[go: up one dir, main page]

Skip to main content
Log in

Intelligent acquisition algorithm of dynamic traffic data based on Internet of Things

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In order to improve the intelligent traffic management and dispatching capabilities, it is required to optimize the design of intelligent acquisition of dynamic traffic data and optimize the processing of traffic data information by dynamically mining traffic data. Therefore, an intelligent acquisition algorithm of dynamic traffic data based on Internet of Things is proposed in this paper. In this algorithm, the distributed wireless sensor networking is used to construct an Internet of Things model of traffic data acquisition and optimize deployment and design of Internet of Things nodes for data acquisition; the adaptive weighted algorithm is used to process the fusion of dynamic traffic data to extract spectral characteristic quantity of dynamic traffic data, and the spectrum analysis method is used to perform anomaly detection to dynamic traffic data; then the detection results are conducted with fuzzy clustering, so as to realize intelligent acquisition of dynamic traffic data in Internet of Things environment. The simulation results show that in acquisition of dynamic traffic data, this method has high acquisition accuracy under different SNR. In this paper, when the SNR is 4 dB, the acquisition accuracy is 100, while the traditional method is up to 16 dB, the acquisition accuracy is 100. The proposed method has the advantages of high accuracy, good data recall, strong anti-interference ability during acquisition and good self-adaptability.

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

Similar content being viewed by others

References

  1. Nie, E.H., Su, W.J., Yu, C.C.: Research and design of a vehicle-mounted detective system for fatigue driving behavior. Comput. Simul. 30(8), 173–177 (2013)

    Google Scholar 

  2. Xing, S.N., Liu, F.A., Zhao, X.H.: Parallel high utility pattern mining algorithm based on cluster partition. J. Comput. Appl. 36(8), 2202–2206 (2016)

    Google Scholar 

  3. Chen, X.F., Xu, H.G., Ni, A.N.: Dynamic load balancing mechanism and algorithms in parallel microscopic traffic simulation. Comput. Simul. 30(8), 164–168 (2013)

    Google Scholar 

  4. Peng, L.Y.: Traffic data anti step fusion algorithm based on improved sliding mode disturbance control rule. Control Eng. China. 21(4), 515–519 (2014)

    Google Scholar 

  5. Thomas, Y., Xylomenos, G., Tsilopoulos, C., et al.: Object-oriented packet caching for ICN. In: Proceedings of ACM SIGCOMM workshop on ICN. San Francisco, CA, USA, pp. 89–97 (2015)

  6. Ykarim, B., Djamal, B., Allel, H.: On the use of fuzzy dominance for computing service skyline based on QoS. In: IEEE International Conference on Web Services (ICWS). Washington, pp. 540–547 (2011)

  7. Rui, L.L., Li, Q.M.: Short-term traffic flow prediction algorithm based on combined model. JEIT 38(5), 1227–1233 (2016)

    Google Scholar 

  8. Chen, B., Liu, X.P., Liu, K.F., et al.: Fuzzy approximation-based adaptive control of nonlinear delayed systems with unknown dead zone. IEEE Trans. Fuzzy Syst. 22(2), 237–248 (2014)

    Article  Google Scholar 

  9. Tong, S.C., Huo, B.Y., Li, Y.M.: Observer-based adaptive decentralized fuzzy fault-tolerant control of nonlinear large-scale systems with actuator failures. IEEE Trans. Fuzzy Syst. 22(1), 1–15 (2014)

    Article  Google Scholar 

  10. Han, S.I., Lee, J.M.: Fuzzy echo state neural networks and funnel dynamic surface control for prescribed performance of a nonlinear dynamic system. IEEE Trans. Ind. Electron. 61(2), 1099–1112 (2014)

    Article  Google Scholar 

  11. Zeng, W.X., Zhao, Y., He, Z.Q.: A study of a normalized error calibration method based on parallel high-speed data acquisition system. Appl. Mech. Mater. 54(23), 738–739 (2015)

    Google Scholar 

  12. Dai, R., Duan, X.: Research on knowledge acquisition of motorcycle intelligent design system based on rough set. Comput. Comput. Technol. Agric. V 368(23), 16–27 (2016)

    Google Scholar 

  13. Heo, G., Jeon, J.: A study on the data compression technology-based intelligent data acquisition (IDAQ) system for structural health monitoring of civil structures. Sensors 17(7), 1620–1624 (2017)

    Article  Google Scholar 

  14. Cao, C., Cui, F., Xu, L.: Research on intelligent traffic control model and simulation based on the internet of things and cloud platform. J. Comput. Theor. Nanosci. 13(12), 9886–9892 (2016)

    Article  Google Scholar 

  15. Zhao, H., Shi, L., Li, G., et al.: Research on data acquisition time optimization of bus travel time prediction method. In: The workshop on advanced research & technology in industry applications. pp. 15–21 (2016)

  16. Widyantara, I.M.O., Sastra, N.P.: Internet of things for intelligent traffic monitoring system: a case study in denpasar. Int. J. Emerg. Trends Technol. Comput. Sci. 30(3), 169–173 (2015)

    Article  Google Scholar 

  17. Zhan, H., Wang, M., Wang, B., et al.: Research and development of general data acquisition system based on wireless sensor network dynamic network technology. In: Online analysis and computing science, IEEE. pp. 310–314 (2016)

  18. Lei, T.F., Li, W., Wang, J.F., et al.: Research on the characteristic of automotive failure diagnosis based on complex networks. Open Mech. Eng. J. 9(1), 508–513 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Technology Foundation for Selected Overseas Chinese under Grant No. 2016-1901, from the Ministry of Human Resources and Social Security of P.R.C., <Research on the Impact of Intelligent Vehicles on Traffic System>.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Honghai Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, J., Li, H., Yin, S. et al. Intelligent acquisition algorithm of dynamic traffic data based on Internet of Things. Cluster Comput 22 (Suppl 3), 7657–7664 (2019). https://doi.org/10.1007/s10586-018-2348-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2348-z

Keywords