Internet of Vehicles Based Approach for Road Safety Applications Using Sensor Technologies

  • Nikheel Soni
  • Reza MalekianEmail author
  • Darius Andriukaitis
  • Dangirutis Navikas


In this paper, the Internet of Vehicles approach is used to develop a novel low cost sensor based system for road safety applications in intelligent transportation systems. It was found that major hazards that compromise road safety include weather related factors, poor road surfaces and presence of sharp turns. A wireless sensor network based solution consisting of embedded systems for the vehicular clients and infrastructure waypoints is developed for detecting road safety hazards and warning users about potentially hazardous events from causes that include presence of speed bumps, sharp turns and weather related factors of rain and fog. Hazards detected by the embedded systems are conveyed to the user by using the Vehicle-to-Vehicle communication and Vehicle-to-infrastructure communication interfaces developed in this study for inter-vehicle communication and obtaining sensory information from infrastructure waypoints respectively. Accuracy achieved was 88% for speed bump detection, 73.86% for detecting sharp turns and 100% for detection of rain and fog. Communication systems in the designed solution are optimized by reducing the size of packet being exchanged which improves transmission speed, packet losses and congestion on the network. Thus, the designed solution is capable of improving road safety by using an Internet of Vehicles approach.


Internet of vehicles Vehicle-to-vehicle communications Vehicle-to-infrastructure communications Road safety Intelligent transportation systems 



The subject is supported by and National Research Foundation, South Africa (grant numbers: IFR160118156967 and RDYR160404161474), and partially by the Transport Electronics. Centre at Kaunas University of Technology. The authors would like to thank Mr. Arnav Thakur for his help during revision of this paper and we appreciate his contribution in revising the paper.


  1. 1.
    “Road safety - arrive alive South African Government”,, 2017 [Online]. Available Accessed 21 March 2017.
  2. 2.
    Law Offices of Michael Pines, APC. (2017). Top 25 causes of car accidents [Online]. Available at Accessed 21 March 2017.
  3. 3.
    Hannan, M., Habib, S., Javadi, M., Samad, S., Muad, A., & Hussain, A. (2013). Inter-vehicle wireless communications technologies, issues and challenges. Information Technology Journal, 12(4), 558–568.CrossRefGoogle Scholar
  4. 4.
    Chauhan, A., & Sharma, N. (2015). Vehicle-to-vehicle communication: traffic safety over RF communication. International Journal of Scientific Research and Management (IJSRM), 3(5), 2769–2772.Google Scholar
  5. 5.
    Taehyoung, K. (2015). Assessment of vehicle-to-vehicle communication based applications in an urban network doctor of philosophy in civil engineering. Blacksburg: Virginia Polytechnic Institute and State University.Google Scholar
  6. 6.
    He, C. R., & Orosz, G. (2017). Saving fuel using wireless vehicle-to-vehicle communication. In 2017 American control conference (ACC), Seattle, WA (pp. 4946–4951).Google Scholar
  7. 7.
    Howard, B. (2017). “V2 V: What are vehicle-to-vehicle communications and how do they work?—ExtremeTech”, ExtremeTech, 2017 [Online]. Available Accessed 22 July 2017.
  8. 8.
    Giarratana, C., & Giarratana, V. (2016). “How will vehicle-to-vehicle communication drive the future of car technology? Traffic Safety Resource Center”, Traffic Safety Resource Center, 2016 [Online]. Available Accessed 22 July 2017.
  9. 9.
    Malekian, R., Moloisane, N. R., Nair, L., Maharaj, B. T., & Chude-Okonkwo, U. A. K. (2017). Design and implementation of a wireless OBD II fleet management system. IEEE Sensors Journal, 17(4), 1154–1164.CrossRefGoogle Scholar
  10. 10.
    Kim, Y. H., Cahyadi, W. A., & Chung, Y. H. (2015). Experimental demonstration of VLC-based vehicle-to-vehicle communications under fog conditions. IEEE Photonics Journal, 7(6), 1–9.CrossRefGoogle Scholar
  11. 11.
    “nRF24L01/2.4 GHz RF/Products/Home-ultra low power wireless solutions from NORDIC SEMICONDUCTOR”,, 2017 [Online]. Available Accessed 02 November 2017.
  12. 12.
    Muhammad Ikram, A., Liu, C., Bennis, M., & Saad, W. (2017). Towards low-latency and ultra-reliable vehicle-to-vehicle communication. In 2017 European conference on IEEE, networks and communications (EuCNC) (pp. 1–5).Google Scholar
  13. 13.
    Stallings, W. (2011). Network security essentials (4th ed., pp. 44–56). Boston: Prentice Hall.Google Scholar
  14. 14.
    Cooney, M. (2017). “Six key challenges loom over car communication technology”. Network World. [Online]. Available Accessed 22 July 2017.
  15. 15.
    Devapriya, W., Babu, C. N. K., & Srihari, T. (2015). Advance driver assistance system (ADAS)-speed bump detection. In 2015 IEEE international conference on computational intelligence and computing research (ICCIC), Madurai (pp. 1–6).Google Scholar
  16. 16.
    Jain, M., & Kaul, S. (2012). Speed-breaker early warning system. In 2012 USENIX federated conferences week, New Delhi.Google Scholar
  17. 17.
    Singh, M. P. S., Shukla, S., & Krishnan, U. (2017). Detection of humps and potholes on roads and notifying the same to the drivers. International Journal of Management and Applied Science, 3(1), 130–133.Google Scholar
  18. 18.
    Bickmore, M. (2017). Night vision - 3 things you need to know when choosing a camera| burglar free zone, Burglar Free Zone [Online]. Available Accessed 29 October 2017.
  19. 19.
    Abaya, W., Basa, J., Sy, M., Abad, A., & Dadios, E. (2014). Low cost smart security camera with night vision capability using Raspberry Pi and OpenCV. In 2014 International conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM).Google Scholar
  20. 20.
    Chen, D., Cho, K., Han, S., Jin, Z., & Shin, K., (2017). Invisible sensing of vehicle steering with smartphones. In The proceedings of 13th annual conference on mobile systems, applications, and services (MobiSys'15), New York (pp. 1–13).
  21. 21.
    Met Office, “What is fog?”, Met Office. (2017). [Online]. Available Accessed 29 October 2017.
  22. 22.
    Wang, Z., Ye, N., Wang, R., & Li, P. (2016). TMicroscope: Behavior perception based on the slightest RFID tag motion. Elektronika ir Elektrotechnika, 22(2), 114–122.CrossRefGoogle Scholar
  23. 23.
    Malekian, R., Kavishe, A., Maharaj, B. T., Gupta, P., Singh, G., & Waschefort, H. (2016). Smart vehicle navigation system using hidden Markov model and RFID technology. Wireless Personal Communications, 90(4), 1717–1742.CrossRefGoogle Scholar
  24. 24.
    “Total internal reflection”, (2017). [Online]. Available: Accessed 29 Oct 2017.
  25. 25.
    Ye, N., Wang, Z., Malekian, R., Zhang, Y., Wang, R. (2015). A method of vehicle route prediction based on social network analysis. Journal of Sensors, 15, 1–10.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ElectricalElectronic and Computer Engineering, University of PretoriaPretoriaSouth Africa
  2. 2.Department of Computer Science and Media Technology, Internet of Things and People Research CenterMalmö UniversityMalmöSweden
  3. 3.Department of Electronics EngineeringKaunas University of TechnologyKaunasLithuania

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