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Fingerprinting-based indoor localization involves building a signal strength radio map. This map is usually built manually by a person holding the mapping device, which results in orientation-dependent fingerprints due to signal attenuation by the human body. To offset this distortion, fingerprints are typically collected for multiple orientations, but this requires a high effort for large localization areas. In this paper, we propose an approach to reduce the mapping effort by modeling the WLAN signal attenuation caused by the human body. By applying the model to the captured signal to compensate for the attenuation, it is possible to generate an orientationindependent fingerprint. We demonstrate that our model is location and person independent and its output is comparable with manually created radio maps. By using the model, the WLAN scanning effort can be reduced by 75% to 87.5% (depending on the number of orientations).

Pervasive computing envisions seamless support for user tasks through cooperating devices that are present in an environment. Fluctuating availability of devices, induced by mobility and failures, requires mechanisms and algorithms that allow applications to adapt to their ever‐changing execution environments without user intervention. To ease the development of adaptive applications, Becker et al. (3) have proposed the peer‐based component system PCOM. This system provides fundamental mechanisms to support the automated composition of applications at runtime. In this article, we discuss the requirements on algorithms that enable automatic configuration of pervasive applications. Furthermore, we show how finding a configuration can be interpreted as Distributed Constraint Satisfaction Problem. Based on this, we present an algorithm that is capable of finding an application configuration in the presence of strictly limited resources. To show the feasibility of this algorithm, we present an evaluation based on simulations and real‐world measurements and we compare the results with a simple greedy approximation.

Public bus services are widely deployed in cities around the world because they provide cost-effective and economic public transportation. However, from a passenger point of view urban bus systems can be complex and difficult to navigate, especially for disadvantaged users, i.e. tourists, novice users, older people, and people with impaired cognitive or physical abilities. We present Urban Bus Navigator (UBN), a reality-aware urban navigation system for bus passengers with the ability to recognize and track the physical public transport infrastructure such as buses. Unlike traditional location-aware mobile transport applications, UBN acts as a true navigation assistant for public transport users. Insights from a six-month long trial in Madrid indicate that UBN removes barriers for public transport usage and has a positive impact on how people feel about public transport journeys.