The wide availability of GPS-enabled devices has fueled the development of location-based applications. Among the most popular are navigation applications that guide users along their way. Due to the limited availability and precision of GPS in typical indoor environments, most navigation applications are limited to outdoor scenarios. To mitigate this, researchers and practitioners have been developing alternative systems to capture the position of a mobile device indoors. Using these systems, they clearly show that it is possible to provide precise location information at a reasonable cost. However, past applications in this domain have often focused exclusively on indoor scenarios.In this paper, we describe an approach to integrate the indoor and outdoor world to provide a seamless hybrid navigation experience. The approach builds upon our past work on indoor localization and integrates with our existing infrastructure for outdoor navigation that has been developed in the GAMBAS European research project. To evaluate the approach, we present data gathered from a real-world deployment as part of the SIMON European research project.

A key challenge for rapidly growing cities of today is to provide effective public transport services to satisfy the increasing demands for urban mobility. Toward this goal, the Internet of Things (IoT) has great potential to overcome existing deficiencies of public transport systems given its ability to embed smart technology into real-life urban contexts. In this paper, we show how this paradigm can be applied to the public transport domain and present the Urban Bus Navigator (UBN), an IoT enabled navigation system for urban bus riders. UBN provides two novel information services for bus users: 1) micro-navigation and 2) crowd-aware route recommendation. Micro-navigation refers to fine-grained contextual guidance of passengers along a bus journey by recognizing boarded bus vehicles and tracking the passenger's journey progress. Crowd-aware route recommendation collects and predicts crowd levels on bus journeys to suggest better and less crowded routes to bus riders. We present the technical system behind the UBN and report on results from an in-the-wild study in Madrid, Spain, that indicates removed barriers for public transport usage and a positive impact on how people feel about bus journeys.

Existing context recognition applications for personal mobile devices are usually fine-tuned to recognize the set of characteristics required to support a particular user task. Outside of a laboratory environment, however, users are often involved in multiple tasks at a time which requires the simultaneous execution of several applications. Yet, due to the energy constraints of most personal mobile devices this approach is inherently limited in scale. In this paper, we show how this problem can be avoided by applying a component-based approach to application development and execution. We present a component system that enables developers to build individual applications in isolation without compromising runtime efficiency. When applications are executed simultaneously, the component system applies a novel technique called configuration folding to automatically remove redundancies. Our experimental evaluation of configuration folding shows that it can save up to 48 percent of energy when applied to a music and a speech detection application, thus, amortizing energy costs after a few seconds of execution.

Indoor localization based on signal strength fingerprinting has received significant attention from the community. This method is attractive because it does not require complex hardware beyond a simple radio transmitter. However, its main limitation is the inaccuracy caused by the variability of the signal strength. When applied to the localization of people, the signal variability can be attributed to three main sources: environmental dynamics (movement of people or objects), movement of transceiver (changes in the position and/or orientation of the transceivers) and body effects (distortion of the wireless signal due to body absorption). Our work focuses on the impact of the last two sources and provides two important contributions. First, we present an analysis to quantify the effects of antenna disorientation and transmitter misplacement. For the RFID system used in our work, these effects can decrease the localization accuracy by up to 50%. Motivated by these results, we identify parts of the human body where tags are less affected by unintentional movements. Second, we describe how multiple transmitters can be used to overcome the absorption effects of the human body. Our results indicate that four transmitters provide a reasonable trade-off between accuracy and hardware cost. We validate our findings through an extensive set of measurements gathered in a home environment. Our tests indicate that by following the guidelines proposed in this paper, the localization accuracy can improve from around 20% up to 88%.