Catálogo de publicaciones - libros

We analyze the feasibility of boostrapping a location service through geocoded postal addresses rather than the common wardriving technique. A location service that contains the MAC addresses and geographic position of wireless LAN access points enables positioning services for WLAN devices and location-aware networking protocols. This work thus compares the accuracy of access point position estimates obtained based on RF signal strengths readings (wardriving) with the accuracy of the geocoded postal address. The results show similar accuracy for geocoding in comparison to typical wardriving studies, with significantly reduced effort if postal addresses of access point positions are known.

Indoor positioning systems based on 802.11 and fingerprints offer reasonably low position errors. We study the deployment, calibration, and measurement factors for position errors by systematically investigating (1) the number of access points, (2) the number of samples in the training phase, (3) the number of samples in the position determination phase, and (4) the setup of the grid of reference points. Further, we bring out the best of the positioning system by selecting advantageous values for these parameters. For our study, we utilize a test environment with a size of about 312 square meters that is covered with 612 reference points arranged in an equally spaced grid.

Continuous logging of a person’s geographical position is required for various “life-log” applications, such as memory aids, automatic blog generation, and life pattern analysis. GPS is one way of logging, but it is unable to track movements indoors, and hence cannot track peoplefs ordinary activities. We propose a WiFi-based location detection technology for location logging. It detects a device’s location from received WiFi beacon signals. It works indoors and outdoors, and its estimated accuracy is often comparable to that of GPS. We built WiFi-based location logging systems based on a smart phone and a keychain-like device using custom hardware. These prototypes record WiFi information every few minutes, and this information is converted into actual location logs. We describe some life patterns created by analyzing these location logs. We also discuss various application examples and ideas for when continuous location logging becomes commonplace.

High-level and longer-term activity recognition has great potentials in areas such as medical diagnosis and human behavior modeling. So far however, activity recognition research has mostly focused on low-level and short-term activities. This paper therefore makes a first step towards recognition of high-level activities as they occur in daily life. For this we record a realistic 10h data set and analyze the performance of four different algorithms for the recognition of both low- and high-level activities. Here we focus on simple features and computationally efficient algorithms as this facilitates the embedding and deployment of the approach in real-world scenarios. While preliminary, the experimental results suggest that the recognition of high-level activities can be achieved with the same algorithms as the recognition of low-level activities.

Increasingly, cell phones are used to browse for information while location systems assist in gathering information that is most appropriate to the user’s current location. We seek to take this one step further and actually overlay information on to the physical world using the cell phone’s camera and thereby minimize a user’s cognitive effort. This “magic lens” approach has many applications of which we are exploring two: indoor building navigation and dynamic directory assistance. In essence, we match “landmarks” identified in the camera image with those stored in a building database. We use two different types of features – floor corners that can be matched against a floorplan and SIFT features that can be matched to a database constructed from other images. The camera’s pose can be determined exactly from a match and information can be properly aligned so that it can overlay directly onto the phone’s image display. In this paper, we present early results that demonstrate it is possible to realize this capability for a variety of indoor environments. Latency is shown to already be reasonable and likely to be improved by further optimizations. Our goal is to further explore the computational tradeoff between the server and phone client so as to achieve an acceptable latency of a few seconds.

In this paper we present an approach to analyzing the social behaviors that occur in a typical office space. We describe a system consisting of over 200 motion sensors connected in a wireless network observing a medium-sized office space populated with almost 100 people for a period of almost a year. We use a representation of the data in the sensor network, which allows us to efficiently evaluate gross patterns of office-wide social behavior of its occupants during expected seasonal changes in the workforce as well as unexpected social events that affect the entire population of the space. We present our experiments with a method based on Kullback-Leibler metric applied to the office activity modelled as a Markov process. Using this approach we detect gross deviations of short term office-wide behavior patterns from previous long-term patterns spanning various time intervals. We compare detected deviations to the company calendar and find and provide some quantitative analysis of the relative impact of those disruptions across a range of temporal scales. We also present a favorable comparison to results achieved by applying the same analysis to email logs.

Location-aware systems provide customised services or applications according to users’ locations. While much research has been carried out in developing models to represent location information and spatial relationships, it is usually limited to modelling simple environments (cf. [13,19,3]). This paper proposes a unified space model for more complex environments (e.g., city plan or forest). This space model provides a flexible, expressive, and powerful spatial representation. It also proposes a new data structure – an integrated lattice and graph model – to express comprehensive spatial relationships. This structure not only provides multiple graphs at different abstraction levels, but it also collapses the whole map into smaller local graphs. This mechanism is beneficial in reducing the complexity of creating and maintaining a map and improving the efficiency of path finding algorithms.

The emerging pervasive computing services will eventually lead to the establishment of a context marketplace, where context consumers will be able to obtain the information they require by a plethora of context providers. In this marketplace, several aspects need to be addressed, such as: support for flexible federation among context stakeholders enabling them to share data when required; efficient query handling based on navigational, spatial or semantic criteria; performance optimization, especially when management of mobile physical objects is required; and enforcement of privacy and security protection techniques concerning the sensitive context information maintained or traded. This paper presents mechanisms that address the aforementioned requirements. These mechanisms establish a robust spatially-enhanced distributed context management framework and have already been designed and carefully implemented.

is a promising location technique for many awareness applications in pervasive computing. However, as research on LF systems goes beyond there is an increasing need for better comparison of proposed LF systems. Developers of LF systems are also lacking good frameworks for understanding different options when building LF systems. This paper proposes a taxonomy to address both of these problems. The proposed taxonomy has been constructed from a literature study of 51 papers and articles about LF. For researchers the taxonomy can also be used as an aid when scoping out future research in the area of LF.

People rapidly learn the capabilities of a new location, without observing every service and product. Instead they map a few observations to familiar clusters of capabilities. This paper proposes a similar approach to computer discovery of routine location capabilities, applying machine learning to predict unobserved capabilities based on a combination of a small body of local observations and a larger body of data that is not specific to the location. We propose using the time and place of deleting items from a to-do list application to provide the local data. For reminder purposes, an area within easy walking distance is a single location, but may contain many different shops and services, collectively offering its own combination of capabilities. Truncated singular value decomposition maps the observations to combinations of features, rather than to a single cluster. Simulations, using distributions derived from real world data, demonstrate the feasibility of this approach.