Covidmonitor is a crowdsensing tool to support epidemologists and public health authorities in monitoring the covid-19 pandemic. The tool collects data to support transdisciplinary studies aiming at improving the knowledge of the pandemic evolution as well as monitor the citizens' behaviour and mental health. Covidmonitor leverages a previously existing mobile crowdsensing platform, SenseMyCity, adapted in collaboration with epidemology, public health and psychology researchers. Our biggest challenge was to identify the relevant metrics for the target trans-disciplinary studies and map them to collectable data. Covidmonitor explores the concept of citizens as probes to sample collective behaviour. The mobile application launches questionnaires about hygiene practices, use of personal protection equipment, health and emotional state. The questionnaires are triggered by different logic, adequate to the multi-dimensional perspectives of the target studies. Covidmonitor also seamlessly collects relevant mobility data without significant battery consumption. Finally, it enables voluntary sharing of location and symptom history, to facilitate tracing in case of infection. The tool considers user privacy and data minimisation by design, and is currently under preliminary scrutiny of the data protection regulator in Portugal.

3D maps of urban environments are useful in various fields ranging from cellular network planning to urban planning and climatology. These models are typically constructed using expensive techniques such as manual annotation with 3D modeling tools, extrapolated from satellite or aerial photography, or using specialized hardware with depth sensing devices. In this work, we show that 3D urban maps can be extracted from standard GNSS data, by analyzing the received satellite signals that are attenuated by obstacles, such as buildings. Furthermore, we show that these models can be extracted from low-accuracy GNSS data, crowdsourced opportunistically from standard smartphones during their user's uncontrolled daily commute trips, unleashing the potential of applying the principle to wide areas. Our proposal incorporates position inaccuracies in the calculations, and accommodates different sources of variability of the satellite signals' SNR. The diversity of collection conditions of crowdsourced GNSS positions is used to mitigate bias and noise from the data. A binary classification model is trained and evaluated on multiple urban scenarios using data crowdsourced from over 900 users. Our results show that the generalization accuracy for a Random Forest classifier in typical urban environments lies between 79% and 91% on 4 m wide voxels, demonstrating the potential of the proposed method for building 3D maps for wide urban areas.

3D maps of urban environments are useful in various fields, from cellular network planning to urban planning and climatology. We show that 3D urban maps can be extracted from received satellite signals that are attenuated by obstacles, such as buildings, from low-accuracy GNSS data, crowdsourced opportunistically from standard smartphones during their user's uncontrolled daily commute trips. Our proposal incorporates position inaccuracies in the calculations, and the diversity of collection conditions of crowdsourced GNSS positions is used to mitigate bias and noise from the data. A binary classification model is trained and evaluated on multiple urban scenarios. Our results show that the generalization accuracy for a Random Forest classifier lies between 79% and 91%, demonstrating the potential of the proposed method for building 3D maps for wide urban areas. In the demo, we show multiple 3D visualizations of the various processing stages, which can be viewed interactively using Google Earth, allowing hands-on exploration of the work.

Smart cities aim to improve the citizens' quality of life by leveraging information about urban scale processes extracted from heterogeneous data sources collected on citywide deployments. The Internet-of-Things (IoT) is, thus, the enabler of smart city technologies at urban scale. In this paper, we present PortoLivingLab, a multisource sensing infrastructure that leverages IoT technology to achieve city-scale sensing of four phenomena: weather, environment, public transport, and people flows. To sense these processes on a city scale, we deployed a vehicular network with over 600 vehicles and 19 static environmental sensors. We also developed an easily reconfigurable crowdsensing platform and carried out several crowdsensing campaigns with more than 600 participants. The data is collected in a common backend and stored using similar spatio-temporal data models to simplify sharing and joint analysis for the characterization of urban dynamics. We describe the architecture and composing elements of PortoLivingLab, highlighting the IoT technologies, and challenges faced. We present several proof-of-concept use cases (e.g., passenger flows from WiFi connections) that provide new insights into different components of an evolving and moving city. Finally, we lay out the future lines of work that will strive for finding hidden phenomena by leveraging data from the three complementary platforms.

The experience of daily stress among bus drivers has shown to affect physical and psychological health, and can impact driving behavior and overall road safety. Although previous research consistently supports these findings, little attention has been dedicated to the design of a stress detection method able to synchronize physiological and psychological stress responses of public bus drivers in their day-to-day routine work. To overcome this limitation, we propose a mobile sensing approach to detect georeferenced stress responses and facilitate memory recall of the stressful situations. Data were collected among public bus drivers in the city of Porto, Portugal (145 h, 36 bus drivers, +2300 km), and results supported the validation of our approach among this population and allowed us to determine specific stressor categories within certain areas of the city. Furthermore, data collected throughout the city allowed us to produce a citywide “stress map” that can be used for spotting areas in need of local authority intervention. The enriching findings suggest that our system can be a promising tool to support applied occupational health interventions for public bus drivers and guide authorities' interventions to improve these aspects in “future” cities.