Walk this way! An IoT-based urban routing system for smart cities

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 162; p. 106857
• Main Authors: Pimpinella, Andrea, Redondi, Alessandro E.C., Cesana, Matteo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 24.10.2019; Elsevier Sequoia S.A
• Subjects: Air pollution; Air quality; Data retrieval; Experiments; Graphical user interface; Internet of Things; Interpolation; Kriging interpolation; Model accuracy; Outdoor air quality; Pedestrians; Route selection; Sensors; Smart cities; Sound pressure; Spatial interpolation; Temporal forecasting; Urban environments; Urban routing; Spatial interpolation; Internet of things; Urban routing; Temporal forecasting; Smart cities
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2019.07.013

Future smart cities are expected to change radically the way people live, interact and move in urban environments. This will be possible thanks to the massive amount of data that will be generated by ubiquitously deployed sensor devices through the Internet of Things paradigm. Indeed, solutions able to improve the quality of urban mobility for citizens are of particular interests. As a matter of fact, they are a key objective for many municipal administrations as well as one of the priority themes of the European Commission. In this context, this work proposes an advanced smart urban routing service named SURF, which is specifically thought for pedestrians and cyclists willing to move inside a city. The system allows to retrieve the best route between a source location and a destination according to user-defined objective function (e.g., selecting the route with the best air quality or with the lowest average temperature). This is possible through the interaction with a federation of IoT testbeds, deployed worldwide. This paper comments on the implementation and the evaluation of the proposed system, focusing on both the backend (data retrieval and spatio/temporal data interpolation and forecasting operations) and the front-end (graphical user interface). We assess the performance of several spatial interpolation and temporal prediction models, to understand their relationship with the particular sensor measurements (air pollution, temperature, sound pressure level, etc.). We show through experiments that for what concerns spatial interpolation, Universal Kriging is generally able to perform well across all sensor measurements and can be selected as a generic interpolation strategy. As for temporal prediction, experiments highlight a tradeoff between model accuracy and look-ahead capability. We note that short and mid-term prediction methods show satisfactory performance across all sensor measurements. Finally, subjective and objective experiments demonstrate the positive impact of IoT-based solutions for smart routing on urban citizens.