MADServer: An Architecture for Opportunistic Mobile Advanced Delivery

Rapid increases in cellular data traffic demand creative alternative delivery vectors for data. Despite the conceptual attractiveness of mobile data offloading, no concrete web server architectures integrate intelligent offloading in a production-ready and easily deployable manner without relying on vast infrastructural changes to carriers’ networks. Delay-tolerant networking technology offers the means to do just this. We introduce MADServer, a novel DTN-based architecture for mobile data offloading that splits web con- tent among multiple independent delivery vectors based on user and data context. It enables intelligent data offload- ing, caching, and querying solutions which can be incorporated in a manner that still satisfies user expectations for timely delivery. At the same time, it allows for users who have poor or expensive connections to the cellular network to leverage multi-hop opportunistic routing to send and receive data. We also present a preliminary implementation of MADServer and provide real-world performance evaluations

The Context of Coordinating Groups in Dynamic Mobile Networks

International audienceContext-awareness in dynamic and unpredictable environments is a well-studied problem, and many approaches handle sensing, understanding, and acting upon context information. Entities in these environments are not in isolation, and oftentimes the manner in which entities coordinate depends on some (implicit) notion of their shared context. In this paper, we are motivated by the need to explicitly construct notions of the context of a group that can support better coordination within the group. First we identify an efficient representation of context (both of an individual and of a group) that can be shared across wireless connections without incurring a significant communication overhead. Second we provide precise semantics for different types of groups, each with compelling use cases in these dynamic computing environments. Finally, we define and demonstrate protocols for efficiently computing groups and their context in a distributed manner