banner
Home > Open Journal Systems

Transforming transportation: Embracing the potential of 5G, heterogeneous networks, and software defined networking in intelligent transportation systems

Surabhi Saxena, Radha Raman Chandan, Ramkumar Krishnamoorthy, Upendra Kumar, Prabhdeep Singh, Ashish Kumar Pandey, Shashi Kant Gupta

Abstract


Intelligent transportation systems (ITS) emphasise the significance of vehicle networks. The growing need for services that are safer, more effective, more affordable, infotainment-focused, and sustainable, however, presents difficulties for these networks. To create innovative applications, researchers and businesses are working. Through effectively coordinating vehicle operations, ITS promotes safe driving, efficient traffic flow, and effective route planning. Referring to automobile heterogeneous, autonomous, flexible, and programmable networks is important given the requirement for convergence of technology in communications. For research and network development, new emerging technologies present intriguing gaps. In this paper, we provide an analysis of wireless technology and potential obstacles to delivering vehicle-to-x communication; including linked cars or autonomous vehicles, which that the initial robot to directly impact the everyday Millions of lived individuals. Study investigates the conceptual change in transportation made possible by the incorporation of modern technology into Intelligent Transportation Systems (ITS), including 5G, heterogeneous networks (HN), and Software Defined Networking (SDN). The incorporation of 5G ensures unparalleled velocity and minimal latency, allowing instantaneous communication between automobiles and infrastructure. Vehicles are easily switched between several network technologies due to heterogeneous networks’ seamless communication structure. Technology developments generated an important increase in the worldwide ITS market from 2018 to 2025. During the same time, the global market for SDN increased significantly, indicating a rising to need for programmable and dynamic network infrastructures. The simultaneous growth patterns in the SDN and ITS industries between 2018 and 2025 indicate to a general shift in the sector toward more intelligence and connectivity. It is predicted that this development continues for future. We pay particular attention to the SDN used in the 5G architecture and how it affects HN.


Keywords


intelligent transportation systems (ITS); software defined networking (SDN); 5G architecture; heterogeneous networks (HN)

Full Text:

PDF

References


1. Han T, Li S, Zhong Y, et al. 5G Software-Defined Heterogeneous Networks with Cooperation and Partial Connectivity. IEEE Access. 2019, 7: 72577-72590. doi: 10.1109/access.2019.2920363

2. Qiu J, Grace D, Ding G, et al. Air-Ground Heterogeneous Networks for 5G and Beyond via Integrating High and Low Altitude Platforms. IEEE Wireless Communications. 2019, 26(6): 140-148. doi: 10.1109/mwc.0001.1800575

3. Alshaer H, Haas H. Software-Defined Networking-Enabled Heterogeneous Wireless Networks and Applications Convergence. IEEE Access. 2020, 8: 66672-66692. doi: 10.1109/access.2020.2986132

4. Ravi B, Thangaraj J, Shandilya SK. Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles. Physical Communication. 2022, 50: 101498. doi: 10.1016/j.phycom.2021.101498

5. Khasawneh AM, Helou MA, Khatri A, et al. Service-Centric Heterogeneous Vehicular Network Modeling for Connected Traffic Environments. Sensors. 2022, 22(3): 1247. doi: 10.3390/s22031247

6. Nkenyereye L, Nkenyereye L, Islam SMR, et al. Software-Defined Network-Based Vehicular Networks: A Position Paper on Their Modeling and Implementation. Sensors. 2019, 19(17): 3788. doi: 10.3390/s19173788

7. Gohar A, Nencioni G. The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System. Sustainability. 2021, 13(9): 5188. doi: 10.3390/su13095188

8. Rahouti M, Xiong K, Xin Y. Secure Software-Defined Networking Communication Systems for Smart Cities: Current Status, Challenges, and Trends. IEEE Access. 2021, 9: 12083-12113. doi: 10.1109/access.2020.3047996

9. Wu Y, Dai HN, Wang H, et al. A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory. IEEE Communications Surveys & Tutorials. 2022, 24(2): 1175-1211. doi: 10.1109/comst.2022.3158270

10. Abayagunawardhana SS, Halgamuge MN, Jayasekara CS. Estimation of Computation Time for Software—Defined Networking—Based Data Traffic Offloading System in Heterogeneous Network. Wireless Communication Security. Published online December 7, 2022: 223-251. doi: 10.1002/9781119777465.ch12

11. Iqbal W, Abbas H, Daneshmand M, et al. An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software-Defined Security. IEEE Internet of Things Journal. 2020, 7(10): 10250-10276. doi: 10.1109/jiot.2020.2997651

12. Wang X, Garg S, Lin H, et al. Heterogeneous Blockchain and AI-Driven Hierarchical Trust Evaluation for 5G-Enabled Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 2021: 1-10. doi: 10.1109/tits.2021.3129417

13. Xia D, Wan J, Xu P, et al. Deep Reinforcement Learning-Based QoS Optimization for Software-Defined Factory Heterogeneous Networks. IEEE Transactions on Network and Service Management. 2022, 19(4): 4058-4068. doi: 10.1109/tnsm.2022.3208342

14. Mahmood A, Zhang WE, Sheng QZ, et al. Trust Management for Software-Defined Heterogeneous Vehicular Ad Hoc Networks. Security, Privacy and Trust in the IoT Environment. 2019: 203-226. doi: 10.1007/978-3-030-18075-1_10

15. Kumhar M, Bhatia J. Emerging Communication Technologies for 5G-Enabled Internet of Things Applications. Blockchain for 5G-Enabled IoT. 2020: 133-158. doi: 10.1007/978-3-030-67490-8_6

16. Mahmood A. Towards Software Defined Heterogeneous Vehicular Networks for Intelligent Transportation Systems. 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 2019. doi: 10.1109/percomw.2019.8730827

17. Din S, Paul A, Ahmad A, et al. Hierarchical architecture for 5G based software-defined intelligent transportation system. IEEE INFOCOM 2018—IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 2018. doi: 10.1109/infcomw.2018.8406895

18. Yu M. Construction of Regional Intelligent Transportation System in Smart City Road Network via 5G Network. IEEE Transactions on Intelligent Transportation Systems. 2022: 1-9. doi: 10.1109/tits.2022.3141731

19. Su Z, Dai M, Xu Q, et al. UAV Enabled Content Distribution for Internet of Connected Vehicles in 5G Heterogeneous Networks. IEEE Transactions on Intelligent Transportation Systems. 2021, 22(8): 5091-5102. doi: 10.1109/tits.2020.3043351

20. Raza N, Jabbar S, Han J, et al. Social vehicle-to-everything (V2X) communication model for intelligent transportation systems based on 5G scenario. Proceedings of the 2nd International Conference on Future Networks and Distributed Systems. 2018. doi: 10.1145/3231053.3231120

21. Marappan R, Vardhini PAH, Kaur G, et al. Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks. Soft Computing. 2023, 27(16): 11853-11867. doi: 10.1007/s00500-023-08623-w

22. Muruganandam N, R Venkatesan*, Raja Marappan & V Venkataraman, Optimization and Analysis of Wireless Networks Lifetime using Soft Computing for Industrial Applications. Journal of Scientific & Industrial Research. 2023, 82(01). doi: 10.56042/jsir.v82i1.70211

23. Lyu K, Li J. Gradient descent maximizes the margin of homogeneous neural networks. arXiv 2019. arXiv:1906.05890.

24. Cohen TS, Geiger M, Weiler M. A general theory of equivariant cnns on homogeneous spaces. Advances in neural information processing systems, 2019. 32.

25. Tulin M, Volker B, Lancee B. The same place but different: How neighborhood context differentially affects homogeneity in networks of different social groups. Journal of Urban Affairs. 2019, 43(1): 57-76. doi: 10.1080/07352166.2019.1578176

26. Bharathiraja N, Shobana M, Anand MV, et al. A secure and effective diffused framework for intelligent routing in transportation systems. International Journal of Computer Applications in Technology. 2023, 71(4): 363-370. doi: 10.1504/ijcat.2023.132405

27. Balakrishnan S, Suresh T, Marappan R, et al. New hybrid decentralized evolutionary approach for DIMACS challenge graph coloring & wireless network instances. International Journal of Cognitive Computing in Engineering. 2023, 4: 259-265. doi: 10.1016/j.ijcce.2023.07.002

28. Marappan R, Bhaskaran S. New evolutionary operators in coloring DIMACS challenge benchmark graphs. International Journal of Information Technology. 2022, 14(6): 3039-3046. doi: 10.1007/s41870-022-01057-x




DOI: https://doi.org/10.32629/jai.v7i4.1219

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Surabhi Saxena, Radha Raman Chandan, Ramkumar Krishnamoorthy, Upendra Kumar, Prabhdeep Singh, Ashish Kumar Pandey, Shashi Kant Gupta

License URL: https://creativecommons.org/licenses/by-nc/4.0/