Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Optimizing Environment-aware VANET Clustering using Machine Learning

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Clustering is important to improve the quality of service in many VANET protocols and applications, such as data dissemination, media access control, Internet of vehicles and intrusion detection systems. Since the performance of a clustering algorithm is highly influenced by the surrounding environment, an environment-aware clustering algorithm that can adapt itself to follow the changes in the environment features is required. In this paper, we propose a machine learning-based framework to include this awareness to an arbitrary clustering algorithm. This is done by optimizing the clustering algorithm’s parameters taking into consideration the road structure and the traffic features that affect the clustering performance. Our framework aims to model how the optimum values of different configuration parameters change with the considered features. Then, these models are used to allow the clustering algorithm to adjust its configurations in real-time according to the measured environment features. After applying this framework on a state-of-the-art clustering algorithm, the performance of the resulting algorithm is compared to other clustering schemes as well as the original algorithm. The obtained results prove the efficiency of the proposed approach reflected by the significant improvements in the different quality metrics and maintaining these metrics in the highest possible levels despite the changes in the considered environment features.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. We use the term dataset to indicate the collection of data that is used to train the machine learning model.

  2. These are several regression loss functions. In this work, we use Mean Square Error (MSE) which is the most commonly used function. The details of the regression function and regression loss are described in Section 2.3.1.

Abbreviations

CH:

Cluster Head

CHA:

CH Alienation

CHL:

Cluster Head Lifetime

CM:

Cluster Member

CML:

CM Life-Time

CPO:

Control Packet Overhead

CVL:

Cross Validation

DHC:

Double Head Clustering

DDHC:

Dynamic DHC

EWMA:

Exponentially Weighted Moving Average

GA:

Genetic Algorithm

ITS:

Intelligent Transportation System

MaOO:

Many Objectives Optimization

MLS-SVR:

Multi-output Least-Squares Support Vector Regression

NOC:

Number of Clusters

PMS:

Parameter Modeling Stage

SSS:

Solutions Selection Stage

SVR:

Support Vector Regression

TB:

Threshold-Based

UFC:

Unified framework for clustering

V2V:

Vehicular to Vehicular

V2X:

Vehicular to Thing

VANET:

Vehicular Ad hoc Network

VAT:

Vehicle Alienation Time

References

  1. Noor-A-Rahim, Md. et al.: 6G for vehicle-to-everything (V2X) communications: Enabling technologies, challenges, and opportunities. Proceedings of the IEEE, 110 (2022)

  2. El Zorkany, M., Yasser, A., Galal, A.I.: Vehicle to vehicle V2V communication: scope, importance, challenges, research directions and future. The Open Transp. J. 11, (2020)

  3. Hamdi, M., Audah, L., Rashid, S.: Data dissemination in VANETs using clustering and probabilistic forwarding based on adaptive jumping multi-objective firefly optimization. IEEE Access, 10 (2022)

  4. Shah, A.S., Karabulut, M.A., Ilhan, H., Tureli, U.: Performance optimization of cluster-based MAC protocol for VANETs. IEEE Access, 8 (2020)

  5. Bangui, H., Ge, M., Buhnova, B.: A hybrid data-driven model for intrusion detection in VANET. Procedia Comput Sci. 184 (2021)

  6. Hosmani, S., Mathapati, B.: Efficient Vehicular Ad Hoc Network routing protocol using weighted clustering technique. Int. J. Inf. Technol. 13 (2021)

  7. Gasmi, R., Harous, S.: Robust Connectivity-Based Internet of Vehicles Clustering Algorithm. Wirel. Pers. Commun. 18 (2022) (2022)

  8. Kaur, R., Ramachandran, R., Doss, R., Pan, L.: The importance of selecting clustering parameters in VANETs: A survey. Comput. Sci. Rev. 40, (2021)

  9. Husnain, G., Anwar, S.: An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET). Plos one, 16 (2021)

  10. Zhang, J., Ren, M., Labiod, H., Khoukhi, L.: Every dog has its day: A comparative study of clustering algorithms in VANETs. In: IEEE Symposium on Computers and Communications (ISCC) (2017)

  11. Arena, F., Pau, G.: An overview of vehicular communications. Future Internet, 11 (2019)

  12. Vodopivec, S., Bester, J., Kos, A.: A multihoming clustering algorithm for vehicular ad hoc networks. Int. J. Distrib. Sensor Netw. 10, 107085 (2014)

    Article  Google Scholar 

  13. Liu, L., Chen, C., Qiu, T., Zhang, M., Li, S., Zhou, B.: A data dissemination scheme based on clustering and probabilistic broadcasting in VANETs. Veh. Commun. 13, 78–88 (2018)

    Google Scholar 

  14. Ucar, S., Ergen, S.C., Ozkasap, O.: Multihop-cluster-based IEEE 802.11p and LTE hybrid architecture for VANET safety message dissemination. Trans. Veh. Technol. 65:2621–2636 (2016)

  15. Shi, C., Zhou, Y., Li, W., Li, H., Lu, N., Cheng, N., Yang, T.: A centralized clustering based hybrid vehicular networking architecture for safety data delivery. In: IEEE Global Communications Conference (GLOBECOM) (2017)

  16. Dong, W., Lin, F., Zhang, H., Yin, Y.: A cluster-based recursive broadcast routing algorithm to propagate emergency messages in city VANETs. In: IEEE International Conference on Communication Software and Networks (ICCSN) (2017)

  17. Ramakrishnan, B., Selvi, M., Nishanth, R.B., Joe, M.M.: An emergency message broadcasting technique using transmission power based clustering algorithm for vehicular ad hoc network. Wirel. Pers. Commun. 94, 3197–3216 (2017)

    Article  Google Scholar 

  18. Chaurasia, B.K., Alam, M.I., Prakash, A., Tomar, R.S., Verma, S.: MPMAC: Clustering based mac protocol for VANETs. Wirel. Pers. Commun., pp. 1–28 (2019)

  19. Hernandez-Jayo, U., Mammu, A.S.K., Sainz, N.: Deterministic MAC protocol based on clustering for VANETs. Intell. Transp. Syst., pp. 135–152 (2016)

  20. Bharati, S., Zhuang, W.: CAH-MAC: cooperative ADHOC MAC for vehicular networks. IEEE J. Sel. Areas Commun. 31, 470–479 (2013)

    Article  Google Scholar 

  21. Elhoseny, M. Shankar, K.: Energy Efficient Optimal Routing for Communication in VANETs via Clustering Model, pp. 1–14. Springer International Publishing (2020)

  22. Aadil, F., Ahsan, W., Rehman, Z.U., Shah, P.A., S. Rho, Mehmood, I.: Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). J. Supercomput. 74, 4542–4567 (2018)

  23. Khan, Z., Fan, P., Fang, S., Abbas, F.: An unsupervised cluster-based VANET-oriented evolving graph (CVoEG) model and associated reliable routing scheme. IEEE Trans. Intell. Transp. Syst., pp. 1–16 (2019)

  24. Ebadinezhad, S., Dereboylu, Z., Ever, E.: Clustering-based modified ant colony optimizer for internet of vehicles (CACOIOV). Sustainability 11, 2624 (2019)

    Article  Google Scholar 

  25. Kumar, N., Singh, J.P., Bali, R.S., Misra, S., Ullah, S.: An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing. Clust. Comput. 18(3), 1263–1283 (2015)

    Article  Google Scholar 

  26. Subba, B., Biswas, S., Karmakar, S.: A game theory based multi layered intrusion detection framework for wireless sensor networks. Int. J. Wirel. Inf. Netw., pp. 1–23 (2018)

  27. BrijilalRuban, C., Paramasivan, B.: Cluster-based secure communication and certificate revocation scheme for VANET. Comput. J. 62, 263–275 (2018)

    Article  Google Scholar 

  28. Alsuhli, G., Fahmy, Y., Khattab, A.: A bio-inspired metaheuristic framework for clustering optimization in VANETs. IET Intell. Transp, Syst (2020)

    Google Scholar 

  29. Ahsan, W., Khan, M.F., Aadil, F., Maqsood, M., Ashraf, S., Nam, Y., Rho, S.: Optimized node clustering in VANETs by using meta-heuristic algorithms. Electronics 9, 394 (2020)

    Article  Google Scholar 

  30. Fahad, M., Aadil, F., Rehman, Z., Khan, S., Shah, P.A., Muhammad, K., Lloret, J., Wang, H., Lee, J.W., Mehmood, I.: Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Comput. Electr. Eng. 70, 853–870 (2018)

    Article  Google Scholar 

  31. Khan, M.F., Aadil, F., Maqsood, M., Bukhari, S.H.R., Hussain, M., Nam, Y.: Moth flame clustering algorithm for internet of vehicle (MFCA-IoV). IEEE Access 7, 11613–11629 (2019)

    Article  Google Scholar 

  32. Ishtiaq, A., Ahmed, S., Khan, M.F., Aadil, F., Maqsood, M., Khan, S.: Intelligent clustering using moth flame optimizer for vehicular ad hoc networks. Int. J. Distrib. Sensor Netw. 15 (2019)

  33. Cooper, C., Franklin, D., Ros, M., Safaei, F., Abolhasan, M.: A comparative survey of VANET clustering techniques. IEEE Commun. Surv. Tutor. 19, 657–681 (2017)

    Article  Google Scholar 

  34. Ren, M., Zhang, J., Khoukhi, L., Labiod, H., Vèque, V.: A unified framework of clustering approach in vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 19, 1401–1414 (2018)

    Article  Google Scholar 

  35. Hadded, M., Zagrouba, R., Laouiti, A., Muhlethaler, P., Saidane, L.A.: A multi-objective genetic algorithm-based adaptive weighted clustering protocol in VANETs. In: IEEE Congress on Evolutionary Computation (CEC) (2015)

  36. Alsuhli, G.H., Khattab, A., Fahmy, Y.A.: An evolutionary approach for optimized VANET clustering. In: IEEE International Conference on Microelectronics (ICM) (2019)

  37. C.S. Cooper, Clustering in vanets under realistic channel conditions. Ph.D. thesis, University of Wollongong (2015)

  38. Cooper, C., Ros, M., Safaei, F., Franklin, D., Abolhasan, M.: Simulation of contrasting clustering paradigms under an experimentally-derived channel model. In: IEEE Vehicular Technology Conference (VTC2014-Fall) (2014)

  39. Alsuhli, G.H., Khattab, A., Fahmy, Y.A.: Double-head clustering for resilient VANETs. Wirel. Commun. Mobile Comput. 2019, 17 (2019)

    Article  Google Scholar 

  40. Braun, M.A., Shukla, P.K., Schmeck, H.: Obtaining optimal Pareto front approximations using scalarized preference information. In: ACM Annual Conference on Genetic and Evolutionary Computation (2015)

  41. Golkarnarenji, G., Naebe, M., Badii, K., Milani, A.S., Jazar, R.N., Khayyam, H.: A machine learning case study with limited data for prediction of carbon fiber mechanical properties. Comput. Ind. 105, 123–132 (2019)

    Article  Google Scholar 

  42. Wen, Z., Shi, J., Li, Q., He, B., Chen, J.: ThunderSVM: A fast SVM library on GPUs and CPUs. J. Mach Learn Res. 19, 1–5 (2018)

    MathSciNet  Google Scholar 

  43. Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems 9. MIT Press (1997)

  44. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing, 14:199–222 (2004)

  45. Singh, T., Pal, M., Arora, V.: Modeling of oblique load test on batter pile group based on support vector machines and gaussian regression. Geotech. Geol. Eng. 36, 1597–1607 (2018)

    Article  Google Scholar 

  46. Xu, S., An, X., Qiao, X., Zhu, L., Li, L.: Multi-output least-squares support vector regression machines. Pattern Recognit Lett. 34, 1078–1084 (2013)

    Article  Google Scholar 

  47. Simulation of Urban MObility (SUMO). (2021) https://sourceforge.net/projects/sumo/. Accessed December,

  48. Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation. Springer, US (2009)

    Book  Google Scholar 

  49. Network Simulator (NS3). (2021) http://www.nsnam.org/. Accessed December

  50. Marella, A., Bonfanti, A., Bortolasor, G., Herman, D.: Implementing innovative traffic simulation models with aerial traffic survey. CRC Press, pp. 571–578 (2017)

  51. Deep, K., et al.: A real coded genetic algorithm for solving integer and mixed integer optimization problems. Appl. Math. Comput. 212, 505–518 (2009)

    MathSciNet  MATH  Google Scholar 

  52. Rawashdeh, Z.Y., Mahmud, S.M.: A novel algorithm to form stable clusters in vehicular ad hoc networks on highways. EURASIP J. Wirel. Commun. Netw. 2012, 15 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. EECE Department, Cairo University, Giza, Egypt

    Yasmine Fahmy & Ahmed Khattab

  2. System-on-Chip Center (SoCC), Khalifa University, Abu Dhabi, United Arab Emirates

    Ghada Alsuhli

Authors
  1. Yasmine Fahmy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghada Alsuhli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed Khattab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasmine Fahmy.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

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

Fahmy, Y., Alsuhli, G. & Khattab, A. Optimizing Environment-aware VANET Clustering using Machine Learning. Int. J. ITS Res. 21, 394–408 (2023). https://doi.org/10.1007/s13177-023-00357-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-023-00357-1

Keywords

Search

Navigation