Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, location-aware web search) become interesting only when the underlying location system is available ubiquitously and is not limited to a single office environment. Unfortunately, the installation and calibration overhead involved for most of the existing research systems is too prohibitive to imagine deploying them across, say, an entire city. In this work, we evaluate the feasibility of building a wide-area 802.11 Wi-Fi-based positioning system. We compare a suite of wireless-radio-based positioning algorithms to understand how they can be adapted for such ubiquitous deployment with minimal calibration. In particular, we study the impact of this limited calibration on the accuracy of the positioning algorithms. Our experiments show that we can estimate a user’s position with a median positioning error of 13–40 meters (depending upon the characteristics of the environment). Although this accuracy is lower than existing positioning systems, it requires substantially lower calibration overhead and provides easy deployment and coverage across large metropolitan areas. 1

Abstract. Location estimation is an important part of many ubiquitous computing systems. Particle filters are simulation-based probabilistic approximations which the robotics community has shown to be effective for tracking robots ’ positions. This paper presents a case study of applying particle filters to location estimation for ubiquitous computing. Using trace logs from a deployed multi-sensor location system, we show that particle filters can be as accurate as common deterministic algorithms. We also present performance results showing it is practical to run particle filters on devices ranging from high-end servers to handhelds. Finally, we discuss the general advantages of using probabilistic methods in location systems for ubiquitous computing, including the ability to fuse data from different sensor types and to provide probability distributions to higher-level services and applications. Based on this case study, we conclude that particle filters are a good choice to implement location estimation for ubiquitous computing. 1

Abstract. NearMe is a server, algorithms, and application programming interfaces (APIs) for clients equipped with 802.11 wireless networking (Wi-Fi) to compute lists of people and things that are physically nearby. NearMe compares clients ’ lists of Wi-Fi access points and signal strengths to compute the proximity of devices to one another. Traditional location sensing systems compute and compare absolute locations, which requires extensive a priori calibration and configuration. Because we base NearMe entirely on proximity information, NearMe works “out of the box ” with no calibration and minimal setup. Many “location-aware ” applications only require proximity information, and not absolute location: examples include discovering nearby resources, sending an email to other persons who are nearby, or detecting synchronous user operations between mobile devices. As more people use the system, NearMe grows in both the number of places that can be found (e.g. printers and conference rooms) and in the physical range over which other people and places can be found. This paper describes our algorithms and infrastructure for proximity sensing, as well as some of the clients we have implemented for various applications. 1

Abstract: The physical locations of clients and access points in a wireless LAN may have a large impact on network performance. However, today’s WLAN management tools do not provide information about the location of clients apart from which access point they associate with. In this paper, we describe a scalable and easy-to-deploy WLAN management system that includes a self-configuring location estimation engine. Our system has been in operation on one floor of our building for several months. Using our system to observe WLAN usage in our building, we show that information about client locations is crucial for understanding WLAN performance. Although WLAN location systems are a widely studied topic, the novel aspects of our location system primarily relate to ease of deployment. The main contribution of this paper is to show the utility of officegranularity location in performing wireless management tasks. 1

In recent years, positioning systems for indoor areas using the existing wireless local area network infrastructure have been suggested. Such systems make use of location fingerprinting rather than time or direction of arrival techniques for determining the location of mobile stations. While experimental results related to such positioning systems have been presented, there is a lack of analytical models that can be used as a framework for designing and deploying the positioning systems. In this paper, we present an analytical model for analyzing such positioning systems. We develop the framework for analyzing a simple positioning system that employs the Euclidean distance between a sample signal vector and the location fingerprints of an area stored in a database. We analyze the effect of the number of access points that are visible and radio propagation parameters on the performance of the positioning system and provide some preliminary guidelines on its design.

Abstract. Recognition of everyday physical activities is difficult due to the challenges of building informative, yet unobtrusive sensors. The most widely deployed and used mobile computing device today is the mobile phone, which presents an obvious candidate for recognizing activities. This paper explores how coarse-grained GSM data from mobile phones can be used to recognize high-level properties of user mobility, and daily step count. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collectors over a period of one month, yielding an overall average accuracy of 85%, and a daily step count number that reasonably approximates the numbers determined by several commercial pedometers. 1

Abstract. We present the design, prototype implementation, and evaluation of CenceMe, a personal sensing system that enables members of social networks to share their sensing presence with their buddies in a secure manner. Sensing presence captures a user’s status in terms of his activity (e.g., sitting, walking, meeting friends), disposition (e.g., happy, sad, doing OK), habits (e.g., at the gym, coffee shop today, at work) and surroundings (e.g., noisy, hot, bright, high ozone). CenceMe injects sensing presence into popular social networking applications such as Facebook, MySpace, and IM (Skype, Pidgin) allowing for new levels of “connection ” and implicit communication (albeit non-verbal) between friends in social networks. The CenceMe system is implemented, in part, as a thin-client on a number of standard and sensor-enabled cell phones and offers a number of services, which can be activated on a per-buddy basis to expose different degrees of a user’s sensing presence; these services include, life patterns, my presence, friend feeds, social interaction, significant places, buddy search, buddy beacon, and “above average?” 1

Abstract. Contactless smart cards are used in access control and payment systems. This paper illustrates an attack which effectively allows an attacker to ‘borrow ’ the victim’s card for a short period without requiring physical access to the victim’s card. As a result the legitimate owner will remain unaware of the attack. We show that our hardware successfully executed a relay attack against an ISO 14443A contactless smart card, up to a distance of 50 m. Simply relaying information between the card and reader over a longer distance does not require the same technical resources from the attacker as hardware tampering or cryptanalysis. This attack is therefore a feasible method for circumventing current security protocols with little effort. Since application-level measures fail to protect against relay attacks, we discuss possible solutions involving characteristics of the physical communication medium.

Abstract. Distance-bounding protocols aim to prevent an adversary from pretending that two parties are physically closer than they really are. We show that proposed distance-bounding protocols of Hu, Perrig and Johnson (2003), Sastry, Shankar and Wagner (2003), and Čapkun and Hubaux (2005, 2006) are vulnerable to a guessing attack where the malicious prover preemptively transmits guessed values for a number of response bits. We also show that communication channels not optimized for minimal latency imperil the security of distance-bounding protocols. The attacker can exploit this to appear closer himself or to perform a relaying attack against other nodes. We describe attack strategies to achieve this, including optimizing the communication protocol stack, taking early decisions as to the value of received bits and modifying the waveform of transmitted bits. We consider applying distance-bounding protocols to constrained devices and evaluate existing proposals for distance bounding in ad hoc networks. 1

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply to some extent users ‟ interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction, and report on a personalized friend & location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system a particular individual‟s visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories, and recommend each user a group of potential friends in a GIS community. Third, we estimate an individual‟s interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchicalgraph-based similarity measurement (HGSM), is proposed to uniformly model each individual‟s location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people‟s outdoor movements, 2) the visited popularity of a geospatial region and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate