Advancing Road Safety through Cloud Based RSU Solutions for Smart Internet of Vehicles
PDF

How to Cite

K., Periyarselvam, Akash G., Harish V., and Thunaivan V. 2024. “Advancing Road Safety through Cloud Based RSU Solutions for Smart Internet of Vehicles”. Journal of ISMAC 6 (2): 176-87. https://doi.org/10.36548/jismac.2024.2.008.

Keywords

— Road safety
— damage location
— Internet of Vehicles
— Roadside Units
— road condition
Published: 22-06-2024

Abstract

This research proposes a system to improve road safety by detecting and alerting drivers about hazardous road conditions in real-time. Vehicles equipped with sensors, GPS, and communication devices can autonomously detect hazardous road conditions and transmit alerts to a central cloud system through IoT MQTT protocol. Upon receiving alerts, vehicles can dynamically adjust their routes to avoid hazardous areas, reducing the risk of accidents. By utilizing IoT technology, the system ensures the reliability and authenticity of shared information, enhancing trust and overall road safety awareness. The proposed system architecture leverages cloud-based Roadside Units (RSUs) to facilitate communication among smart vehicles, providing real-time road condition information and ensuring secure and efficient data exchange. The system aims to address the challenges of existing IoV frameworks, such as data protection, key management, storage, concurrency performance, and response speed, by implementing robust security measures and efficient data management strategies.

References

  1. Kuliczkowska, Emilia. "An analysis of road pavement collapses and traffic safety hazards resulting from leaky sewers." The Baltic Journal of Road and Bridge Engineering 11, no. 4 (2016): 251-258.
  2. Qin, Haoran, Yining Tan, Yuxian Chen, Wei Ren, and Kim-Kwang Raymond Choo. "Tribodes: A tri-blockchain-based detection and sharing scheme for dangerous road condition information in internet of vehicles." IEEE Internet of Things Journal (2023).
  3. Othmane, Lotfi Ben, Harold Weffers, Mohd Murtadha Mohamad, and Marko Wolf. "A survey of security and privacy in connected vehicles." Wireless sensor and mobile ad-hoc networks: vehicular and space applications (2015): 217-247.
  4. Singh, Rajinder, Parvinder Singh, and Manoj Duhan. "An effective implementation of security based algorithmic approach in mobile adhoc networks." Human-centric Computing and Information Sciences 4 (2014): 1-14.
  5. Yu, Lu, Juan Deng, Richard R. Brooks, and Seok Bae Yun. "Automobile ECU design to avoid data tampering." In Proceedings of the 10th annual cyber and information security research conference, pp. 1-4. 2015.
  6. Li, Mushu, Jie Gao, Lian Zhao, and Xuemin Shen. "Adaptive computing scheduling for edge-assisted autonomous driving." IEEE Transactions on Vehicular Technology 70, no. 6 (2021): 5318-5331.
  7. Cao, Mingwei, Liping Zheng, Wei Jia, and Xiaoping Liu. "Joint 3D reconstruction and object tracking for traffic video analysis under IoV environment." IEEE Transactions on Intelligent Transportation Systems 22, no. 6 (2020): 3577-3591.
  8. Dai, Cheng, Xingang Liu, Weiting Chen, and Chin-Feng Lai. "A low-latency object detection algorithm for the edge devices of IoV systems." IEEE Transactions on Vehicular Technology 69, no. 10 (2020): 11169-11178.
  9. Xie, Qiwei, Xiyuan Hu, Lei Ren, Lianyong Qi, and Zhao Sun. "A binocular vision application in IoT: Realtime trustworthy road condition detection system in passable area." IEEE Transactions on Industrial Informatics 19, no. 1 (2022): 973-983.
  10. Thakur, Arnav, Reza Malekian, and Dijana Capeska Bogatinoska. "Internet of things based solutions for road safety and traffic management in intelligent transportation systems." In ICT Innovations 2017: Data-Driven Innovation. 9th International Conference, ICT Innovations 2017, Skopje, Macedonia, September 18-23, 2017, Proceedings 9, pp. 47-56. Springer International Publishing, 2017.