ABSTRACT Over the past few years, wireless networking technologies have made vast forays into our daily lives. Today, one can find 802.11 hardware and other personal wireless technology employed at homes, shopping malls, coffee shops and airports. Present-day wireless network deployments bear two important properties: they are unplanned, with most access points (APs) deployed by users in a spontaneous manner, resulting in highly variable AP densities; and they are unmanaged, since manually configuring and managing a wireless network is very complicated. We refer to such wireless deployments as being chaotic.

Abstract — Understanding user mobility is critical for simulations of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17%. I.

Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.

Recent research has shown that one can use Distributed Hash Tables (DHTs) to build scalable, robust and efficient applications. One question that is often left unanswered is that of simplicity of implementation and deployment. In this paper, we explore a case study of building an application for which ease of deployment dominated the need for high performance. The application we focus on is Place Lab, an end-user positioning system. We evaluate whether it is feasible to use DHTs as an application-independent building block to implement a key component of Place Lab: its “mapping infrastructure.” We present Prefix Hash Trees, a data structure used by Place Lab for geographic range queries that is built entire on top of a standard DHT. By strictly layering Place Lab’s data structures on top of a generic DHT service, we were able to decouple the deployment and management of Place Lab from that of the underlying DHT. We identify the characteristics of Place Lab that made it amenable for deploying in this layered manner, and comment on its effect on performance.

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.

The broadcast and tetherless nature of wireless networks and the widespread deployment of Wi-Fi hotspots makes it easy to remotely locate a user by observing her wireless signals. Location is private information and can be used by malicious individuals for blackmail, stalking, and other privacy violations. In this paper, we analyze the problem of location privacy in wireless networks and present a protocol for improving location privacy. Our basic approach is to obfuscate several types of privacy-compromising information revealed by a mobile node, including sender identity, time of transmission, and signal strength. Our design is driven by realsystem implementation and field experiments along with analysis and simulations. Our system allows users to choose the level of privacy they desire, thereby increasing the performance of less private users (while not sacrificing private users ’ privacy at the same time). We evaluated our system based on real-life mobility data and wireless LAN coverage. Our results show that a user of our system can be indistinguishable from a thousand users in the same coverage area.

This paper presents Virgil, an automatic access point discovery and selection system. Unlike existing systems that select access points based entirely on received signal strength, Virgil scans for all available APs at a location, quickly associates to each, and runs a battery of tests to estimate the quality of each AP’s connection to the Internet. Virgil also probes for blocked or redirected ports, to guide AP selection in favor of preserving application services that are currently in use. Results of our evaluation across five neighborhoods in three cities show Virgil finds a usable connection from 22% to 100 % more often than selecting based on signal strength alone. By caching AP test results, Virgil both improves performance and success rate. Our overhead is acceptable and is shown to be faster than manually selecting an AP with Windows XP.

As mobile devices continue to shrink, users are no longer merely nomads, but truly mobile, employing devices on the move. At the same time, these users no longer rely on a single managed network, but exploit a wide variety of connectivity options as they spend their day. Together, these trends argue that systems must consider the derivative of connectivity— the changes inherent in movement between separately managed networks, with widely varying capabilities. To manage the derivative of connectivity, we exploit the fact that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device’s owner, and customizes a predictive mobility model for that specific user. Rather than rely on a synthetic model or aggregate observations, this custom-tailored model can be used together with past observations of wireless network capabilities to generate connectivity forecasts. Applications can in turn use these forecasts to plan future network use with confidence. We have built a BreadCrumbs prototype, and evaluated it with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications. 1

Abstract. This paper examines the positioning accuracy of a GSM beaconbased location system in a metropolitan environment. We explore five factors effecting positioning accuracy: location algorithm choice, scan set size, simultaneous use of cells from different providers, training and testing on different devices, and calibration data density. We collected a 208-hour, 4350Km driving trace of three different GSM networks covering the Seattle metropolitan area. We show a median error of 94m in downtown and 196m in residential areas using a single GSM network and the best algorithm for each area. Estimating location using multiple providers ’ cells reduces median error to 65-134 meters and 95 % error to 163m in the downtown area, which meets the accuracy requirements for E911. We also show that a small 60-hour calibration drive is sufficient for enabling a metropolitan area similar to Seattle. 1

A growing number of mobile computing applications are centered around the user’s location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone’s camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87 % when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.