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PortoLivingLab: An IoT-Based Sensing Platform for Smart Cities

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 pape...

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Bibliographic Details
Published in:IEEE internet of things journal 2018-04, Vol.5 (2), p.523-532
Main Authors: Santos, Pedro M., Queiros, Cristina, Sargento, Susana, Aguiar, Ana, Barros, Joao, Rodrigues, Joao G. P., Cruz, Susana B., Lourenco, Tiago, d'Orey, Pedro M., Luis, Yunior, Rocha, Cecilia, Sousa, Sofia, Crisostomo, Sergio
Format: Article
Language:English
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Summary: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.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2018.2791522