Skip to main content
SpringerLink
Log in

An Enhanced AI-Enabled Routing Optimization Algorithm for Internet of Vehicles (IoV)

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Smart automobiles have become popular in recent years, facilitating the expansion of the Internet of Vehicles (IoV) networks. The Internet of Vehicles (IoV) is a network of automobiles with the ability to exchange and analyse data in real-time, necessitating a well-organized and effective data transmission method. Key problems in identifying an optimal path among the cars are cluster stability and dynamic topology change in IoV. The novelty of this manuscript lies in the route optimization method dependent on grid size, orientation, velocity, node number, and range. The proposed approach for creating and evaluating the best cluster head (CH) is derived from Harris Hawks' Optimization for Intelligent Route Clustering, for the optimal discovery of routes amongst the vehicles in the Internet of Vehicles networks. To analyse and validate the proposed method, other cutting-edge techniques are analysed. Considering the constraints such as the number of clusters and network, variable communication ranges, and vehicle quantity, our results suggest that the proposed method performs better than other techniques in the literature. Further experimentations have been performed considering Packet Delivery Ratio (PDR), bandwidth utilization, and latency which shows supremacy over other existing approaches. Furthermore, statistical analysis shows improvement in cluster optimization (by 80%) and increase stability of cluster (by 90.6 R-squared).

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

Similar content being viewed by others

Data Availability

All the data is within the main manuscript.

Code Availability

All the data is within the main manuscript.

References

  1. Omar, N., Yaakob, N., Husin, Z., & Elshaikh, M. (2020). Design and development of GreedLea routing protocol for Internet of Vehicle (IoV). In: IOP Conference Series: Materials Science and Engineering, vol. 767, no. 1, article 012034.

  2. Guo, Z., Zhang, Y., Lv, J., Liu, Y., & Liu, Y. (2021). An online learning collaborative method for traffic forecasting and routing optimization. IEEE Transactions on Intelligent Transportation Systems, vol. 22.

  3. Gawas, M. A., & Govekar, S. S. (2019). A novel selective cross-layer based routing scheme using ACO method for vehicular networks. Journal of Network and Computer Applications, 143, 34–46.

    Article  Google Scholar 

  4. Husnain, G., Anwar, S. (2022). An Intelligent Probabilistic Whale Optimization Algorithm (i-WOA) for Clustering in Vehicular Ad Hoc Networks. International Journal of Wireless Information Networks, 29, 143–156. https://doi.org/10.1007/s10776-022-00555-w

  5. Sumi, L., & Ranga, V. (2018). An IoT-VANET-based traffic management system for emergency vehicles in a smart city. Springer.

  6. Hamid, U. Z., Zamzuri, H., & Limbu, D. K. (2019). Internet of Vehicle (IoV) applications in expediting the implementation of smart highway of autonomous vehicle: a survey. Springer.

  7. Awang, A., Husain, K., Kamel, N., & Aissa, S. (2017). Routing in vehicular ad-hoc networks: A survey on single-and cross-layer design techniques, and perspectives. IEEE Access, 5, 9497–9517.

    Article  Google Scholar 

  8. Sharef, B. T., Alsaqour, R. A., & Ismail, M. (2014). Vehicular communication ad hoc routing protocols: A survey. Journal of Network and Computer Applications, 40, 363–396.

    Article  Google Scholar 

  9. Shah, S. A., Shiraz, M., Nasir, M. K., & Noor, R. B. (2014). Unicast routing protocols for urban vehicular networks: Review, taxonomy, and open research issues. Journal of Zhejiang University SCIENCE C, 15(7), 489–513.

    Article  Google Scholar 

  10. Dua, A., Kumar, N., & Bawa, S. (2014). A systematic review on routing protocols for vehicular ad hoc networks. Vehicular Communications, 1(1), 33–52.

    Article  Google Scholar 

  11. Wahid, I., Ikram, A. A., Ahmad, M., Ali, S., & Ali, A. (2018). State of the art routing protocols in VANETs: A review. Procedia Computer Science, 130, 689–694.

    Article  Google Scholar 

  12. Liu, J., Wan, J., Wang, Q., Deng, P., Zhou, K., & Qiao, Y. (2016). A survey on position-based routing for vehicular ad hoc networks. Telecommunication Systems, 62(1), 15–30.

    Article  Google Scholar 

  13. Brendha, R., & Prakash, V. S.. (2017). A survey on routing protocols for vehicular ad hoc networks. In: 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2017.

  14. Saleh, H. H., & Hasson, S. T. (2019). A survey of routing algorithms in vehicular networks. In: 2019 International Conference on Advanced Science and Engineering (ICOASE), Zakho, Duhok, Iraq, 2019.

  15. Ghaffari, A. (2020). Hybrid opportunistic and position-based routing protocol in vehicular ad hoc networks. Journal of Ambient Intelligence and Humanized Computing, 11(4), 1593–1603.

    Article  Google Scholar 

  16. Abbas, M. T., Muhammad, A., & Song, W. C. (2020). SD-IoV: SDN enabled routing for internet of vehicles in road-aware approach. Journal of Ambient Intelligence and Humanized Computing, 11(3), 1265–1280.

    Article  Google Scholar 

  17. Baykasoğlu, A., & Akpinar, Ş. (2017). Weighted superposition attraction (WSA): A swarm intelligence algorithm for optimization problems - part 1: Unconstrained optimization. Applied Soft Computing, 56, 520–540.

    Article  Google Scholar 

  18. Fatemidokht, H., & Rafsanjani, M. K. (2018). F-Ant: An effective routing protocol for ant colony optimization based on fuzzy logic in vehicular ad hoc networks. Neural Computing and Applications, 29(11), 1127–1137.

    Article  Google Scholar 

  19. Yaqoob, S., Ullah, A., Akbar, M., Imran, M., & Shoaib, M. (2019). Congestion avoidance through fog computing in internet of vehicles. Journal of Ambient Intelligence and Humanized Computing, 10(10), 3863–3877.

    Article  Google Scholar 

  20. Archetti, C., Guerriero, F., & Macrina, G. (2021). The online vehicle routing problem with occasional drivers. Computers & Operations Research, 127, article 105144.

  21. Liu, H., Guo, Z., & Zhang, Z. (2020). A hybrid multi-level optimisation framework for integrated production scheduling and vehicle routing with flexible departure time. International Journal of Production Research, 30, 1–8.

    Google Scholar 

  22. Zhang, D., Li, D., Sun, H., & Hou, L. (2021). A vehicle routing problem with distribution uncertainty in deadlines. European Journal of Operational Research, 292.

  23. Kaur, S., Aseri, T. C., & Rani, S. (2019). QoS-aware routing in vehicular ad hoc networks using ant colony optimization and bee colony optimization. Springer.

  24. Elhoseny, M., & Shankar, K. (2020). Energy efficient optimal routing for communication in VANETs via clustering Model. Springer.

  25. Wang, W., Xia, F., Nie, H. et al. (2020). Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22..

  26. Hussain, S. A., Yusof, K. M., Hussain, S. M., & Singh, A. V. (2019). A review of quality-of-service issues in Internet of Vehicles (IoV). In: 2019 Amity International Conference on Artificial Intelligence (AICAI), Amity University Dubai, 2019.

  27. Cheng, J., Cheng, J., Zhou, M., Liu, F., Gao, S., & Liu, C. (2015). Routing in Internet of Vehicles: A review. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2339–2352.

    Article  Google Scholar 

  28. Kayarga, T., & Kumar, S. A. (2021). A study on various technologies to solve the routing problem in Internet of Vehicles (IoV). Wireless Personal Communications, 119(1), 459–487.

    Article  Google Scholar 

  29. Tuyisenge, L., Ayaida, S., Tohme, & Afilal, L. E. (2018). Network architectures in internet of vehicles (IoV): review, protocols analysis, challenges and issues. In: International Conference on Internet of Vehicles, Paris, France, 2018.

  30. Ksouri, C., Jemili, I., Mosbah, M., & Belghith, A. (2022). Towards general Internet of Vehicles networking: routing protocols survey. Concurrency and Computation: Practice and Experience, 21, article e5994.

  31. Husnain, G., & Anwar, S. (2021). An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET). PLoS One, 16(4), e0250271. https://doi.org/10.1371/JOURNAL.PONE.0250271.

  32. Ahsan, W., et al. (2020). Optimized node clustering in VANETs by using meta-heuristic algorithms. Electronics, 9(3), 394. https://doi.org/10.3390/electronics9030394

    Article  Google Scholar 

  33. Fahad, M., et al. (2018). Grey wolf optimization-based clustering algorithm for vehicular ad-hoc networks. Computers & Electrical Engineering, 70, 853–870. https://doi.org/10.1016/j.compeleceng.2018.01.002

    Article  Google Scholar 

  34. Husnain, G., Anwar, S., & Shahzad, F. (2017). Performance evaluation of CLPSO and MOPSO routing algorithms for optimized clustering in Vehicular Ad hoc Networks. In: Proceedings of 2017 14th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2017,, pp. 772–778. https://doi.org/10.1109/IBCAST.2017.7868141.

  35. Yao, P., & Wang, H. (2017). Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle. Soft Computing, 21(18), 5475–5488. https://doi.org/10.1007/s00500-016-2138-6

    Article  Google Scholar 

  36. Aadil, F., Bajwa, K. B, Khan, S., Chaudary, N. M., & Akram, A. (2016). CACONET: Ant Colony Optimization (ACO) based clustering algorithm for VANET. PLoS One, 11(5), e0154080. https://doi.org/10.1371/journal.pone.0154080.

  37. Shah, Y. A., Habib, H. A., Aadil, F., Khan, M. F., Maqsood, M., & Nawaz, T. (2018). CAMONET: Moth-Flame Optimization (MFO) based clustering algorithm for VANETs. IEEE Access, 6, 48611–48624. https://doi.org/10.1109/ACCESS.2018.2868118

    Article  Google Scholar 

  38. Aadil, F., Ahsan, W., Rehman, Z. U., Shah, P. A., Rho, S., & Mehmood, I. (2018). Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). The Journal of Supercomputing, 74(9), 4542–4567. https://doi.org/10.1007/s11227-018-2305-x

    Article  Google Scholar 

  39. Hossain, M. A., et al. (2021). Multi-objective Harris Hawks optimization Algorithm based 2-Hop Routing Algorithm for CR-VANET. IEEE Access, 9, 58230–58242. https://doi.org/10.1109/ACCESS.2021.3072922

    Article  Google Scholar 

  40. Ghassan Husnain, Shahzad Anwar, Fahim Shahzad, Gulbadan Sikander, Rehan Tariq, Maheen Bakhtyar, & Sangsoon Lim. (2022). An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs. Journal of Sensors, 2022, Article ID 6790082, 15 pages. https://doi.org/10.1155/2022/6790082

  41. Aadil, F., Raza, A., Khan, M., Maqsood, M., Mehmood, I., & Rho, S. (2018). Energy aware cluster-based routing in flying ad-hoc networks. Sensors, 18(5), 1413. https://doi.org/10.3390/s18051413

    Article  Google Scholar 

  42. Raza, A., Khan, M. F., Maqsood, M., Haider, B., & Aadil, F. (2020). Adaptive k-means clustering for Flying Ad-hoc Networks. KSII Transactions on Internet and Information Systems, 14(6), 2670–2685. https://doi.org/10.3837/tiis.2020.06.019

    Article  Google Scholar 

  43. Aadil, F., Young Song, O., Mushtaq, M., Maqsood, M., Ejaz Sheikh, S., & Baber, J. (2020).An efficient cluster optimization framework for internet of things (IoT) based Wireless Body Area Networks. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-02-2020-0075.

  44. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

Download references

Funding

The author(s)received no specific funding for this work.

Author information

Authors and Affiliations

  1. Department of Computer Science, Iqra National University, Peshawar, Pakistan

    Ghassan Husnain

  2. Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan

    Ghassan Husnain & Shahzad Anwar

  3. Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan

    Fahim Shahzad

Authors
  1. Ghassan Husnain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahzad Anwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fahim Shahzad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ghassan Husnain.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Husnain, G., Anwar, S. & Shahzad, F. An Enhanced AI-Enabled Routing Optimization Algorithm for Internet of Vehicles (IoV). Wireless Pers Commun 130, 2623–2643 (2023). https://doi.org/10.1007/s11277-023-10394-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10394-4

Keywords

Search

Navigation