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.

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.

We address the problem of localization in vehicular ad hoc networks. Our goal is to leverage vehicle communications and smartphone sensors to improve the overall localization performance. Assuming vehicles are equipped with the IEEE 802.11p wireless interfaces, we employ a two-stage Bayesian filter to track the vehicle's position: an unscented Kalman filter for heading estimation using smartphone inertial sensors, and a particle filter that fuses vehicle-to-vehicle signal strength measurements received from mobile anchors whose positions are uncertain, with velocity, GPS position, and map information. Our model leads to a robust localization system and is able to provide useful position information even in the absence of GPS data. We evaluate the algorithm performance using real-world measurements collected from four communicating vehicles in an urban scenario, and considering different combinations of location information sources.

A modular system architecture based on separate compression and network coding is known to be theoretically suboptimal for relevant classes of sensor networks with correlated sources. Motivated by this observation, we present a feasible solution for joint source and network coding with distortion constraints. By choosing encoders that are simple scalar index assignments, we are able to move the complexity to the destination decoder. Given the network topology and the correlation structure of the data, our algorithms solve the problem of finding encoder and decoder instances that minimize the mean square error of every sample. A proof-of-concept and the complexity analysis of the proposed algorithms underline the effectiveness of our factor graph approach. The presented schemes are shown to yield low-distortion estimates of the collected data even in scenarios where a modular solution would fail.