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.

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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 off-the-shelf radio transceivers. 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 transceiver 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 and describe how multiple transceivers can be used to overcome the absorption effects of the human body. We validate our findings through an extensive set of measurements gathered in a home environment. Our tests indicate that by following a set of simple guidelines, we can increase the localization accuracy (the percentage of correct location estimations) by a factor of four (from 20% to 88%), and reduce the maximum localization error (from 7 to 4 m).@en

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%.

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