Skip to main content
SpringerLink
Log in

Stochastic modelling of SDN controller for Internet of Vehicles

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Internet of Vehicles (IoV) cannot be furnished with advanced networking features using standard network parameters like Quality of Service (QoS) and bandwidth management. To meet the QoS and bandwidth requirements for the vehicles, SDN controllers are used for provisioning. In this paper, the main objectives are to run the switching logic on the edge and core switches at the control plane, to balance the network bandwidth load and provisioning the bandwidth requirements to the Internet of Vehicles. State transition modelling and selecting optimal policies at SDN controllers are implemented in this work to better handle the bandwidth and to limit connectivity failures. The best state transitions were projected as optimal learning policies at SDN controller. In this paper, a stochastic method is also considered to estimate bandwidth consumption for IoV. The SDN controllers were modelled using the Semi Markov Decision Process (SMDP) which was then solved using a reinforcement learning model which achieves the higher learning factor which efficiently handles the SDN controller's bandwidth through a variety of rules. The entire network is emulated using Mininet and RYU controllers. The states of the SDN controller is modelled in such a way that the bandwidth provisioning is handled better in our work. When compared to the controllers existing policies, our model improves the bandwidth usage by a factor of at least 29%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Abbas MT, Muhammad A, Song WC (2020) SD-IoV: SDN enabled routing for Internet of Vehicles in road-aware approach. J Ambient Intell Humaniz Comput 11:1265–1280. https://doi.org/10.1007/s12652-019-01319-w

    Article  Google Scholar 

  • Akin E, Korkmaz T (2019) Comparison of routing algorithms with static and dynamic link cost in software defined networking (SDN). IEEE Access 7:148629–148644. https://doi.org/10.1109/access.2019.2946707

    Article  Google Scholar 

  • Babbar H, Rani S (2021) Performance evaluation of qos metrics in software defined networking using ryu controller. In: IOP conference series: materials science and engineering, Vol 1022, No. 1. IOP Publishing, p. 012024

  • Asadollahi S, Bhargavi G, Mohammed S (2018) Ryu controller's scalability experiment on software defined networks. In: 2018 IEEE international conference on current trends in advanced computing (ICCTAC). IEEE, p 1–5

  • Chen LB, Su KY, Mo YC, Chang WJ, Hu WW, Tang JJ, Yu CT (2018) An implementation of deep learning based IoV system for traffic accident collisions detection with an emergency alert mechanism. In: 2018 IEEE 8th international conference on consumer electronics-Berlin (ICCE-Berlin). IEEE, p 1–2

  • Chowdhury DR, Nandi S, Goswami D (2022) Video streaming over Iov using IP multicast. J Netw Comput Appl 197:103259. https://doi.org/10.1016/j.jnca.2021.103259

    Article  Google Scholar 

  • Elhadja B, Salim B (2022) QoS-SDIoV: an efficient QoS routing scheme for software defined internet of vehicles. In: Advances in computing systems and applications: proceedings of the 5th conference on computing systems and applications. p 187–198

  • Gosavi A, Gosavi A (2015) Control optimization with reinforcement learning. Simulation-based optimization: parametric optimization techniques and reinforcement learning. Springer, Boston, MA, pp 197–268

    Chapter  MATH  Google Scholar 

  • Harrabi S, Jaafar IB, Ghedira K (2023) Survey on IoV routing protocols. Wirel Pers Commun 128(2):791–811. https://doi.org/10.1007/s11277-022-09976-5

    Article  Google Scholar 

  • He D, Hancke G, Castiglione A, Meng W (2020) Introduction to the special section on blockchain techniques for the internet of vehicles security (VSI-bciov). Comput Electr Eng 87:106860. https://doi.org/10.1016/j.compeleceng.2020.106860

    Article  Google Scholar 

  • Herbadji A, Goumidi H, Harbi Y, Medani K, Aliouat Z (2020) Blockchain for internet of vehicles security. Blockchain for cybersecurity and privacy. CRC Press, pp 159–197. https://doi.org/10.1201/9780429324932-10

    Chapter  Google Scholar 

  • Jiacheng C, Haibo ZHOU, Ning Z, Peng Y, Lin G, Sherman SX (2016) Software defined internet of vehicles: architecture, challenges and solutions. J Commun Inf Netw 1(1):14–26. https://doi.org/10.1007/bf03391543

    Article  Google Scholar 

  • Jindal A, Aujla GS, Kumar N, Chaudhary R, Obaidat MS, You I (2018) SeDaTiVe: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems. IEEE Netw 32(6):66–73. https://doi.org/10.1109/MNET.2018.1800101

    Article  Google Scholar 

  • Kadhim AJ, Naser JI (2021) Proactive load balancing mechanism for fog computing supported by parked vehicles in Iov-SDN. China Commun 18(2):271–289. https://doi.org/10.23919/jcc.2021.02.019

    Article  Google Scholar 

  • Ksouri C, Jemili I, Mosbah M, Belghith A (2022) Towards general internet of vehicles networking: routing protocols survey. Concurr Comput: Pract Exp 34(7):e5994

    Article  Google Scholar 

  • Kumar TP, Krishna PV (2018) Power modelling of sensors for IoT using reinforcement learning. Int J Adv Intell Paradig 10(1–2):3–22. https://doi.org/10.1504/ijaip.2018.10010528

    Article  Google Scholar 

  • Liang Y, Ma N, Li X, Hu J (2021) Stochastic roadside unit location optimization for information propagation in the internet of vehicles. IEEE Internet Things J 8(17):13316–13327. https://doi.org/10.1109/jiot.2021.3065411

    Article  Google Scholar 

  • Lin K, Li C, Li Y, Savaglio C, Fortino G (2020) Distributed learning for vehicle routing decision in software defined Internet of vehicles. IEEE Trans Intell Transp Syst 22(6):3730–3741

    Article  Google Scholar 

  • Raja G, Dhanasekaran P, Anbalagan S, Ganapathisubramaniyan A, Bashir AK (2020) SDN-enabled traffic alert system for IoV in smart cities. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). p 1093–1098. https://doi.org/10.1109/infocomwkshps50562.2020.9162888

  • Rani P, Hussain N, Khan RAH, Sharma Y, Shukla PK (2021) Vehicular intelligence system: time-based vehicle next location prediction in software-defined internet of vehicles (SDN-IOV) for the smart cities. Intelligence of things AI-IoT based critical-applications and innovations. Springer, Cham, pp 35–54

    Chapter  Google Scholar 

  • Ravi B, Thangaraj J, Shandilya SK (2022) Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles. Phys Commun 50:101498. https://doi.org/10.1016/j.phycom.2021.101498

    Article  Google Scholar 

  • Sharma N, Chauhan N, Chand N (2018) Security challenges in internet of vehicles (IOV) environment. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). https://doi.org/10.1109/icsccc.2018.8703272

  • Shen X, Fantacci R, Chen S (2020) Internet of vehicles [scanning the issue]. Proc IEEE 108(2):242–245

    Article  Google Scholar 

  • Sutton RS, Precup D, Singh S (1999) Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artif Intell 112(1–2):181–211. https://doi.org/10.1016/s0004-3702(99)00052-1

    Article  MathSciNet  MATH  Google Scholar 

  • Tang Y, Wu W (2022) Routing algorithms for heterogeneous vehicular networks. Broadband communications computing and control for ubiquitous intelligence. Springer, Cham, pp 105–123. https://doi.org/10.1007/978-3-030-98064-1_6

    Chapter  Google Scholar 

  • Tao H, Zain JM, Band SB, Sundaravadivazhagan B, Mohamed A, Marhoon HA, Young P (2022) SDN-assisted technique for traffic control and information execution in vehicular adhoc networks. Comput Electr Eng 102:108108. https://doi.org/10.1016/j.compeleceng.2022.108108

    Article  Google Scholar 

  • Vishwakarma L, Nahar A, Das D (2022) Lbsv: Lightweight blockchain security protocol for secure storage and communication in sdn-enabled iov. IEEE Trans Veh Technol 71(6):5983–5994. https://doi.org/10.1109/TVT.2022.3163960

    Article  Google Scholar 

  • Xu W, Zhou H, Cheng N, Lyu F, Shi W, Chen J, Shen X (2018) Internet of vehicles in big data era. IEEE/CAA J Autom Sin 5(1):19–35. https://doi.org/10.1109/jas.2017.7510736

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Puducherry Technological University, Puducherry, 605014, India

    Divya Lanka & Selvaradjou Kandasamy

Authors
  1. Divya Lanka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selvaradjou Kandasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Divya Lanka.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lanka, D., Kandasamy, S. Stochastic modelling of SDN controller for Internet of Vehicles. J Ambient Intell Human Comput 14, 11349–11362 (2023). https://doi.org/10.1007/s12652-023-04649-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-023-04649-y

Keywords

Search

Navigation