Advertisement

Opportunistic IoT Service to Support Safety Driving from Heterogeneous Data Sources

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

Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

References

  1. 1.
    Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., Savaglio, C.: Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. J. Netw. Comput. Appl. 81, 74–84 (2017)CrossRefGoogle Scholar
  2. 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
  3. 3.
    Casadei, R., Fortino, G., Pianini, D., Russo, W., Savaglio, C.V.M.: Modelling and simulation of opportunistic IoT services with aggregate computing. Futur. Gener. Comput. Syst. 91, 252–262 (2019)CrossRefGoogle Scholar
  4. 4.
    Castignani, G., Derrmann, T., Frank, R., Engel, T.: Driver behavior profiling using smartphones: a low-cost platform for driver monitoring. IEEE Intell. Transp. Syst. Mag. 7(1), 91–102 (2015)CrossRefGoogle Scholar
  5. 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
  6. 6.
    Daza, I.G., Bergasa, L.M., Bronte, S., Yebes, J.J., Almazán, J., Arroyo, R.: Fusion of optimized indicators from advanced driver assistance systems (ADAS) for driver drowsiness detection. Sensors 14(1), 1106–1131 (2014)CrossRefGoogle Scholar
  7. 7.
    Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011)CrossRefGoogle Scholar
  8. 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. 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
  10. 10.
    Fu, R., Wang, H., Zhao, W.: Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Syst. Appl. 63, 397–411 (2016)CrossRefGoogle Scholar
  11. 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. 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
  13. 13.
    Gravina, R., Li, Q.: Emotion-relevant activity recognition based on smart cushion using multi-sensor fusion. Inform. Fusion 48, 1–10 (2019). https://doi.org/10.1016/j.inffus.2018.08.001 CrossRefGoogle Scholar
  14. 14.
    Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005)CrossRefGoogle Scholar
  15. 15.
    Jung, S.J., Shin, H.S., Chung, W.Y.: Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell. Transp. Syst. 8(1), 43–50 (2014)CrossRefGoogle Scholar
  16. 16.
    Li, G., Lee, B.L., Chung, W.Y.: Smartwatch-based wearable eeg system for driver drowsiness detection. IEEE Sensors J. 15(12), 7169–7180 (2015)CrossRefGoogle Scholar
  17. 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. 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
  19. 19.
    Masala, G., Grosso, E.: Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses. Transp. Res. C: Emerg. Technol. 49, 32–42 (2014)CrossRefGoogle Scholar
  20. 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. 21.
    Rezaei, M., Klette, R.: Driver drowsiness detection. In: Computer Vision for Driver Assistance, pp. 95–126. Springer, Berlin (2017)Google Scholar
  22. 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
  23. 23.
    Wang, X., Xu, C.: Driver drowsiness detection based on non-intrusive metrics considering individual specifics. Accid. Anal. Prev. 95, 350–357 (2016)CrossRefGoogle Scholar
  24. 24.
    Wang, H., Zhang, C., Shi, T., Wang, F., Ma, S.: Real-time eeg-based detection of fatigue driving danger for accident prediction. Int. J. Neural Syst. 25(2), 1550002 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

Personalised recommendations