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 WhiteFi, the first Wi-Fi like system constructed on top of UHF white spaces. WhiteFi incorporates a new adaptive spectrum assignment algorithm to handle spectrum variation and fragmentation, and proposes a low overhead protocol to handle temporal variation. WhiteFi 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 system evaluation in terms of a prototype implementation and detailed experimental and simulation results.

There are a nuniher of scenarios where it is desirable to have a wireless device connect to multiple networks simullaneowly. Currently, this i5 possible only hy using multiple wireleqs network cards in the device. Unfortunately, using nwltiple wireless cards causes excessive energy drain and consequent reduction of lifetime in hattery operated devices. In this paper, we prnpose a software hased approach, called MultiNet, that facilitates simultaneous connections to multiple networks hy virtualizini a single wireless card. The wireless card is virtualized hy : introducing an intermediate layer helow IP. which continuously switches the card acrms multiple networks. Tlie~ goal o f the switching algorithm is to he transparent to the user who see5 her machine as heing connected to multiple networks. We preent the design, implenientation, and perforninnce of the MnltiNet system. We analyze and evaluate hnWring and sritching algorithnis in terms of delay and energy consumption. Our system is agnostic of the upper layer protocols, and works well over popular IEEE !3002.11 wireless LAN cards. IXTRODUCTIONThere are several papers that articulate the. benefits of virtudimion 141. [71. [151. However. to the hest of our knowledge. the benefits of virtualizing a wireless card has been overlooked. In this paper. we propose MultiNet. a new virtualization architecture that absuacts'a single wireless LAN (WLAN) card to appear as multiple virtual cards to the user. MultiNet allows these virtual cards to be simultaneously connected to physically different wireless networks. We describe our architecture in detail and then present a buffering protocol and two switching algorithms that give good performance for many common applications. such as telnet. ftp. file sharing Our. research is motivated by several compelling~scenarios that are enabled with the above functionality. These scenarios include: increased connectivity for end users; increased range of the wireless network: bridging between infrastructure and ad hoc wireless networks. and painless secure access to sensitive resources. We discuss these in detail in Section 11.To enable these scenarios with current technology. one has to use a single WLAN card for each desired network. However. this is costly. cumbersome. and consumes energy resources that are often limited. An alternative to using more hardware -and web downloads.-. .. is to use MultiNet and its accompanying protocols. MultiNet requires changes to the data link or device driver layer o f the networking stack. It creates and manages multiple network stacks and maintains the associated state information for each network that the card is connected to. Simultaneous connectivity over all networks is achieved by switching the card hetween the desired networks and activating the corresponding stack.An advantage of this architecture is that it allows applications and protocols like TCP/IP to work without any changes.In this paper we make the following four research contributions:. We present a new architecture for virtualizi...

We study a fundamental yet under-explored facet in wireless communication -the width of the spectrum over which transmitters spread their signals, or the channel width. Through detailed measurements in controlled and live environments, and using only commodity 802.11 hardware, we first quantify the impact of channel width on throughput, range, and power consumption. Taken together, our findings make a strong case for wireless systems that adapt channel width. Such adaptation brings unique benefits. For instance, when the throughput required is low, moving to a narrower channel increases range and reduces power consumption; in fixed-width systems, these two quantities are always in conflict.We then present SampleWidth, a channel width adaptation algorithm for the base case of two communicating nodes. This algorithm is based on a simple search process that builds on top of existing techniques for adapting modulation. Per specified policy, it can maximize throughput or minimize power consumption. Evaluation using a prototype implementation shows that SampleWidth correctly identities the optimal width under a range of scenarios. In our experiments with mobility, it increases throughput by more than 60% compared to the best fixed-width configuration.

No abstract

Battery life is a critical performance and user experience metric on mobile devices. However, it is difficult for app developers to measure the energy used by their apps, and to explore how energy use might change with conditions that vary outside of the developer's control such as network congestion, choice of mobile operator, and user settings for screen brightness. We present an energy emulation tool that allows developers to estimate the energy use for their mobile apps on their development workstation itself. The proposed techniques scale the emulated resources including the processing speed and network characteristics to match the app behavior to that on a real mobile device. We also enable exploring multiple operating conditions that the developers cannot easily reproduce in their lab. The estimation of energy relies on power models for various components, and we also add new power models for components not modeled in prior works such as AMOLED displays. We also present a prototype implementation of this tool and evaluate it through comparisons with real device energy measurements.

No abstract

The FCC ruling on Nov 4th, 2008 on white spaces has opened up the possibility of wireless network deployments over white spaces, i.e., vacant UHF TV channels. A key requirement for any white space device (WSD) is that it must ensure that none of of its transmissions interfere with incumbents, namely TV transmitters and wireless microphones. The FCC ruling proposes two techniques for WSDs to meet these requirements: spectrum sensing and the use of a geo-location database. A host of prior work has focussed on building better spectrum sensing techniques for WSDs to determine those parts of teh spectrum that are currently occupied by primaries. While potentially feasible, this approach is technically challenging. Hence, in this paper we propose SenseLess, an alternate design and approach towards building a white spaces network. As suggested by the very name, in SenseLess, WSDs rely less on spectrum sensing to determine white spaces availability. Instead, they primarily rely on 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 this infrastructure service and compare our results to ground truth spectrum measurements. We present the unique system design considerations in the design and implementation of SenseLess that arise due to operating over the white spaces. We also evaluate the efficiency and scalability of SenseLess.

Localizing the sources of performance problems in large enterprise networks is extremely challenging. Dependencies are numerous, complex and inherently multi-level , spanning hardware and software components across the network and the computing infrastructure. To exploit these dependencies for fast, accurate problem localization, we introduce an Inference Graph model, which is well-adapted to user-perceptible problems rooted in conditions giving rise to both partial service degradation and hard faults. Further, we introduce the Sherlock system to discover Inference Graphs in the operational enterprise, infer critical attributes, and then leverage the result to automatically detect and localize problems. To illuminate strengths and limitations of the approach, we provide results from a prototype deployment in a large enterprise network, as well as from testbed emulations and simulations. In particular, we find that taking into account multi-level structure leads to a 30% improvement in fault localization, as compared to two-level approaches.