Opportunistic IoT Service to Support Safety Driving from Heterogeneous Data Sources

  • Giancarlo Fortino
  • Raffaele Gravina
  • Qimeng Li
  • Claudio SavaglioEmail author
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


The Internet of Things (IoT) represents an ecosystem where heterogeneous components seamlessly interoperate aiming to provide opportunistic (highly contextualized, dynamic, transient, and co-located) cyberphysical services in every application scenario, including smart automotive. Just in the context of advanced driving assistance systems, this paper proposes a modeling approach supporting the interactions among multiple Smart Objects (SOs, like Smartphone, Smart Bracelet, Smart Cushion, etc.) within the vehicle, in order to retrieve information regarding driver psycho-physical status and to alert if risky conditions (i.e., distraction, drowsiness, high stress level, or aggressive behaviors) are detected. The outlined “Driving Assistance Service” is expected to collect contextualized data from heterogeneous SOs (not purposely designed for implementing such service nor for interoperating), to perform data fusion and analysis, and finally to provide multimodal alerts on the driver’s smartphone. The goal of this work, indeed, is to show that the proposed metamodel-based approach facilitates the implementation of such an integrated IoT service, also improving the embedded and closed ADASs currently available at the state-of-the-art.


Internet of Things Opportunistic services Sensor data fusion Safety driving assistance Driver monitoring 



This work has been carried out under the framework of INTER-IoT, Research and Innovation action—Horizon 2020 European Project, Grant Agreement #687283, financed by the EU.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Informatics, Modeling, Electronics and SystemsUniversity of CalabriaRendeItaly

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