We present a practical, measurement-based model that captures the effect of interference in 802.11-based wireless LAN or mesh networks. The goal is to model capacity of any given link in the presence of any given number of interferers in a deployed network, carrying any specified amount of offered load. Central to our modeling approach is a MAC-layer model for 802.11 that is fed by PHY-layer models for deferral and packet capture behaviors, which in turn are profiled based on measurements. The target network to be evaluated needs only O(N) measurement steps to gather metrics for individual links that seed the models. We provide two solution approaches – one based on direct simulation (slow, but accurate) and the other based on analytical methods (faster, but approximate). We present elaborate validation results for a 12 node 802.11b mesh network using upto 5 interfering transmissions. We demonstrate, using as comparison points three simpler modeling approaches, that the accuracy of our approach is much better, predicting link capacities with errors within 10 % of the base channel datarate for about 90% of the cases.

Accurately selecting modulation rates for time-varying channel conditions is critical for avoiding performance degradations due to rate overselection when channel conditions degrade or underselection when channel conditions improve. In this paper, we design a custom cross-layer framework that enables (i) implementation of multiple and previously unimplemented rate adaptation mechanisms, (ii) experimental evaluation and comparison of rate adaptation protocols on controlled, repeatable channels as well as residential urban and downtown vehicular and non-mobile environments in which we accurately measure channel conditions with 100-µs granularity, and (iii) comparison of performance on a per-packet basis with the ideal modulation rate obtained via exhaustive experimental search. Our evaluation reveals that SNR-triggered protocols are susceptible to overselection from the ideal rate when the coherence time is low (a scenario that we show occurs in practice even in a nonmobile topology), and that “in-situ ” training can produce large gains to overcome this sensitivity. Another key finding is that a mechanism effective in differentiating between collision and fading losses for hidden terminals has severely imbalanced throughput sharing when competing links are even slightly heterogeneous. In general, we find trained SNRbased protocols outperform loss-based protocols in terms of the ability to track vehicular clients, accuracy within outdoor environments, and balanced sharing with heterogeneous links (even with physical layer capture).

In order to evaluate, improve, or expand a deployed, citywide wireless mesh network, it is necessary to assess the network’s spatial performance. In this paper, we present a general framework to accurately predict a network’s wellserved area, termed the metric region, via a small number of measurements. Assessment of deployed networks must address two key issues: non-uniform physical-layer propagation and high spatial variance in performance. Addressing nonuniformity, our framework estimates a mesh node’s metric region via a data-driven sectorization of the region. We find each sector’s boundary (radius) with a two-stage process of estimation and then measurement-driven “push-pull ” refinement of the estimated boundary. To address high spatial variation, our coverage estimation couples signal strength measurements with terrain information from publicly available digital maps to estimate propagation characteristics between a wireless node and the client’s location. To limit measurements and yield connected metric regions, we consider performance metrics (such as signal strength) to be monotonic with distance from the wireless node within each sector. We show that despite measured violations in coverage monotonicity, we obtain high accuracy with this assumption. We validate our estimation and refinement framework with measurements from 30,000 client locations obtained in each of two currently operational mesh networks, Google-WiFi and TFA. We study three illustrative metrics: coverage, modulation rate, and redundancy, and find that to achieve a given accuracy, our framework requires two to five times fewer measurements than grid sampling strategies. Finally, we use the framework to evaluate the two deployments and study the average size and location of their coverage holes as well as the impact of client association policies on load-balancing.

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. Categories and Subject Descriptors:

While WiFi was initially designed as a local-area access network, mesh networking technologies have led to increasingly expansive deployments of WiFi networks. In urban environments, the WiFi mesh frequently supplements a number of existing access technologies, including wired broadband networks, 3G cellular, and commercial WiFi hotspots. It is an open question what role city-wide WiFi deployments play in the increasingly diverse access network spectrum. We study the usage of the Google WiFi network deployed in Mountain View, California, and find that usage naturally falls into three classes, based almost entirely on client device type. Moreover, each of these classes of use has significant geographic locality, following the distribution of residential, commercial, and transportation areas of the city. Finally, we find a diverse set of mobility patterns that map well to the archetypal use cases for traditional access technologies.

Deployments of city-wide multi-hop 802.11 networks introduce challenges for maintaining client performance at vehicular speeds. We experimentally demonstrate that current network interfaces employ policies that result in long outage durations, even when clients are always in range of at least one access point. Consequently, we design and evaluate a family of client-driven handoff techniques that target vehicular mobility in multi-tier multi-hop wireless mesh networks. Our key technique is for clients to invoke an association change based on (i) joint use of channel quality measurements and AP quality scores that reflect long-term differences in AP performance and (ii) controlled measurement and hand-off time scales to balance the need for the instantaneously best association against performance penalties incurred from spurious handoffs due to channel fluctuations and marginally improved associations. We utilize a 4,000 user urban deployment to evaluate the performance of a broad class of hand-off policies.

In this paper, we present the vision for an open, urban-scale wireless networking testbed, called CitySense, with the goal of supporting the development and evaluation of novel wireless systems that span an entire city. CitySense is currently under development and will consist of about 100 Linux-based embedded PCs outfitted with dual 802.11a/b/g radios and various sensors, mounted on buildings and streetlights across the city of Cambridge. CitySense takes its cue from citywide urban mesh networking projects, but will differ substantially in that nodes will be directly programmable by end users. The goal of CitySense is explicitly not to provide public Internet access, but rather to serve as a new kind of experimental apparatus for urban-scale distributed systems and networking research efforts. In this paper we motivate the need for CitySense and its potential to support a host of new research and application developments. We also outline the various engineering challenges of deploying such a testbed as well as the research challenges that we face when building and supporting such a system. 1

This thesis presents a measurement-parameterized performance study of deploy-ment factors in wireless mesh networks using four performance metrics: client cov-erage area, backhaul tier connectivity, protocol-dependent throughput, and per-user fair rates. For each metric, I identify and study deployment factors which strongly influence mesh performance via an extensive set of Monte Carlo simulations capturing realistic physical layer behavior. My findings include: (i) A random topology is un-suitable for a large-scale mesh deployment due to doubled node density requirements, yet a moderate level of perturbations from ideal grid placement has minor impact. (ii) Multiple backhaul radios per mesh node is a cost-effective deployment strategy as it leads to mesh deployments costing 50 % less than with a single-radio architecture. This work adds to the understanding of mesh deployment factors and their general impact on performance, providing further insight into practical mesh deployments. Acknowledgments First and foremost, I would like to thank my advisor, Dr. Edward Knightly, for the guidance, support, and opportunities he has provided me. He has been a

A series of complex dependencies conspire to make it difficult to model mobile networks, including mobility, channel and radio characteristics, and power consumption. To address these challenges, we have designed and built a testbed for large-scale mobile experimentation, called the Diverse Outdoor Mobile Environment. DOME consists of computer-equipped buses, battery-powered nomadic nodes, organic WiFi APs, and a municipal WiFi mesh network. While the construction of a testbed such as DOME presents a significant engineering challenge, this paper describes a concrete set of scientific results derived from this experience. We argue that a broad range of mobility experiments could be performed in a testbed which provides the properties of temporal, technological, and spatial diversity. We demonstrate these properties in our testbed through analysis of data collected from DOME over a period of four years. Finally, we use DOME to provide insight into several open problems in mobile systems research. 1.

In this paper, we characterize the achievable rate region for any 802.11-scheduled static multi-hop network. To do so, we first characterize the achievable edge-rate region, that is, the set of edge rates that are achievable on the given topology. This requires a careful consideration of the inter-dependence among edges, since neighboring edges collide with and affect the idle time perceived by the edge under study. We approach this problem in two steps. First, we consider two-edge topologies and study the fundamental ways by which they interact. Then, we consider arbitrary multi-hop topologies, compute the effect that each neighboring edge has on the edge under study in isolation, and combine to get the aggregate effect. We then use the characterization of the achievable edge-rate region to characterize the achievable rate region. We verify the accuracy of our analysis by comparing the achievable rate region derived from simulations with the one derived analytically. We make a couple of interesting and somewhat surprising observations while deriving the rate regions. First, the achievable rate region with 802.11 scheduling is not necessarily convex. Second, the performance of 802.11 is surprisingly good. For example, in all the topologies used for model verification, the max-min allocation under 802.11 is at least 64 % of the max-min allocation under a perfect scheduler.