Nowadays, routing proposals must deal with a panoply of heterogeneous devices, intermittent connectivity, and the users’ constant need for communication, even in rather challenging networking scenarios. Thus, we propose a Social-aware Content-based Opportunistic Routing Protocol, SCORP, that considers the users’ social interaction and their interests to improve data delivery in urban, dense scenarios. Through simulations, using synthetic mobility and human traces scenarios, we compare the performance of our solution against other two social-aware solutions, dLife and Bubble Rap, and the social-oblivious Spray and Wait, in order to show that the combination of social awareness and content knowledge can be beneficial when disseminating data in challenged networks.
Besides taking into account the interest that users have in the content, @cite_5 also considers information about the users' social relationships to improve content availability. For that, a utility function is computed for each data object considering the access probability to the object and the involved cost in accessing it, as well as the user's social strength towards the different communities that he she belongs to and or has interacted with. The idea is to have users fetching data objects that maximize the utility function with respect to the local cache limitations, choosing the objects that are of interest to him herself and can be further disseminated in the communities with which they have strong social ties.
@cite_5
2112263843
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.