Catálogo de publicaciones - libros

In this paper we present a user-centric position sensing system that is based on asynchronous, independent ultrasonic beacons. These stationary transmitter units are small, cheap to manufacture, and have power requirements low enough to run each from a small solar cell and a nearby light source. Each beacon is programmed to emit a short, 40 kHz ultrasonic signal with a unique transmission period. The mobile receiver unit first associates a received signal with a beacon based on the observed periodicity, then measures the Doppler shift in the periodicity that results from movements of the receiver. Using Doppler shifts from a number of different beacons, the receiver is able to estimate both its position and velocity by employing a particle filter. In this paper, we describe our positioning algorithm, the hardware, and proof-of-concept results.

Fiducial scene markers provide inexpensive vision-based location systems that are of increasing interest to the Pervasive Computing community. Already established in the Augmented Reality (AR) field, markers are cheap to print and straightforward to locate in three dimensions. When used as a component of a smart environment, however, there are issues of obscuration, insufficient camera resolution and limited numbers of unique markers. This paper looks at the advantages of clustering multiple markers together to gain resilience to these real world problems. It treats the visual channel as an erasure channel and relevant coding schemes are applied to decode data that is distributed across the marker cluster using an algorithm that does not require each tag to be individually numbered. The advantages of clustering are determined to be a resilience to obscuration, more robust position and pose determination, better performance when attached to inconvenient shapes, and an ability to encode more than a database key into the environment. A real world example comparing the positioning capabilities of a cluster of tags with that of a single tag is presented. It is apparent that clustering provides a position estimate that is more robust, without requiring external definition of a co-ordinate frame using a database.

In the area of pervasive computing a key concept is context-awareness. One type of context information is location information of wireless network clients. Research in indoor localization of wireless network clients based on signal strength is receiving a lot of attention. However, not much of this research is directed towards handling the issue of adapting a signal strength based indoor localization system to the hardware and software of a specific wireless network client, be it a tag, PDA or laptop. Therefore current indoor localization systems need to be manually adapted to work optimally with specific hardware and software. A second problem is that for a specific hardware there will be more than one driver available and they will have different properties when used for localization. Therefore the contribution of this paper is twofold. First, an automatic system for evaluating the fitness of a specific combination of hardware and software is proposed. Second, an automatic system for adapting an indoor localization system based on signal strength to the specific hardware and software of a wireless network client is proposed. The two contributions can then be used together to either classify a specific hardware and software as unusable for localization or to classify them as usable and then adapt them to the signal strength based indoor localization system.

We have developed a location-aware sightseeing support system for visitors to KOTOHIRAGU Shrine, using only popular mobile phones employing the gpsOne system. Its design is not a map-based navigation system, but a shared virtual world system like multi-player online role-playing games. We conducted an experiment recruiting 29 subjects from real tourists visiting the shrine, who had their own compatible GPS-phones. From the survey, we have found that location-aware sightseeing support system using mobile phones can be accepted by young people, but the generation gap is wider than expected.

We present a novel method for mapping and localization in indoor environments using a wearable gesture interface. The ear-mounted FreeDigiter device consists of an infrared proximity sensor and a dual axis accelerometer. A user builds a topological map of a new environment by walking through the environment wearing our device. The accelerometer is used to identify footsteps while the proximity sensor detects doorways. While mapping an environment, finger gestures are used to label detected doorways. Once a map is constructed, a particle filter is employed to track a user walking through the mapped environment while wearing the device. In this tracking mode, the device can be used as a context-aware gesture interface by responding to finger gestures differently according to which room the user occupies. We present experimental results for both mapping and localization in a home environment.

We present a map modelling toolkit that meets the special requirements of pedestrian navigation in intelligent environments. Its central component is a graphical editor, which supports geometric modelling of architectural ground plans through polygon meshes. Multiple levels and their interconnections, such as ramps and staircases, can be represented through the aid of layers. In order to support a full range of activities, from travelling to interacting with pervasive user interfaces, coarse models on an outdoor scale can be hierarchically refined by submodels on building and room scales. The XML-encoded models can be useful for positioning systems, referencing spatial context and for route finding through multi-story buildings. Besides the editor, the toolkit provides a routing module for pedestrian navigation.

Ubiquitous computing requires ready access to information that is relevant to users’ context – especially information relevant to their current location. Applications on our personal devices should be able to autonomously and continuously harvest the information provided at that location and interrupt us only when it is important to do so. Currently, client devices are designed for explicit querying for information rather than continuous background harvesting of relevant information. To enable ubiquitous access to location-specific information, we can take advantage of the widespread deployment of WiFi networks. There is a wealth of location-specific information that network providers are willing to make publicly available to any users. However, today’s models for accessing wireless networks do not easily support this due primarily to concerns over security and bandwidth utilization. In this paper, we present and compare the different methods that can be applied to solve the problem of continuous background access to location-specific information. Specifically, we compare client-pull and server-push models and show how tradeoffs can be made involving privacy, power consumption on devices, and utilization of wireless bandwidth. We also present three applications and discuss how the tradeoffs affect their design.

In mobile scenarios, privacy is an aspect of growing importance. In order to avoid the creation of movement profiles, participating nodes change their identifying properties on a regular basis in order to hide their identities and stay anonymous. The drawback of this action is that nodes which previously had a connection have no means to recognise this fact. A complete re-authentication would be necessary – if possible at all. This paper discusses this new problem and proposes two possible solutions for re-identification of anonymous nodes, one based on symmetric encryption and one based on secure hashes.

In location-based community services (LBCSs) , the positions of several targets are interrelated. Users can be notified when targets approach or separate from each other. Typical application areas are instant messaging, mobile gaming, dating, fleet management and logistics, as well as child tracking. Finding appropriate anonymization techniques for LBCSs is a hard problem since (i) the targets are continuously monitored and (ii) identifiers of the targets must not change in order to maintain coherence within a community. LBCSs are inherently stateful. Therefore, existing anonymization techniques for location-based services are not suited for LBCSs. In this paper, we present an anonymization technique for LBCSs, which employs distance-preserving coordinate transformations in conjunction with pseudonyms. It is based on the idea that for determining the distance between targets only relative positions are needed. It supports target anonymity, either with respect to the location provider, which collects the position fixes, or the LBS provider. The paper also presents the results of simulations, which we have performed in order to evaluate the proposed mechanism.

The automatic estimation of the user’s current interruptibility is important to seamlessly adapt a device’s behaviour to the user’s situation. Different people differ in the way they rate their interruptibility. In this paper we investigate three options how to adapt an interruptibility estimation system to a particular user: by finding prototypical users, using experience sampling, or using knowledge of prototypical situations. We have experimentally tested all three approaches on a data set of 94 situations that have been annotated by 24 different users.