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

Unified radio and network control across heterogeneous hardware platforms

Experimentation is an important step in the investigation of techniques for handling spectrum scarcity or the development of new waveforms in future wireless networks. However, it is impractical and not cost effective to construct custom platforms for each future network scenario to be investigated. This problem is addressed by defining Unified Programming Interfaces that allow common access to several platforms for experimentation-based prototyping, research, and development purposes. The design of these interfaces is driven by a diverse set of scenarios that capture the functionality relevant to future network implementations while trying to keep them as generic as possible. Herein, the definition of this set of scenarios is presented as well as the architecture for supporting experimentation-based wireless research over multiple hardware platforms. The proposed architecture for experimentation incorporates both local and global unified interfaces to control any aspect of a wireless system while being completely agnostic to the actual technology incorporated. Control is feasible from the low-level features of individual radios to the entire network stack, including hierarchical control combinations. A testbed to enable the use of the above architecture is utilized that uses a backbone network in order to be able to extract measurements and observe the overall behaviour of the system under test without imposing further communication overhead to the actual experiment. Based on the aforementioned architecture, a system is proposed that is able to support the advancement of intelligent techniques for future networks through experimentation while decoupling promising algorithms and techniques from the capabilities of a specific hardware platform

Opportunistic Third-Party Backhaul for Cellular Wireless Networks

With high capacity air interfaces and large numbers of small cells, backhaul -- the wired connectivity to base stations -- is increasingly becoming the cost driver in cellular wireless networks. One reason for the high cost of backhaul is that capacity is often purchased on leased lines with guaranteed rates provisioned to peak loads. In this paper, we present an alternate \emph{opportunistic backhaul} model where third parties provide base stations and backhaul connections and lease out excess capacity in their networks to the cellular provider when available, presumably at significantly lower costs than guaranteed connections. We describe a scalable architecture for such deployments using open access femtocells, which are small plug-and-play base stations that operate in the carrier's spectrum but can connect directly into the third party provider's wired network. Within the proposed architecture, we present a general user association optimization algorithm that enables the cellular provider to dynamically determine which mobiles should be assigned to the third-party femtocells based on the traffic demands, interference and channel conditions and third-party access pricing. Although the optimization is non-convex, the algorithm uses a computationally efficient method for finding approximate solutions via dual decomposition. Simulations of the deployment model based on actual base station locations are presented that show that large capacity gains are achievable if adoption of third-party, open access femtocells can reach even a small fraction of the current market penetration of WiFi access points.Comment: 9 pages, 6 figure