Skip to main content

Advertisement

Log in

A Novel Internet of Vehicles’s Task Offloading Decision Optimization Scheme for Intelligent Transportation System

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In the future intelligent transportation system (ITSs), there will be a lot of negotiation work between vehicle and vehicle (V2V) and between vehicle and infrastructure (V2I), so it is very necessary to design efficient and energy-saving offloading strategy. Aiming at the three conflicting optimization objectives of offloading delay, energy consumption and load balancing, an efficient and energy-saving offloading decision scheme in the scenario of Internet of vehicles was proposed in this paper. Firstly, the task segmentation model, offloading delay model, energy consumption model, load balancing model and multi-objective optimization model were constructed. Then, based on the comprehensive consideration of data offloading delay, energy consumption and load balance, a task offloading scheme based on MOEA/D was proposed. Finally, the proposed scheme was compared with NSGA-II-based scheme, NSGA-III-based scheme,PESA-II-based scheme and SPEA-II-based scheme. The simulation results show that a task offloading scheme based on MOEA/D is obviously superior to the above schemes in terms of offloading delay, energy consumption and load balancing, and can provide efficient and energy-saving offloading service.

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

Access this article

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.

Fig.1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability

The datasets generated during and analysed during the current study are not publicly available due to [REASON(S) WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.

References

  1. Gerla M, Lee E K,Pau G,et al. 2014 Internet of vehicles: from intelligent grid to autonomous cars and vehicular clouds[C]. In IEEE World Forum on Internet of Things, Seoul, 2014: 241–246.

  2. Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., et al. (2014). A comprehensive survey on vehicular ad hoc network[J]. Journal of Network and Computer Applications, 37(1), 380–392.

    Article  Google Scholar 

  3. Wei-song, S., Xing-zhou, Z., Yi-fan, W., et al. (2019). Edge computing: Current situation and outlook[J]. Journal of Computer Research Development, 56(1), 69–89.

    Google Scholar 

  4. Zi-ming, Z., Fang, L., Zhi-ping, C., et al. (2018). Edge computing: Platforms, applications and challenges[J]. Journal of Computer Research Development, 55(2), 327–337.

    Google Scholar 

  5. Mach, P., & Becvar, Z. (2017). Mobile edge computing: a survey on architecture and computation offloadi ng [J]. IEEE Communication Survey & Tutorials, 19(3), 1628–1656.

    Article  Google Scholar 

  6. Mao, S., Wu, J., Liu, L., et al. (2022). Energy-Efficient Cooperative Communication and Computation for Wireless Powered Mobile-Edge Computing[J]. IEEE Systems Journal, 16(1), 287–298. https://doi.org/10.1109/JSYST.2020.3020474

    Article  Google Scholar 

  7. Malandrino, F., Casetti, C., Chiasserini, C. F., et al. (2014). The role of parked cars in content downloading for vehicular networks[J]. Vehicular Technology IEEE Transactions on, 63(9), 4606–4617.

    Article  Google Scholar 

  8. Hu Y, Cui T, Huang X, et al. 2019 Task offloading based on lyapunov optimization for MEC -assisted platooning[C]. In IEEE 11th International Conference on Wireless Communications and Signal Processing (WCSP), Xi’an, China, Oct. 23–25, 2019. Piscataway, pp.1–5.

  9. Dai, Y., Zhang, K., Maharjan, S., et al. (2020). Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond[J]. IEEE Transactions on Vehicular Technology, 69(10), 12175–12186.

    Article  Google Scholar 

  10. Pham, Q., Leanh, T., Tran, N. H., et al. (2018). Decentralized computation offloading and resource allocation for mobile-edge computing: a matching game approach[J]. IEEE Access, 6, 75868–75885.

    Article  Google Scholar 

  11. Guo, S. T., Xiao, B., Yang, Y., et al. (2019). Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing[J]. IEEE Transactions on Mobile Computing, 18(2), 319–333.

    Article  Google Scholar 

  12. Li, Hongxing, et al. 2016 “Mobile edge computing: Progress and challenges.” In 2016 4th IEEE international conference on mobile cloud computing, services, and engineering (MobileCloud). IEEE.

  13. Xu, X., Zhang, X., Liu, X., et al. (2020). Adaptive Computation Offloading With Edge for 5G-Envisioned Internet of Connected Vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 22(99), 5213–5222.

    Google Scholar 

  14. Xu, X., Cao, H., Geng, Q., et al. (2022). Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment[J]. Concurrency and Computation Practice and Experience, 34(14), 1–15. https://doi.org/10.1002/cpe.5674

    Article  Google Scholar 

  15. Ning, Z., Huang, J., Wang, X., et al. (2019). Mobile edge computing-enabled Internet of vehicles: Toward energy-efficient scheduling[J]. IEEE Network, 33(5), 198–205.

    Article  Google Scholar 

  16. Gao, H., Huang, W., Duan, Y., Yang, X., & Zou, Q. (2019). Research on cost-driven services composition in an uncertain environment. J. Internet Technol., 20(3), 755–769.

    Google Scholar 

  17. Ma, H., Chen, X., Zhou, Z., & Yu, S. (2020). Dynamic Task Offloading for Moving Edge Computing with Green Energy [J]. Journal of Computer Research and Development, 57(09), 1823–1838.

    Google Scholar 

  18. Haibo, Z., Li, Hu., Shanxue, C., & Xiaofan, He. (2019). Computing Offloading and Resource Optimization in Ultra-dense Networks with Mobile Edge Computation[J]. Journal of Electronics & Information Technology, 41(05), 1194–1201.

    Google Scholar 

  19. Shichao, X., Zhixiu, Y., Yongju, X., & Yun, Li. (2020). A Distributed Heterogeneous Task Offloading Methodology for Mobile Edge Computing [J]. Journal of Electronics & Information Technology, 42(12), 2891–2898.

    Google Scholar 

  20. Alqahtani, F., Al-Maitah, M., & Elshakankiry, O. (2022). A proactive caching and offloading technique using machine learning for mobile edge computing users[J]. Computer Communications, 181, 224–235. https://doi.org/10.1016/j.comcom.2021.10.017

    Article  Google Scholar 

  21. Li, M., Xiong, N., Zhang, Y., et al. (2022). Priority-mece: A mobile edge cloud ecosystem based on priority tasks offloading[J]. Mobile Networks and Applications, 27(4), 1768–1777.

    Article  Google Scholar 

  22. Li, X., Wan, J., Dai, H. N., et al. (2019). A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing[J]. IEEE Transactions on Industrial Informatics, 15(7), 4225–4234.

    Article  Google Scholar 

  23. Wang, K., Yu, X. Y., Lin, W. L., et al. (2019). Computing aware scheduling in mobile edge computing system[J]. Wireless Networks, 2019, 1–17. https://doi.org/10.1155/2019/3816237

    Article  Google Scholar 

  24. Liu, J., Li, P., Liu, J., & Lai, J. (2019). Joint Offloading and Transmission Power Control for Mobile Edge Computing. IEEE Access, 7, 81640–81651. https://doi.org/10.1109/ACCESS.2019.2921114

    Article  Google Scholar 

  25. Wu, Q., Zhao, Y., Fan, Q., et al. (2023). Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning[J]. IEEE Journal of Selected Topics in Signal Processing, 17(1), 66–81.

    Article  Google Scholar 

  26. Zhu, S., Zhao, M., & Zhang, Q. (2022). Multi-objective optimal offloading decision for multi-user structured tasks in intelligent transportation edge computing scenario[J]. The Journal of Supercomputing, 78(16), 17797–17825. https://doi.org/10.1007/s11227-022-04549-6

    Article  Google Scholar 

  27. Hossain, M. D., Huynh, L. N. T., Sultana, T., et al. (2020). Collaborative Task Offloading for Overloaded Mobile Edge Computing in Small-Cell Networks[C]. International Conference on Information Networking (ICOIN), 2020, 717–722.

    Google Scholar 

  28. Alameddine, H. A., Sharafeddine, S., Sebbah, S., et al. (2019). Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing[J]. IEEE Journal on Selected Areas in Communications, 37(3), 668–682.

    Article  Google Scholar 

  29. Wang, F., Xu, J., & Cui, S. (2020). Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems[J]. IEEE Transactions on Wireless Communications, 19(4), 2443–2459.

    Article  Google Scholar 

  30. Azizi, S., Othman, M., & Khamfroush, H. (2023). DECO: A Deadline-Aware and Energy-Efficient Algorithm for Task Offloading in Mobile Edge Computing[J]. IEEE systems journal, 17(1), 952–963.

    Article  Google Scholar 

Download references

Funding

This project was supported by Tianjin Natural Science Foundation General Project (22JCZDJC00600).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by ZHU Si-feng, WANG Yu, CHEN Hao and Zhang Hui. The first draft of the manuscript was written by WANG Yu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yu Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts 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

Zhu, Sf., Wang, Y., Chen, H. et al. A Novel Internet of Vehicles’s Task Offloading Decision Optimization Scheme for Intelligent Transportation System. Wireless Pers Commun 137, 2359–2379 (2024). https://doi.org/10.1007/s11277-024-11499-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11499-0

Keywords