Catálogo de publicaciones - libros

Today, most mobile applications use geo-referenced points of interest (POIs) on location-based maps to call the user’s attention to interesting spots in the surroundings. The presentation of both, maps and POIs, is commonly location-based but not yet adapted to the individual user’s needs and situation. To foster the user’s information perception by emphasising the location-based information that is most relevant to the individual user, we propose the POI concept – the modeling, processing, and visualisation of POIs. We introduce a data model for POIs, supporting the exchange of POIs and define an architecture to process and present POIs in cooperation with a mobile information system. With the integration of context-awareness into POIs, we contribute to the development of innovative location- and context-aware mobile applications.

This paper presents the design, implementation, and evaluation of a footprint-based indoor location system on traditional Japanese GETA sandals. Our footprint location system can significantly reduce the amount of infrastructure required in the deployed environment. In its simplest form, a user simply has to put on the GETA sandals to track his/her locations without any setup or calibration efforts. This makes our footprint method easy for everywhere deployment. The footprint location system is based on the dead-reckoning method. It works by measuring and tracking the displacement vectors along a trial of footprints (each displacement vector is formed by drawing a line between each pair of footprints). The position of a user can be calculated by summing up the current and all previous displacement vectors. Additional benefits of the footprint based method are that it does not have problems found in existing indoor location systems, such as obstacles, multi-path effects, signal noises, signal interferences, and dead spots. However, the footprint based method has a problem of accumulative error over distance traveled. To address this issue, it is combined with a light RFID infrastructure to correct its positioning error over some long distance traveled.

We present an improvement to ultrasound–based indoor location systems like Cricket [1]. By encoding and modulating the ultrasound pulses, we are able to achieve greater accuracy in distance measurements. Besides improving the distance measurements, we improve the position update rate by synchronizing the active beacons. We also propose a method that could further improve the update rate by superimposing encoded ultrasound pulses. Further, an experimental evaluation of our improvements is presented.

In recent years, wireless LAN technologies have experienced unprecedented growth, and new services and problems have occurred. In this paper, we propose a position estimation technique using directional antennas to assist the detection of wireless access points. Using an asymmetric model for estimation, our technique can radicalize probability distribution quicker than using a symmetric model. Our technique consists of three steps. The first measures the current position of the user, the direction of the antenna and the received signal strength of a target wireless access point. The second step estimates the position of the wireless access point from measured data using a signal strength model based on directivity. And the final step presents estimated results that assist the user. These steps are repeated for real-time assistance. We also conducted an evaluation experiments to clarify the effectiveness of our proposed technique.

Location contexts are important for many context-aware applications. A significant location is a specialized form of location context for expressing a user’s daily activity. We propose a method to cluster positions measured by cellular phones into significant locations with multiple radii. Cellular phones we used are equipped with a positioning system, where data can be taken in low frequency with wide-varying estimated errors. In order to learn significant locations, our system exploits multiple radii for coping with these characteristics and for adapting to a variety of users’ spatial behavioral patterns. We also discuss appropriate parameters for our clustering method.

In this paper, we investigate the deficiency of Goyal and Egenhofer’s method for modeling cardinal directional relations between simple regions and provide the computational model based on the concept of mathematical morphology, which can be a complement and refinement of Goyal and Egenhofer’s model for crisp regions. To the best of our knowledge, the cardinal directional relations between fuzzy regions have not been modeled. Based on fuzzy set theory, we extend Goyal and Egenhofer’s model to handle fuzziness and provide a computational model based on alpha-morphology, which combines fuzzy set theory and mathematical morphology, to refine the fuzzy cardinal directional relations. Then the computational problems are investigated. The definitions for the cardinal directions are not important and we aim to present the methodology and power of using fuzzy morphology to model directional relations. We also give an example of spatial configuration in 2-dimentional discrete space. The experiment results confirm the cognitive plausibility of our computational models.

A major issue in Pervasive Computing in order to design and implement context–aware applications is to correlate information provided by distributed devices to furnish a more comprehensive view of the context they habit. Such a correlation activity requires considering a spatial model of this environment, even if the kind of information processed is not only of spatial nature. This paper focuses on the notions of place and conceptual spatial relation to present a commonsense formal model of space supporting reasoning about meaningful correlation. The model consists of a relational structure that can be viewed as the semantic specification for a hybrid logic language, whose formulas represent contextual information and whose satisfiability procedures enhance reasoning, allowing the local perspective typical of many approach to context–awareness.

This paper outlines work in progress related to the construction of a system which is able to deliver information in a contextually sensitive manner within a pervasive computing environment, through the use of semantic and knowledge technologies. Our approach involves modelling of task and domain as well as location and device. We discuss ideas and steps already taken in the development of prototype components, and outline our future work in this area.

There is increasing interest in location-aware systems and applications. It is important for any designer of such systems and applications to understand the nature of user and device mobility. Furthermore, an understanding of the effect of user mobility on access points (APs) is also important for designing, deploying, and managing wireless networks. Although various studies of wireless networks have provided insights into different network environments and user groups, it is often hard to apply these findings to other situations, or to derive useful abstract models.

In this paper, we present a general methodology for extracting mobility information from wireless network traces, and for classifying mobile users and APs. We used the Fourier transform to convert time-dependent location information to the frequency domain, then chose the two strongest periods and used them as parameters to a classification system based on Bayesian theory. To classify mobile users, we computed (the maximum distance between any two APs visited by a user during a fixed time period) and observed how this quantity changes or repeats over time. We found that user mobility had a strong period of one day, but there was also a large group of users that had either a much smaller or much bigger primary period. Both primary and secondary periods had important roles in determining classes of mobile users. Users with one day as their primary period and a smaller secondary period were most prevalent; we expect that they were mostly students taking regular classes. To classify APs, we counted the number of users visited each AP. The primary period did not play a critical role because it was equal to one day for most of the APs; the secondary period was the determining parameter. APs with one day as their primary period and one week as their secondary period were most prevalent. By plotting the classes of APs on our campus map, we discovered that this periodic behavior of APs seemed to be independent of their geographical locations, but may depend on the relative locations of nearby APs. Ultimately, we hope that our study can help the design of location-aware services by providing a base for user mobility models that reflect the movements of real users.

This paper investigates the efficiency of in-door next location prediction by comparing several prediction methods. The scenario concerns people in an office building visiting offices in a regular fashion over some period of time. We model the scenario by a dynamic Bayesian network and evaluate accuracy of next room prediction and of duration of stay, training and retraining performance, as well as memory and performance requirements of a Bayesian network predictor. The results are compared with further context predictor approaches – a state predictor and a multi-layer perceptron predictor using exactly the same evaluation set-up and benchmarks. The publicly available Augsburg Indoor Location Tracking Benchmarks are applied as predictor loads. Our results show that the Bayesian network predictor reaches a next location prediction accuracy of up to 90% and a duration prediction accuracy of up to 87% with variations depending on the person and specific predictor set-up. The Bayesian network predictor performs in the same accuracy range as the neural network and the state predictor.