In [1] Peer-to-peer streaming is proposed to enable end-to-end users to communicate and utilize the available resources in the network to share video content.…”