Wireless spectrum is a scare resource, but in practice much of it is underused by current owners. To enable better use of this spectrum, we propose an auction approach that leverages dynamic spectrum access techniques to allocate spectrum in a secondary market. These are markets where spectrum owners can either sell or lease spectrum to other parties. Unlike previous auction approaches, we seek to take advantage of the ability to share spectrum among some bidders while respecting the needs of others for exclusive use. Thus, unlike unlicensed spectrum (e.g., Wi-Fi), which can be shared by any device, and exclusive-use licensed spectrum, where sharing is precluded, we enable efficient allocation by supporting sharing alongside quality-of-service protections. We present SATYA (Sanskrit for "truth"), a strategyproof and scalable spectrum auction algorithm whose primary contribution is in the allocation of a right to contend for spectrum to both sharers and exclusive-use bidders. Achieving strategyproofness in our setting requires appropriate handling of the externalities created by sharing. Using realistic Longley-Rice-based propagation modeling and data from the FCC's CDBS database, we conduct extensive simulations that demonstrate SATYA's ability to handle heterogeneous agent types involving different transmit powers and spectrum needs.

The 2010 FCC ruling on white spaces proposes relying on a database of incumbents as the primary means of determining white space availability at any white space device (WSD). While the ruling provides broad guidelines for the database, the specifics of its design, features, implementation, and use are yet to be determined. Furthermore, architecting a network where all WSDs rely on the database raises several systems and networking challenges that have remained unexplored. Also, the ruling treats the database only as a storehouse for incumbents. We believe that the mandated use of the database has an additional opportunity: a means to dynamically manage the RF spectrum. Motivated by this opportunity, in this paper, we present SenseLess, a database-driven white spaces network. As suggested by its very name, in SenseLess, WSDs rely on a database service to determine white spaces availability as opposed to spectrum sensing. The service, using a combination of an up-to-date database of incumbents, sophisticated signal propagation modeling, and an efficient content dissemination mechanism to ensure efficient, scalable, and safe white space network operation. We build, deploy, and evaluate SenseLess and compare our results to ground truth spectrum measurements. We present the unique system design considerations that arise due to operating over the white spaces. We also evaluate its efficiency and scalability. To the best of our knowledge, this is the first paper that identifies and examines the systems and networking challenges that arise from operating a white space network, which is solely dependent on a channel occupancy database.

Networking over UHF white spaces is fundamentally different from conventional Wi-Fi along three axes: spatial variation, temporal variation, and fragmentation of the UHF spectrum. Each of these differences gives rise to new challenges for implementing a wireless network in this band. We present the design and implementation of Net7, the first Wi-Fi like system constructed on top of UHF white spaces. Net7 incorporates a new adaptive spectrum assignment algorithm to handle spectrum variation and fragmentation, and proposes a low overhead protocol to handle temporal variation. builds on a simple technique, called SIFT, that reduces the time to detect transmissions in variable channel width systems by analyzing raw signals in the time domain. We provide an extensive evaluation of the system in terms of a prototype implementation and detailed experimental and simulation results.

Determining node and event locations is a canonical task for many wireless network applications. Yet dedicated infrastructure for determining position information is expensive, energy-consuming, and simply unavailable in many deployment scenarios. This paper presents an accurate, cheap and scalable framework, called Sextant, for determining node position and event location in sensor networks. Sextant operates by setting up and solving a system of geographic constraints based on connectivity information from the underlying communication network. Sextant achieves high accuracy by enabling non-convex constraints to be used to refine position estimates. It represents position estimates as potentially non-contiguous collections of points. This general representation enables Sextant to use _negative information_, that is, information on where a node or event is not located, to refine location estimates. Sextant unifies both node and event detection within the same general framework. It can provide high precision without dedicated localization hardware by aggressively extracting constraints from the link layer, representing areas precisely with Bezier-enclosed polygons and probability distributions, and using event detection to refine node position estimates. A compact representation and a fully distributed implementation make the framework practical for resource-limited devices. The framework has been implemented, deployed and tested on laptops, PDAs and Mica-2 motes. Physical experiments show that a large number (98%) of the nodes in a network can determine their positions based on a small number (30%) of landmark nodes and that a large number (90%) of events can be located with low median error.

The execution time of computationally-intensive applications such as protein folding and fractal generation can be reduced by implementing these applications as Web services that run in parallel. Additionally, some of these Web services may save state periodically to resume execution later on. However, currently, there is no solution to load balance this class of Web services, and to replicate the saved state for the purposes of resumption. This paper describes the architecture of JULIET, a system that load balances .NET Web services across a Windows cluster in a distributed fashion. The system is also fault tolerant since it supports failovers and replication of data generated by the Web services at the application level. The system is designed to be minimally-visible to the Web service and the client that consumes it.