SDN-based Time Series Traffic Flow Forecasting in VANET

Shuvro, Ali Abir; Khan, Mohammad Shian; Rahman, Monzur

dc.contributor.author	Shuvro, Ali Abir
dc.contributor.author	Khan, Mohammad Shian
dc.contributor.author	Rahman, Monzur
dc.date.accessioned	2023-04-28T05:23:36Z
dc.date.available	2023-04-28T05:23:36Z
dc.date.issued	2022-05-30
dc.identifier.citation	[1] D. Kreutz, F. M. Ramos, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, “Software-defined networking: A comprehensive survey,” Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76, 2014. [2] M. I. Lali, R. Mustafa, F. Ahsan, M. Nawaz, and W. Aslam, “Performance evaluation of software defined networking vs. traditional networks,” The Nucleus, vol. 54, no. 1, pp. 16–22, 2017. [3] J. Mackenzie, J. F. Roddick, and R. Zito, “An evaluation of htm and lstm for short-term arterial traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 5, pp. 1847–1857, 2018. [4] X. Yu, S. Xiong, Y. He, W. E. Wong, and Y. Zhao, “Research on campus traffic congestion detection using bp neural network and markov model,” Journal of information security and applications, vol. 31, pp. 54–60, 2016. [5] I. Ku, Y. Lu, M. Gerla, R. L. Gomes, F. Ongaro, and E. Cerqueira, “Towards softwaredefined vanet: Architecture and services,” in 2014 13th annual Mediterranean ad hoc networking workshop (MED-HOC-NET), pp. 103–110, IEEE, 2014. [6] H. Kim and N. Feamster, “Improving network management with software defined networking,” IEEE Communications Magazine, vol. 51, no. 2, pp. 114–119, 2013. [7] C. J. Bernardos, A. De La Oliva, P. Serrano, A. Banchs, L. M. Contreras, H. Jin, and J. C. Zúñiga, “An architecture for software defined wireless networking,” IEEE wireless communications, vol. 21, no. 3, pp. 52–61, 2014. [8] L. Cai, K. Janowicz, G. Mai, B. Yan, and R. Zhu, “Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting,” Transactions in GIS, vol. 24, no. 3, pp. 736–755, 2020. [9] M. Xu, W. Dai, C. Liu, X. Gao, W. Lin, G.-J. Qi, and H. Xiong, “Spatial-temporal transformer networks for traffic flow forecasting,” arXiv preprint arXiv:2001.02908, 2020. [10] M. Gerla, “Vehicular cloud computing,” in 2012 The 11th annual mediterranean ad hoc networking workshop (Med-Hoc-Net), pp. 152–155, IEEE, 2012. [11] I. A. Abbasi and A. Shahid Khan, “A review of vehicle to vehicle communication protocols for vanets in the urban environment,” future internet, vol. 10, no. 2, p. 14, 2018. 45 [12] G. Karagiannis, O. Altintas, E. Ekici, G. Heijenk, B. Jarupan, K. Lin, and T. Weil, “Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions,” IEEE communications surveys & tutorials, vol. 13, no. 4, pp. 584–616, 2011. [13] S. Khatri, H. Vachhani, S. Shah, J. Bhatia, M. Chaturvedi, S. Tanwar, and N. Kumar, “Machine learning models and techniques for vanet based traffic management: Implementation issues and challenges,” Peer-to-Peer Networking and Applications, vol. 14, no. 3, pp. 1778–1805, 2021. [14] C. Harsch, A. Festag, and P. Papadimitratos, “Secure position-based routing for vanets,” in 2007 IEEE 66th Vehicular Technology Conference, pp. 26–30, IEEE, 2007. [15] S. Kudva, S. Badsha, S. Sengupta, I. Khalil, and A. Zomaya, “Towards secure and practical consensus for blockchain based vanet,” Information Sciences, vol. 545, pp. 170–187, 2021. [16] F. Alam, I. Katib, and A. S. Alzahrani, “New networking era: Software defined networking,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 11, 2013. [17] O. N. Fundation, “Software-defined networking: The new norm for networks,” ONF White Paper, vol. 2, no. 2-6, p. 11, 2012. [18] K. Kirkpatrick, “Software-defined networking,” Communications of the ACM, vol. 56, no. 9, pp. 16–19, 2013. [19] H. I. Kobo, A. M. Abu-Mahfouz, and G. P. Hancke, “A survey on software-defined wireless sensor networks: Challenges and design requirements,” IEEE access, vol. 5, pp. 1872–1899, 2017. [20] Z. Qin, G. Denker, C. Giannelli, P. Bellavista, and N. Venkatasubramanian, “A software defined networking architecture for the internet-of-things,” in 2014 IEEE network operations and management symposium (NOMS), pp. 1–9, IEEE, 2014. [21] A. Rahman, M. K. Nasir, Z. Rahman, A. Mosavi, S. Shahab, and B. Minaei-Bidgoli, “Distblockbuilding: A distributed blockchain-based sdn-iot network for smart building management,” IEEE Access, vol. 8, pp. 140008–140018, 2020. [22] S. Sezer, S. Scott-Hayward, P. K. Chouhan, B. Fraser, D. Lake, J. Finnegan, N. Viljoen, M. Miller, and N. Rao, “Are we ready for sdn? implementation challenges for softwaredefined networks,” IEEE Communications Magazine, vol. 51, no. 7, pp. 36–43, 2013. 46 [23] J. Xie, F. R. Yu, T. Huang, R. Xie, J. Liu, C. Wang, and Y. Liu, “A survey of machine learning techniques applied to software defined networking (sdn): Research issues and challenges,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 393–430, 2018. [24] M. J. Islam, M. Mahin, S. Roy, B. C. Debnath, and A. Khatun, “Distblacknet: A distributed secure black sdn-iot architecture with nfv implementation for smart cities,” in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6, IEEE, 2019. [25] G. A. Qadir, S. Askar, et al., “Software defined network based vanet,” International Journal of Science and Business, vol. 5, no. 3, pp. 83–91, 2021. [26] M. O. Kalinin, V. Krundyshev, and P. Semianov, “Architectures for building secure vehicular networks based on sdn technology,” Automatic Control and Computer Sciences, vol. 51, no. 8, pp. 907–914, 2017. [27] M. Zhu, J. Cao, D. Pang, Z. He, and M. Xu, “Sdn-based routing for efficient message propagation in vanet,” in International Conference on Wireless Algorithms, Systems, and Applications, pp. 788–797, Springer, 2015. [28] J. Bhatia, R. Dave, H. Bhayani, S. Tanwar, and A. Nayyar, “Sdn-based real-time urban traffic analysis in vanet environment,” Computer Communications, vol. 149, pp. 162–175, 2020. [29] S. Rahimipour, R. Moeinfar, and S. M. Hashemi, “Traffic prediction using a self-adjusted evolutionary neural network,” Journal of Modern Transportation, vol. 27, no. 4, pp. 306–316, 2019. [30] L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li, “T-gcn: A temporal graph convolutional network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3848–3858, 2019. [31] X. Du, H. Zhang, H. Van Nguyen, and Z. Han, “Stacked lstm deep learning model for traffic prediction in vehicle-to-vehicle communication,” in 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pp. 1–5, IEEE, 2017. [32] Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, “Traffic flow prediction with big data: a deep learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, 2014. 47 [33] J. Hu, P. Gao, Y. Yao, and X. Xie, “Traffic flow forecasting with particle swarm optimization and support vector regression,” in 17th international ieee conference on intelligent transportation systems (itsc), pp. 2267–2268, IEEE, 2014. [34] S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929, 2019. [35] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems, pp. 5998–6008, 2017. [36] C. Chen, Freeway performance measurement system (PeMS). University of California, Berkeley, 2002.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1861
dc.description	Supervised by Mr. Md. Sakhawat Hossen, Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	Intelligent Transportation Systems(ITS) provides services for proper traffic assistance. Vehicular Ad-hoc Network(VANET) provides internet connectivity to vehicles and helps in traffic guidance. In this paper, traffic flow prediction is done using a modified transformer architecture for time-series vehicular data. Sequences are generated from the dataset for capturing temporal dependencies. The transformer model has been engineered to capture inter-feature correlations along with inter-sample correlations. Our transformer model has performed much better than other models like LSTM. We also propose a holistic networking model where the vehicles will be connected to Road-side Units(RSUs) and the backbone network will be Software Defined Network(SDN). The traditional design principles, that incorporates data, control and management planes together in a network device, are incapable to adapt with this much data growth, bandwidth, speed, security, scalability compared to SDN as it provides with centralized programmable mechanism reliably. The trained parameters learned using the transformer model will be passed throughout the network for traffic guidance. Similar sized packets are passed using a simulator to demonstrate the time required for the propagation of the parameters.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Vehicular Ad-hoc Network, Transformer, Sequence length, Encoders, Attention mechanism, Traffic flow, Software-defined Network	en_US
dc.title	SDN-based Time Series Traffic Flow Forecasting in VANET	en_US
dc.type	Thesis	en_US