Skip to main content
SpringerLink
Log in

Edge Clustering and Communication Efficiency with GNNs in Internet of Vehicles

  • Conference paper
  • First Online:
The 6th International Conference on Wireless, Intelligent and Distributed Environment for Communication (WIDECOM 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 185))

  • 80 Accesses

Abstract

Vehicular edge networks are pivotal in delivering services and applications that rely heavily on efficient resource allocation. Various strategies utilize intelligence, prediction, optimization, and incentive modelling to ensure optimal functioning within these networks. Despite the advancements, vehicular networks face persistent challenges that inhibit efficient resource allocation and communication. The most notable challenges are sporadic connectivity, transmission delays, and inherent uncertainty due to highly dynamic environments. In light of these challenges, integrating graph neural networks (GNNs), which learn hidden spatial and functional patterns of complex vehicular networks with clustering methodologies, emerges as a promising solution. By harnessing the power of GNNs and clustering, this approach provides an opportunity for more intelligent organization of the network nodes to reduce transmission delays and to improve resource allocation in highly dynamic environments. It creates a holistic environment that supports predictions and estimates based on trending communication and mobility features.

This work is partially supported by Canada Research grants.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Source Code for the project is hosted on https://github.com/jegraham/1-GNN-Clustering.

  2. 2.

    PyTorch Geometric https://pytorch-geometric.readthedocs.io/en/latest/.

References

  1. Benhaddou, D.: Wireless Sensor and Mobile Ad-hoc Networks: Vehicular and Space Applications. Springer (2015)

    Google Scholar 

  2. Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proc. of the ACM Int. Conference on Information & Knowledge Management, pp. 3767–3776 (2021)

    Google Scholar 

  3. Dey, K.C., Rayamajhi, A., Chowdhury, M., Bhavsar, P., Martin, J.: Vehicle-to-vehicle (v2v) and vehicle-to-infrastructure (v2i) communication in a heterogeneous wireless network—performance evaluation. Transp. Res. Part C Emerg. Technol. 68, 168–184 (2016)

    Article  Google Scholar 

  4. Gai, K., Qiu, M., Zhao, H., Tao, L., Zong, Z.: Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. J. Network Comput. Appl. 59, 46–54 (2016)

    Article  Google Scholar 

  5. Gasmi, R., Aliouat, M.: Vehicular ad hoc networks versus Internet of Vehicles - a comparative view. In: Proc. of the IEEE Int. Conference on Networking and Advanced Systems (ICNAS), pp. 1–6 (2019)

    Google Scholar 

  6. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30, (2017)

    Google Scholar 

  7. Hu, H., Lee, M.J.: Graph neural network-based clustering enhancement in VANET for cooperative driving. In: Proc. of the Int. IEEE Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 162–167 (2022)

    Google Scholar 

  8. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. Preprint (2014). arXiv:1412.6980

    Google Scholar 

  9. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2016)

    Google Scholar 

  10. Kipf, T.N., Welling, M.: Variational graph auto-encoders (2016). ArXiv:1611.07308 [cs, stat]

    Google Scholar 

  11. Lee, S.S., Lee, S.: Resource allocation for vehicular fog computing using reinforcement learning combined with heuristic information. IEEE Internet Things J. 7(10), 10,450–10,464 (2020)

    Article  Google Scholar 

  12. Lin, C.H., Fang, Y.H., Chang, H.Y., Lin, Y.C., Chung, W.H., Lin, S.C., Lee, T.S.: GCN-CNVPS: Novel method for cooperative neighboring vehicle positioning system based on graph convolution network. IEEE Access 9, 153,429–153,441 (2021)

    Google Scholar 

  13. Lin, K., Gao, J., Li, Y., Savaglio, C., Fortino, G.: Multi-granularity collaborative decision with cognitive networking in intelligent transportation systems. IEEE Trans. Intell. Transp. Syst., 1–11 (2022)

    Google Scholar 

  14. Liu, Y., Xu, C., Zhan, Y., Liu, Z., Guan, J., Zhang, H.: Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach. Comput. Networks 129, 399–409 (2017)

    Article  Google Scholar 

  15. LiWang, M., Hosseinalipour, S., Gao, Z., Tang, Y., Huang, L., Dai, H.: Allocation of computation-intensive graph jobs over vehicular clouds in IoV. IEEE Internet Things J. 7(1), 311–324 (2020)

    Article  Google Scholar 

  16. Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using SUMO. In: Proc. of the IEEE Int. Conf. on Intelligent Transportation Systems, pp. 2575–2582 (2018)

    Google Scholar 

  17. Luong, N.C., Wang, P., Niyato, D., Wen, Y., Han, Z.: Resource management in cloud networking using economic analysis and pricing models: A survey. IEEE Commun. Surv. Tutor. 19(2), 954–1001 (2017)

    Article  Google Scholar 

  18. Meneguette, R.I., Boukerche, A., Pimenta, A.H.M., Meneguette, M.: A resource allocation scheme based on semi-Markov decision process for dynamic vehicular clouds. In: Proc. of the IEEE Int. Conference on Communications (ICC), pp. 1–6 (2017)

    Google Scholar 

  19. Opolka, F.L., Solomon, A., Cangea, C., Veličković, P., Liò, P., Hjelm, R.D.: Spatio-temporal deep graph infomax (2019). ArXiv:1904.06316 [cs, stat]

    Google Scholar 

  20. Shen, Q., Zhu, S., Pang, Y., Zhang, Y., Wei, Z.: Temporal aware multi-interest graph neural network for session-based recommendation (2021). ArXiv:2112.15328 [cs]

    Google Scholar 

  21. Shi, J., Du, J., Wang, J., Wang, J., Yuan, J.: Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning. IEEE Trans. Veh. Technol. 69(12), 16,067–16,081 (2020)

    Article  Google Scholar 

  22. Sommer, C., Eckhoff, D., Brummer, A., Buse, D.S., Hagenauer, F., Joerer, S., Segata, M.: Veins: The open source vehicular network simulation framework. In: Springer Recent Advances in Network Simulation, pp. 215–252. Springer (2019)

    Google Scholar 

  23. Sun, F., Hou, F., Cheng, N., Wang, M., Zhou, H., Gui, L., Shen, X.: Cooperative task scheduling for computation offloading in vehicular cloud. IEEE Trans. Veh. Technol. 67(11), 11,049–11,061 (2018)

    Article  Google Scholar 

  24. Tam, P., Song, I., Kang, S., Ros, S., Kim, S.: Graph neural networks for intelligent modelling in network management and orchestration: A survey on communications. Electronics 11(20), 3371 (2022)

    Article  Google Scholar 

  25. Varga, A., Hornig, R.: An overview of the OMNeT++ simulation environment. In: Proc. of the Int. Conf. on Simulation Tools and Techniques for Communications, Networks and Systems, p. 10 (2010)

    Google Scholar 

  26. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)

    Google Scholar 

  27. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR (2019)

    Google Scholar 

  28. Wu, Q., Shen, J., Yong, B., Wu, J., Li, F., Wang, J., Zhou, Q.: Smart fog based workflow for traffic control networks. Future Gener. Comput. Syst. 97, 825–835 (2019)

    Article  Google Scholar 

  29. Xiong, L., Zhang, Z., Yao, D.: A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network. Wirel. Netw. 28(2), 621–634 (2022)

    Article  Google Scholar 

  30. Yadav, R., Zhang, W., Kaiwartya, O., Song, H., Yu, S.: Energy-latency trade off for dynamic computation offloading in vehicular fog computing. IEEE Trans. Veh. Technol. 69(12), 14,198–14,211 (2020)

    Article  Google Scholar 

  31. Zhang, H., Liu, Z., Hasan, S., Xu, Y.: Joint optimization strategy of heterogeneous resources in multi-MEC-server vehicular network. Wirel. Netw. 28(2), 765–778 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Brock University, St. Catharines, ON, Canada

    Jessica Graham, Anthony Medico, Renata Dividino & Robson E. De Grande

Authors
  1. Jessica Graham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony Medico
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renata Dividino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robson E. De Grande
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jessica Graham .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  2. Department of Information Technology, Netaji Subhas University of Technology, New Delhi, Delhi, India

    Sanjay Kumar Dhurandher

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Graham, J., Medico, A., Dividino, R., De Grande, R.E. (2024). Edge Clustering and Communication Efficiency with GNNs in Internet of Vehicles. In: Woungang, I., Dhurandher, S.K. (eds) The 6th International Conference on Wireless, Intelligent and Distributed Environment for Communication. WIDECOM 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 185. Springer, Cham. https://doi.org/10.1007/978-3-031-47126-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47126-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47125-4

  • Online ISBN: 978-3-031-47126-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation