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.

Low-power wide-area networks are extending beyond the conventional terrestrial domain. Coastal zones, rivers, wetlands, among others, are nowadays common deployment settings for Internet-of-Things nodes where communication technologies such as LoRa are becoming popular. In this article, we investigate large-scale fading dynamics of LoRa line-of-sight links deployed over an estuary with characteristic intertidal zones, considering both shore-to-shore and shore-to-vessel communications. We propose a novel methodology for path loss prediction which captures i) spatial, ii) temporal and iii) physical features of the RF signal interaction with the environmental dynamics, integrating those features into the two-ray propagation model. To this purpose, we resort to precise hydrodynamic modeling of the estuary, including the specific terrain profile ( bathymetry ) at the reflection point. These aspects are key to accounting for a reflecting surface of varying altitude and permittivity as a function of the tide. Experimental measurements using LoRa devices operating in the 868 MHz band show major trends on the received signal power in agreement with the methodology’s predictions.

Many wireless applications require a propagation model that describes the attenuation of the transmitted signal as a function of the distance between devices. Such channel models are derived commonly from signal strength measurements, and assume that the true distances between wireless terminals are known. In practice, however, the true distances may be unavailable or difficult to obtain, for instance in mobile scenarios or in the absence of line-of-sight. These conditions typically occur in forested environments, urban areas, etc. This paper addresses the problem of path loss model parameter estimation in presence of erroneous distance measurements, such as the ones derived from the GPS positions. We provide a model for the uncertainties, and study the impact of distance errors on the estimation of a log-distance channel model. Our main conclusion is that the path loss model can be estimated with a reasonable accuracy from unreliable distances, provided that the measurements are taken at distances beyond a few standard deviations of the GPS positioning error. In case the maximum communication range does not allow such large distances, we provide a method to correct the erroneous channel model. Real-world measurements are used in order to validate our approach.

In urban road transportation, intersections are traffic bottlenecks with increased waiting delays and associated adverse effects. A recently proposed intelligent intersection management (IIM) approach, the Synchronous Intersection Management Protocol (SIMP), synchronizes the vehicles access to simple single-lane isolated intersections, outperforming competing approaches in various performance metrics. In this paper, we apply SIMP to multi-lane intersections, increasing significantly the applicability of the protocol while dealing with the additional complexity emerging from the multiple crossing conflicts. Using the SUMO simulator, we compare the performance of SIMP with two conventional (Round-Robin - RR and Trivial Traffic Light Control - TTLC) and two IIM approaches (Intelligent Traffic Light Control - ITLC and Q-learning based Traffic Light Control - QTLC) under continuous and interrupted upstream traffic flows scenarios in urban settings. The results using a maximum speed of 30 km/h confirm the superiority of SIMP, improving traffic throughput (~ 14.4%) and reducing travel delays (~ 64.4%) and associated fuel consumption (~ 25.5%) when compared to the best of the other approaches.

A critical step towards smarter and safer cities is to endow them with the abilities to massively gather a wide variety of data sets and to automatically feed those data to decision support tools and applications that leverage artificial intelligence. We present UrbanSense, a platform deployed on the streets of a mid-size European city (Porto, Portugal) to collect key environmental data. The main innovations of UrbanSense are (1) design for affordability and extensibility, (2) its ability to leverage heterogeneous networks to send the data to the cloud (using both real-time and delay-tolerant communications), and (3) its Internet of Things integration to expose the data streams to smart city tools and applications. Beyond discussing the design choices, we present operational results for 6 months of operation and give a detailed account of the challenges faced by the successful deployment of urban sensing technologies in the wild.