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IoTRec: The IoT Recommender for Smart Parking System
TELECOM SudParis, FRA.
University of Cantabria, ESP.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0001-7368-4448
University of Cantabria, ESP.
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2022 (English)In: IEEE Transactions on Emerging Topics in Computing, E-ISSN 2168-6750, Vol. 10, no 1, p. 280-296Article in journal (Refereed) Published
Abstract [en]

This paper proposes a General Data Protection Regulation (GDPR)-compliant Internet of Things (IoT) Recommender (IoTRec) system, developed in the framework of H2020 EU-KR WISE-IoT (Worldwide Interoperability for Semantic IoT) project, which provides the recommendations of parking spots and routes while protecting users' privacy. It provides recommendations by exploiting the IoT technology (parking and traffic sensors). The IoTRec provides four-fold functions. Firstly, it helps the user to find a free parking spot based on different metrics (such as the nearest or nearest trusted parking spot). Secondly, it recommends a route (the least crowded or the shortest route) leading to the recommended parking spot from the user's current location. Thirdly, it provides the real-time provision of expected availability of parking areas (comprised of parking spots organized into groups) in a user-friendly manner. Finally, it provides a GDPR-compliant implementation for operating in a privacy-aware environment. The IoTRec is integrated into the smart parking use case of the WISE-IoT project and is evaluated by the citizens of Santander, Spain through a prototype, but it can be applied to any IoT-enabled locality. The evaluation results show the citizen's satisfaction with the quality, functionalities, ease of use and reliability of the recommendations/services offered by the IoTRec.

Place, publisher, year, edition, pages
IEEE Computer Society, 2022. Vol. 10, no 1, p. 280-296
Keywords [en]
GDPR, Intelligent sensors, Internet of Things (IoT), parking statistics, Prototypes, recommendations, Semantics, smart parking, Urban areas, Vehicles
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:bth-21078DOI: 10.1109/TETC.2020.3014722Scopus ID: 2-s2.0-85099554640OAI: oai:DiVA.org:bth-21078DiVA, id: diva2:1529007
Funder
EU, Horizon 2020, 723156Available from: 2021-02-17 Created: 2021-02-17 Last updated: 2022-08-09Bibliographically approved

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Fricker, Samuel

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