Machine Learning Approach for Intelligent Transport System in IOV-Based Vehicular Network Traffic for Smart Cities

Authors

  • Chandrakant D. Kokane Assistant Professor, Department of Computer Engineering, Nutan Maharashtra Institute of Engineering & Technology, Talegaon(D), Pune, India(MS)
  • Gopal Mohadikar Sr. Assistant Professor, Department of Mechanical Engineering, Tolani Maritime Institute, Induri, Pune, India(MS)
  • Sonu Khapekar Assistant Professor, Department of Computer Engineering, Nutan Maharashtra Institute of Engineering & Technology, Talegaon(D), Pune, India(MS)
  • Bharti Jadhao Assistant Professor, Department of Computer Engineering, Nutan Maharashtra Institute of Engineering & Technology, Talegaon(D), Pune, India(MS)
  • Tushar Waykole Assistant Professor, Department of Computer Engineering, Nutan Maharashtra Institute of Engineering & Technology, Talegaon(D), Pune, India(MS)
  • Vilas V. Deotare Principal, Nutan Maharashtra Institute of Engineering & Technology, Talegaon(D), Pune, India(MS)

Keywords:

Vehicular Network, Internet of Vehicles, Machine Learning, Intelligent transport system, Network traffic

Abstract

The transportation industry will face many significant issues, including traffic congestion, pollution, and ineffective traffic management, as a result of the increasing urbanisation and demographic growth. It appears that one potential solution to these issues may be provided by intelligent transport systems (ITS) that harness the power of vehicular networks on the Internet of Vehicles (IoV). By integrating communication between vehicles and infrastructure (V2V) and vehicles and the cloud (V2C), the proposed ITS architecture seeks to create a comfortable and effective transportation ecosystem. The V2V connection helps with the transmission of data on route conditions, collision avoidance, and speed, position, and other factors. Vehicle-to-infrastructure (V2I) communication enables automobiles to connect with infrastructure components like traffic lights, road signs, and parking systems in order to optimise traffic signal timings and provide drivers with real-time information. Innovative applications like customised recommendations, dynamic navigation, and predictive maintenance are made possible via V2C communication, which makes it possible for cars to connect to the cloud. The recommended method makes use of tree-based machine learning models including Decision Tree (DT), XGBoost (XGB), and Random Forest (RF) to increase traffic detection accuracy and computational efficiency.

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References

M. Shafiq, Z. Tian, A.K. Bashir, A. Jolfaei, X. Yu,“Data mining and machine learning methods for sustainable smart cities traffic classification: a survey”, Sustain Cities Soc, 60 (2020), Article 102177

Z. Lv, B. Hu, H. Lv, “Infrastructure monitoring and operation for smart cities based on IoT system”, IEEE Trans Ind Inf, 16 (3) (2020), pp. 1957-1962, 10.1109/TII.2019.2913535

N. Hussain, P. Rani, H. Chouhan, U.. Gaur, “Cyber security and privacy of connected and automated vehicles (CAVs)-based federated learning: challenges, opportunities, and open issues”, Federated learning for IoT applications, Springer (2022), pp. 169-183

P. Rani, N. Hussain, R.A.H. Khan, Y. Sharma, P.K. Shukla, “Vehicular intelligence system: time-based vehicle next location predicion in software-defined internet of vehicles (SDN-IOV) for the smart cities”, Intelligence of things: AI-IoT based critical-applications and innovations, Springer International Publishing, Cham (2021), pp. 35-54.

O.A. Wahab, A. Mourad, H. Otrok, J. Bentahar, “CEAP: SVM-based intelligent detection model for clustered vehicular adhoc networks”, Expert Syst Appl, 50 (2016), pp. 40-54, 10.1016/j.eswa.2015.12.006

J. Yang, Z. Fei, “Broadcasting with prediction and selective forwarding in vehicular networks”, Int J Distrib Sens Netw, 9 (12) (2013), Article 309041.

H.M. Song, J. Woo, H.K. Kim, “In-vehicle network intrusion detection using deep convolutional neural network”, Veh. Commun., 21 (2020), Article 100198

M.V. Mahoney, “A machine learning approach to detecting attacks by identifying anomalies in network traffic”, Florida Institute of Technology (2003)

H. Ye, L. Liang, G. Ye Li, J. Kim, L. Lu, M. Wu, “Machine learning for vehicular networks: recent advances and application examples”, IEEE Veh Technol Mag, 13 (2) (2018), pp. 94-101.

J. Erman, A. Mahanti, M. Arlitt, “QRP05-4: internet traffic identification using machine learning”, Proceedings of the IEEE globecom 2006, San Francisco, CA, USA (2006), pp. 1-6, 10.1109/GLOCOM.2006.443

J. Liang, J. Chen, Y. Zhu, R. Yu, “A novel Intrusion Detection System for Vehicular Ad Hoc Networks (VANETs) based on diffrences of traffic flow and position”, Appl Soft Comput, 75 (2019), pp. 712-727, 10.1016/j.asoc.2018.12.001

P. Khobragade, P. Ghutke, V. P. Kalbande and N. Purohit, "Advancement in Internet of Things (IoT) Based Solar Collector for Thermal Storage System Devices: A Review", 2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), pp. 1-5, 2022.

Mohammed B, et al. Edge computing intelligence using robust feature selection for network traffic classification in intenet-of-things. IEEE Access 2020;8: 224059–70.

Gad AR, Nashat AA, Barkat TM. Intrusion detection system using machine learning for vehicular Ad hoc networks based on ToN-IoT dataset. IEEE Access 2021; 9:142206–17.

Yang L, Moubayed A, Hamieh I, Shami A. Tree-based intelligent intrusion detection system in internet of vehicles. In: Proceedings of the ieee global communications conference (GLOBECOM); Dec. 2019. p. 1–6.

Gao Y, Wu H, Song B, Jin Y, Luo X, Zeng X. A distributed network intrusion detection system for distributed denial of service attacks in vehicular Ad Hoc network. IEEE Access 2019;7:154560–71.

Peng R, Li W, Yang T, Huafeng K. An internet of vehicles intrusion detection system based on a convolutional neural network. In: Proceedings of the IEEE intl conf on parallel & distributed processing with applications, big data & cloud computing, sustainable computing & communications, social computing & networking (ISPA/BDCloud/SocialCom/SustainCom); 2019. p. 1595–9.

W. Wu et al., "A survey of intrusion detection for in-vehicle networks", IEEE Trans. Intell. Transp. Syst., vol. 21, no. 3, pp. 919-933, Mar. 2020.

J. Liu, S. Zhang, W. Sun and Y. Shi, "In-vehicle network attacks and countermeasures: Challenges and future directions", IEEE Netw., vol. 31, no. 5, pp. 50-58, Sep. 2017

D. Kosmanos et al., "A novel intrusion detection system against spoofing attacks in connected electric vehicles", Array, vol. 5, Mar. 2020.

Y. Sun et al., "Attacks and countermeasures in the Internet of vehicles", Ann. Telecommun., vol. 72, no. 5, pp. 283-295, 2017.

E. Seo, H. M. Song and H. K. Kim, "GIDS: GAN based intrusion detection system for in-vehicle network", Proc. 16th Annu. Conf. Privacy Secur. Trust (PST), pp. 1-6, Aug. 2018.

M.-J. Kang and J.-W. Kang, "Intrusion detection system using deep neural network for in-vehicle network security", PLoS ONE, vol. 11, no. 6, Jun. 2016

H. Lee, S. H. Jeong and H. K. Kim, "OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame", Proc. 15th Annu. Conf. Privacy Secure Trust (PST), pp. 57-5709, Aug. 2017.

T. Marsden, N. Moustafa, E. Sitnikova and G. Creech, "Probability risk identification based intrusion detection system for SCADA systems", Proc. Int. Conf. Mobile Netw. Manage., pp. 353-363, 2017.

Yoon, H. . (2023). A Quantitative Evaluation for Usability under Software Quality Models. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 24–29. https://doi.org/10.17762/ijritcc.v11i3.6194

Deshpande, V. (2021). Layered Intrusion Detection System Model for The Attack Detection with The Multi-Class Ensemble Classifier . Machine Learning Applications in Engineering Education and Management, 1(2), 01–06. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/10

Kumbhkar, M., Shukla, P., Singh, Y., Sangia, R. A., & Dhabliya, D. (2023). Dimensional reduction method based on big data techniques for large scale data. Paper presented at the 2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023, doi:10.1109/ICICACS57338.2023.10100261 Retrieved from www.scopus.com

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Published

06.09.2023

How to Cite

Kokane, C. D. ., Mohadikar, G. ., Khapekar, S. ., Jadhao, B. ., Waykole, T. ., & Deotare, V. V. . (2023). Machine Learning Approach for Intelligent Transport System in IOV-Based Vehicular Network Traffic for Smart Cities. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 06–16. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3430

Issue

Vol. 11 No. 11s (2023)

Section

Research Article

License

Copyright (c) 2023 Chandrakant D. Kokane, Gopal Mohadikar, Sonu Khapekar, Bharti Jadhao, Tushar Waykole, Vilas V. Deotare

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