Optimal Allocation and Deployment of Roadside Units in Cloud-Based Internet of Vehicles Framework
Article Sidebar
Main Article Content
Abstract
The research focuses on internet of vehicles (IoV) where vehicles are equipped with cameras and sensors to monitor traffic jams, accidents, and locations to ensure safety and comfort for drivers. To process sensor data effectively, cloud computing is used because of vast storage and processing capabilities. However, transferring data from sensors to cloud can be challenging due to bandwidth and memory constraints. Therefore, a cloud-based internet of vehicles framework is proposed incorporating Roadside Units (RSUs). RSUs can buffer video streams from vehicles and send them to cloud services. With RSUs in the framework, total latency for transferring video streams to cloud services can be significantly enhanced. In this research, dynamic programming approaches are applied to determine how many RSUs are needed at the lowest cost and greedy algorithm is implemented to prove the optimal solution from dynamic programming. Furthermore, K-means clustering algorithm is applied to find the best locations for RSUs. According to numerical results, the proposed methods can determine the optimal number of 6 RSUs with the minimum cost and allocation of RSUs to serve video streams across regions.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Article Accepting Policy
The editorial board of Thai-Nichi Institute of Technology is pleased to receive articles from lecturers and experts in the fields of engineering and technology written in Thai or English. The academic work submitted for publication must not be published in any other publication before and must not be under consideration of other journal submissions. Therefore, those interested in participating in the dissemination of work and knowledge can submit their article to the editorial board for further submission to the screening committee to consider publishing in the journal. The articles that can be published include solely research articles. Interested persons can prepare their articles by reviewing recommendations for article authors.
Copyright infringement is solely the responsibility of the author(s) of the article. Articles that have been published must be screened and reviewed for quality from qualified experts approved by the editorial board.
The text that appears within each article published in this research journal is a personal opinion of each author, nothing related to Thai-Nichi Institute of Technology, and other faculty members in the institution in any way. Responsibilities and accuracy for the content of each article are owned by each author. If there is any mistake, each author will be responsible for his/her own article(s).
The editorial board reserves the right not to bring any content, views or comments of articles in the Journal of Thai-Nichi Institute of Technology to publish before receiving permission from the authorized author(s) in writing. The published work is the copyright of the Journal of Thai-Nichi Institute of Technology.
References
United States International Trade Commission, “Passenger vehicles: Summary of trends summary,” 2013. [Online]. Available: https://www.usitc.gov/publications/332/pub_ITS_09_PassengerVehiclesSummary5211.pdf
Global Growth Insights. “Automobile Market.” GLOBALGROWTH
INSIGHTS.com. https://www.globalgrowthinsights.com/market-reports/automobile-market-109995 (accessed Jun. 4, 2025).
R. Dhanare, K. K. Nagwanshi, S. Varma, and S. Pathak, “The future of internet of vehicle : challenges and applications,” in Proc. Int. Conf. Comput. Perform. Eval. (ComPE), Shillong, India, Dec. 2021, pp. 023–026.
F. A. Butt, J. N. Chattha, J. Ahmad, M. U. Zia, M. Rizwan, and I. H. Naqvi, “On the integration of enabling wireless technologies and sensor fusion for next-generation connected and autonomous vehicles,” IEEE Access, vol. 10, pp. 14643–14668, 2022.
M. B. Mollah et al., “Blockchain for the Internet of Vehicles towards intelligent transportation systems: A survey,” IEEE Internet Things J., vol. 8, no. 6, pp. 4157–4185, 2021.
R. Cherekar, “Cloud-based big data analytics: Frameworks, challenges, and future trends,” Int. J. AI, Bigdata, Comput. Manage. Stud., vol. 4, no. 1, pp. 35–46, 2023.
J. Beuchert, F. Solowjow, S. Trimpe, and T. Seel, “Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: An event-triggered learning approach,” Sensors, vol. 20, no. 1, 2020, Art. no. 260.
S. Akhavan Bitaghsir and A. Khonsari, “Modeling and improving the throughput of vehicular networks using cache enabled RSUs,” Telecommun. Syst., vol. 70, pp. 391–404, 2019.
Valuates Reports, “Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030,” 2024. [Online]. Available: https://reports.valuates.com/market-reports/QYRE-Auto-7R15191/global-roadside-unit-rsu
D. Liu, S. Xue, B. Zhao, B. Luo, and Q. Wei, “Adaptive dynamic programming for control: A survey and recent advances,” IEEE Trans. Syst., Man, Cybern.: Syst., vol. 51, no. 1, pp. 142–160, 2021.
D. Jungnickel, “The greedy algorithm,” in Graphs, Networks and Algorithms, 2nd ed. Berlin, Germany: Springer, 1999, ch. 5, pp. 129–153.
R. V. Singh and M. S. Bhatia, “Data clustering with modified K-means algorithm,” in Proc. 2011 Int. Conf. Recent Trends Inf. Technol. (ICRTIT), Chennai, India, Jun. 2011, pp. 717–721.
N. S. Rajput et al., “Amalgamating vehicular networks with vehicular clouds, AI, and big data for next-generation ITS services,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 1, pp. 869–883, 2024.
Y. Sasaki et al., “An edge-cloud computing model for autonomous vehicles,” in Proc. 11th Int. Workshop Planning, Perception, Navigation Intell. Vehicle, Macau, China, Nov. 2019, pp. 99–104.
C. Liu and L. Ke, “Cloud assisted Internet of Things intelligent transportation system and the traffic control system in the smart city,” J. Control Decis., vol. 10, no. 2, pp. 174–187, 2023.
S. Tonayali, K. Akkaya, N. Saputro, A. S. Uluagac, and M. Nojoumian, “Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled smart metering systems,” Future Gener. Comput. Syst., vol 78, pp. 547–557, 2018.
X. Ma et al., “QAVA: QoE-aware adaptive video bitrate aggregation for HTTP live streaming based on smart edge computing,” IEEE Trans. Broadcast., vol. 68, no. 3, pp. 661–676, 2022.
P. K. N. Kouonchie, V. Oduol, and G. N. Nyakoe, “Roadside units for vehicle-to-infrastructure communication: An overview,” in Proc. Sustain. Res. and Innov. Conf., Nairobi, Kenya, Oct. 2021, pp. 69–72.
P. Pyykonen, J. Laitinen, J. Viitanen, P. Eloranta, and T. Korhonen, “IoT for intelligent traffic system,” in IEEE 9th Int. Conf. Intell. Comput. Commun. and Process. (ICCP), Cluj-Napoca, Romania, Sep. 2013, pp. 175–179.
