Skip to main content
Log in

Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G Network

  • Research
  • Published:
Journal of Grid Computing Aims and scope Submit manuscript


Smart cities cannot function without autonomous devices that connect wirelessly and enable cellular connectivity and processing. Edge computing bridges mobile devices and the cloud, giving mobile devices access to computing, memory, and communication capabilities via vehicular ad hoc networks (VANET). VANET is a time-constrained technology that can handle requests from vehicles in a shorter amount of time. The most well-known problems with edge computing and VANET are latency and delay. Any congestion or ineffectiveness in this network can result in latency, which affects its overall efficiency. The data processing in smart city affected by latency can produce irregular decision making. Some data, like traffics, congestions needs to be addressed in time. Delay decision making can make application failure and results in wrong information processing. In this study, we created a probability-based hybrid Whale -Dragonfly Optimization (p–H-WDFOA) edge computing model for smart urban vehicle transportation that lowers the delay and latency of edge computing to address such issues. The 5G localized Multi-Access Edge Computing (MEC) servers were additionally employed, significantly reducing the wait and the latency to enhance the edge technology resources and meet the latency and Quality of Service (QoS) criteria. Compared to an experiment employing a pure cloud computing architecture, we reduced data latency by 20%. We also reduced processing time by 35% compared to cloud computing architecture. The proposed method, WDFO-VANET, improves energy consumption and minimizes the communication costs of VANET.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /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.

Similar content being viewed by others

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.


  1. Farooqi, A.M., et al.: A fog computing model for vanet to reduce latency and delay using 5G network in smart city transportation. Appl. Sci. 12(4), 2083 (2022)

    Article  MathSciNet  CAS  Google Scholar 

  2. Farooqi, A.M., et al.: A fog computing model for vanet to reduce latency and delay using 5G network in smart city transportation. Appl. Sci. 12(4), 2083 (2023)

    Article  MathSciNet  Google Scholar 

  3. Marwah, G.P.K., et al.: An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication. Mathematics 10(21), 4030 (2022)

    Article  Google Scholar 

  4. Ma, K., et al.: Reliability-Constrained Throughput Optimization of Industrial Wireless Sensor Networks With Energy Harvesting Relay. IEEE Internet Things J. 8(17), 13343–13354 (2021)

    Article  Google Scholar 

  5. Cao, K., Wang, B., Ding, H., Lv, L., Dong, R., Cheng, T.,... Gong, F.: Improving Physical Layer Security of Uplink NOMA via Energy Harvesting Jammers. IEEE Trans. Inf. Forensic. Secur. 16, 786–799 (2021)

  6. Cao, B., Fan, S., Zhao, J., Tian, S., Zheng, Z., Yan, Y.,... Yang, P.: Large-Scale Many-Objective Deployment Optimization of Edge Servers. IEEE Trans. Intell. Transp. Syst. 22(6), 3841–3849 (2021)

  7. Huang, J., Qian, Y., Hu, R.Q.: A Privacy-Preserving Scheme for Location-Based Services in the Internet of Vehicles. J. Commun. Inf. Netw. 6, 385–395 (2021)

    Article  Google Scholar 

  8. Han, Y., Wang, B., Guan, T., Tian, D., Yang, G., Wei, W., Chuah, J.H.: Research on Road Environmental Sense Method of Intelligent Vehicle Based on Tracking Check. IEEE Trans. Intell. Transp. Syst. 24(1), 1261–1275 (2022)

  9. Siddiqui, S.A., Mahmood, A., Sheng, Q.Z., Suzuki, H., Ni, W.: A survey of trust management in the internet of vehicles. Electronics 10, 2223 (2021)

    Article  Google Scholar 

  10. Xu, J., Park, S.H., Zhang, X., Hu, J.: The Improvement of Road Driving Safety Guided by Visual Inattentional Blindness. IEEE Trans. Intell. Transp. Syst. 23(6), 4972–4981 (2022)

    Article  Google Scholar 

  11. Sadio, O., Ngom, I., Lishou, C.: Design and Prototyping of a Software Defined Vehicular Networking. IEEE Trans. Veh. Technol. 69, 842–850 (2020)

    Article  Google Scholar 

  12. Chen, Y.: Research on collaborative innovation of key common technologies in new energy vehicle industry based on digital twin technology. Energy Rep. 8, 15399–15407 (2022)

    Article  Google Scholar 

  13. Forestiero, A.: Metaheuristic algorithm for anomaly detection in Internet of Things leveraging on a neural-driven multiagent system. Knowl. Based Syst. 228, 107241 (2021)

    Article  Google Scholar 

  14. Xu, J., Guo, K., Sun, P.Z.: Driving Performance Under Violations of Traffic Rules: Novice Vs. Experienced Drivers. IEEE Trans. Intell. Veh. 7(4), 908–917 (2022)

  15. Sun, G., Zhang, Y., Yu, H., Du, X., Guizani, M.: Intersection Fog-Based Distributed Routing for V2V Communication in Urban Vehicular Ad Hoc Networks. IEEE Trans. Intell. Transp. Syst. 21(6), 2409–2426 (2020)

    Article  Google Scholar 

  16. Wu, D., Wu, C.: Research on the Time-Dependent Split Delivery Green Vehicle Routing Problem for Fresh Agricultural Products with Multiple Time Windows. Agriculture 12, 793 (2022)

    Article  Google Scholar 

  17. Poongodi, M., Hamdi, M., Sharma, A., Ma, M., Singh, P.K.: DDoS Detection Mechanism Using Trust-Based Evaluation Systemin VANET. IEEE Access 7, 183532–183544 (2019)

    Article  Google Scholar 

  18. Adhikary, K., Bhushan, S., Kumar, S., Dutta, K.: Hybrid Algorithm to Detect DDoS Attacksin VANETs. Wirel. Pers. Commun. 114, 3613–3634 (2020)

    Article  Google Scholar 

  19. Marwah, G.P.K., Jain, A.: A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis. Sci. Rep. 12, 10287 (2022)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sun, G., Song, L., Yu, H., Chang, V., Du, X.,... Guizani, M.: V2V Routing in a VANET Based on the Autoregressive Integrated Moving Average Model. IEEE Trans. Veh. Technol. 68(1), 908–922 (2019)

  21. Sun, L., Liang, J., Zhang, C., Wu, D., Zhang, Y.: Meta-Transfer Metric Learning for Time Series Classification in 6G-Supported Intelligent Transportation Systems. IEEE Trans. Intell. Transp. Syst. (2023)

  22. Rekkas, V.P., Sotiroudis, S., Sarigiannidis, P., Wan, S., Karagiannidis, G.K., Goudos, S.K.: Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends. Electronics 10, 2786 (2021)

    Article  Google Scholar 

  23. Yu, S., Zhao, C., Song, L., Li, Y., Du, Y.: Understanding traffic bottlenecks of long freeway tunnels based on a novel location-dependent lighting-related car-following model. Tunn. Undergr. Space Technol. 136, 105098 (2023)

  24. Zhang, X., Wang, Z., Lu, Z.: Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm. Appl. Energy 306, 118018 (2022)

  25. Karunathilake, T., Förster, A.: A Survey on Mobile Road Side Units in VANETs. Vehicles 4, 482–500 (2022)

    Article  Google Scholar 

  26. Li, Q., Lin, H., Tan, X., Du, S.: Consensus for Multiagent-Based Supply Chain Systems Under Switching Topology and Uncertain Demands. IEEE Trans. Syst. Man Cybern.: Syst. 50(12), 4905–4918 (2020)

    Article  Google Scholar 

  27. Zhang, Y., Li, S., Wang, S., Wang, X., Duan, H.: Distributed bearing-based formation maneuver control of fixed-wing UAVs by finite-time orientation estimation. Aerosp. Sci. Technol. 136, 108241 (2023)

  28. Yang, H., Zhang, X., Li, Z., Cui, J.: Region-level traffic prediction based on temporal multi-spatial dependence graph convolutional network from GPS data. Remote Sens. 14(2), 303 (2022)

  29. Dai, W., Zhou, X., Li, D., Zhu, S., Wang, X.: Hybrid Parallel Stochastic Configuration Networks for Industrial Data Analytics. IEEE Trans. Ind. Inform. 18(4), 2331–2341 (2022)

    Article  Google Scholar 

  30. Wang, Q., Dai, W., Zhang, C., Zhu, J., Ma, X.: A Compact Constraint Incremental Method for Random Weight Networks and Its Application. IEEE Trans. Neural Netw. Learn. Syst. (2023)

  31. Yang, H., Chen, C., Ni, J., Karekal, S.: A hyperspectral evaluation approach for quantifying salt-induced weathering of sandstone. Sci. Total Environ. 885, 163886 (2023)

    Article  ADS  CAS  PubMed  Google Scholar 

  32. Guo, Y., Zhang, C., Wang, C., Jia, X.: Towards Public Verifiable and Forward-Privacy Encrypted Search by Using Blockchain. IEEE Trans. Dependable Secure Comput. 20(3), 2111–2126 (2023)

    Article  Google Scholar 

  33. Zheng, W., Gong, G., Tian, J., Lu, S., Wang, R., Yin, Z.,... Yin, L.: Design of a Modified Transformer Architecture Based on Relative Position Coding. Int. J. Comput. Intell. Syst. 16(1), (2023)

  34. Jiang, Z., Xu, C.: Disrupting the Technology Innovation Efficiency of Manufacturing Enterprises Through Digital Technology Promotion: An Evidence of 5G Technology Construction in China. IEEE Trans. Eng. Manag. (2023)

  35. Li, S., Chen, J., Peng, W., Shi, X., Bu, W.: A vehicle detection method based on disparity segmentation. Multimed. Tools Appl. 82(13), 19643–19655 (2023)

    Article  Google Scholar 

  36. Jiang, Y., Liu, S., Li, M., Zhao, N., Wu, M.: A new adaptive co-site broadband interference cancellation method with auxiliary channel. Digit. Commun. Netw. (2022)

  37. Xiao, Z., Shu, J., Jiang, H., Min, G., Chen, H.,... Han, Z.: Perception Task Offloading With Collaborative Computation for Autonomous Driving. IEEE J. Sel. Areas Commun. 41(2), 457–473 (2023)

  38. Long, W., Xiao, Z., Wang, D., Jiang, H., Chen, J., Li, Y.,... Alazab, M.: Unified Spatial-Temporal Neighbor Attention Network for Dynamic Traffic Prediction. IEEE Trans. Veh. Technol. 72(2), 1515–1529 (2023)

  39. Dai, X., Xiao, Z., Jiang, H., Chen, H., Min, G., Dustdar, S.,... Cao, J.: A Learning-Based Approach for Vehicle-to-Vehicle Computation Offloading. IEEE Internet Things J. 10(8), 7244–7258 (2023)

  40. Chen, B., Hu, J., Zhao, Y., Ghosh, B.K.: Finite-Time Velocity-Free Rendezvous Control of Multiple AUV Systems With Intermittent Communication. IEEE Trans. Syst. Man Cybern.: Syst. 52(10), 6618–6629 (2022)

    Article  Google Scholar 

  41. Fu, Y., Li, C., Yu, F.R., Luan, T.H., Zhao, P.: An Incentive Mechanism of Incorporating Supervision Game for Federated Learning in Autonomous Driving. IEEE Trans. Intell. Transp. Syst. (2023)

  42. Yue, W., Li, C., Wang, S., Xue, N., Wu, J.: Cooperative Incident Management in Mixed Traffic of CAVs and Human-Driven Vehicles. IEEE Trans. Intell. Transp. Syst. 24(11), 12462–12476 (2023)

    Article  Google Scholar 

  43. Ding, C., Li, C., Xiong, Z., Li, Z., Liang, Q.: Intelligent Identification of Moving Trajectory of Autonomous Vehicle Based on Friction Nano-Generator. IEEE Trans. Intell. Transp. Syst. (2023)

  44. Wang, S., Sheng, H., Zhang, Y., Yang, D., Shen, J.,... Chen, R.: Blockchain-Empowered Distributed Multi-Camera Multi-Target Tracking in Edge Computing. IEEE Trans. Ind. Inform. (2023)

  45. Min, H., Li, Y., Wu, X., Wang, W., Chen, L., Zhao, X.: A measurement scheduling method for multi-vehicle cooperative localization considering state correlation. Veh. Commun. 44, 100682 (2023)

  46. Zhao, X., Fang, Y., Min, H., Wu, X., Wang, W.,... Teixeira, R.: Potential sources of sensor data anomalies for autonomous vehicles: An overview from road vehicle safety perspective. Expert Syst. Appl. 236 (2024)

  47. Mou, J., Gao, K., Duan, P., Li, J., Garg, A.,... Sharma, R.: A Machine Learning Approach for Energy-Efficient Intelligent Transportation Scheduling Problem in a Real-World Dynamic Circumstances. IEEE Trans. Intell. Transp. Syst. (2022)

  48. Cao, B., Li, Z., Liu, X., Lv, Z., He, H.: Mobility-Aware Multiobjective Task Offloading for Vehicular Edge Computing in Digital Twin Environment. IEEE J. Sel. Areas Commun. 41(10), 3046–3055 (2023)

    Article  Google Scholar 

  49. Luo, J., Wang, G., Li, G., Pesce, G.: Transport infrastructure connectivity and conflict resolution: a machine learning analysis. Neural Comput. Appl. 34(9), 6585–6601 (2022)

    Article  Google Scholar 

  50. Cao, B., Zhang, J., Liu, X., Sun, Z., Cao, W., Nowak, R.M.,... Lv, Z.: Edge–Cloud Resource Scheduling in Space–Air–Ground-Integrated Networks for Internet of Vehicles. IEEE Internet Things J. 9(8), 5765-5772 (2022)

  51. Lu, J., Osorio, C.: On the Analytical Probabilistic Modeling of Flow Transmission Across Nodes in Transportation Networks. Transp. Res. Rec. 2676(12), 209–225 (2022)

    Article  Google Scholar 

  52. Wu, Q., Fang, J., Zeng, J., Wen, J., Luo, F.: Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments. Tsinghua Sci. Technol. 29(1), 112–126 (2024)

    Article  Google Scholar 

  53. Ma, B., Liu, Z., Dang, Q., Zhao, W., Wang, J., Cheng, Y.,... Yuan, Z.: Deep Reinforcement Learning of UAV Tracking Control Under Wind Disturbances Environments. IEEE Trans. Instrum. Meas., 72 (2023)

  54. Chen, J., Xu, M., Xu, W., Li, D., Peng, W.,... Xu, H.: A Flow Feedback Traffic Prediction Based on Visual Quantified Features. IEEE Trans. Intell. Transp. Syst. 24(9), 10067-10075 (2023)

Download references


This article has been supported under Project Supported by Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202202402) “Research on the digital rural grassroots data quality management system”.

Author information

Authors and Affiliations



Mengqi Wang: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing.

Jiayuan Mao: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Wei Zhao: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Xinya Han: Investigation, Validation, Resources, Writing—review & editing.

Mengya Li: Formal analysis, Supervision, Writing.

Chuanjun Liao: Formal analysis, Supervision, Writing.

Haomiao Sun: Investigation, Resources, Writing—review & editing.

Kexin Wang: Investigation, Resources, Writing—review & editing.

The contribution of the first and second authors are equivalent in this research work.

Corresponding author

Correspondence to Jiayuan Mao.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

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

Wang, M., Mao, J., Zhao, W. et al. Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G Network. J Grid Computing 22, 25 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: