Opportunistic IoT Service to Support Safety Driving from Heterogeneous Data Sources
- 41 Downloads
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.
KeywordsInternet 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.
- 2.Andreoli, A., Gravina, R., Giannantonio, R., Pierleoni, P., Fortino, G.: SPINE-HRV: a BSN-based toolkit for heart rate variability analysis in the time-domain. In: Wearable and Autonomous Biomedical Devices and Systems for Smart Environment, pp. 369–389. Springer, Berlin (2010)Google Scholar
- 5.Darshana, K., Fernando, M., Jayawadena, S., Wickramanayake, S.: Riyadisi—intelligent driver monitoring system. In: 2013 International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 286–286. IEEE, Piscataway (2013)Google Scholar
- 8.Fortino, G., Russo, W., Savaglio, C., Viroli, M., Zhou, M.: Modeling opportunistic IoT services in open IoT ecosystems. In: Proceedings of 18th Workshop From Objects to Agents, pp. 90–95 (2017)Google Scholar
- 9.Fortino, G., Russo, W., Savaglio, C., Viroli, M., Zhou, M.: Opportunistic cyberphysical services: a novel paradigm for the future internet of things. In: 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), pp. 488–492. IEEE, Piscataway (2018)Google Scholar
- 11.Galarza, E.E., Egas, F.D., Silva, F.M., Velasco, P.M., Galarza, E.D.: Real time driver drowsiness detection based on drivers face image behavior using a system of human computer interaction implemented in a smartphone. In: International Conference on Information Theoretic Security, pp. 563–572. Springer, Berlin (2018)Google Scholar
- 12.Gao, H., Yüce, A., Thiran, J.P.: Detecting emotional stress from facial expressions for driving safety. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5961–5965. IEEE, Piscataway (2014)Google Scholar
- 17.Lisetti, C.L., Nasoz, F.: Affective intelligent car interfaces with emotion recognition. In: Proceedings of 11th International Conference on Human Computer Interaction, Las Vegas. Citeseer (2005)Google Scholar
- 18.Ma, C., Li, Q., Li, W., Gravina, R., Zhang, Y., Fortino, G.: Activity recognition of wheelchair users based on sequence feature in time-series. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3659–3664. IEEE, Piscataway (2017)Google Scholar
- 20.Nass, C., Jonsson, I.M., Harris, H., Reaves, B., Endo, J., Brave, S., Takayama, L.: Improving automotive safety by pairing driver emotion and car voice emotion. In: CHI’05 Extended Abstracts on Human Factors in Computing Systems, pp. 1973–1976. ACM, New York (2005)Google Scholar
- 21.Rezaei, M., Klette, R.: Driver drowsiness detection. In: Computer Vision for Driver Assistance, pp. 95–126. Springer, Berlin (2017)Google Scholar
- 22.Streiffer, C., Raghavendra, R., Benson, T., Srivatsa, M.: DarNet: a deep learning solution for distracted driving detection. In: 18th ACM/IFIP/USENIX Middleware Conference (2017)Google Scholar