Skip to main content
SpringerLink
Log in

An improved hash algorithm for monitoring network traffic in the internet of things

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

With the prompt development of network technology, Internet of Things (IoT), and the speedy enhancement of network performance, numerous network behaviors, including traffic management, are also growing fast. Therefore, the monitoring technology under high-speed network traffic is crucial and essential for quality of services. In fact, monitoring technology essentially helps in understanding the network behavior better; and effectively increase the abilities to monitor, control, and manage the Internet and huge amount of data. The hash technology is usually used for network monitoring and management. In this paper, we investigate how the high-performance hash technology can be used, and improved, for high-speed networks in order to meet the requirements of increasingly challenging fields of traffic detection and monitoring. Moreover, we propose an improved hash function algorithm based on the original hash function to effectively improve the performance of the hash, and enhance the technology's ability to monitor current high-speed network traffic in the field of IoT systems. The experimental results show that the performance of the improved hash technology can be increased by 35–45%, compared with the traditional hash algorithm, and this degree of improvement is undoubtedly huge for the current high-speed network traffic. Moreover, the improved hash algorithm is approximately 11.5% faster than the classical hash function.

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

Similar content being viewed by others

Data availability

Data sharing does not apply to this article because no data set was generated or analyzed during the current research period.

References

  1. Ageev, S.A., Privalov, A.A., Karetnikov, V.V., et al.: An adaptive method for assessing traffic characteristics in high-speed multiservice communication networks based on a fuzzy control procedure. Autom. Remote Control 82(7), 1222–1232 (2021)

    Article  MATH  Google Scholar 

  2. Wellem, T., Lai, Y.K.: Sketch-guided filtering support for detecting super spreaders in high-speed networks. Electron. Lett. 52(17), 1459–1461 (2016)

    Article  Google Scholar 

  3. Jadidi, Z., Muthukkumarasamy, V., Sithirasenan, E., et al.: A probabilistic sampling method for efficient flow-based analysis. J. Commun. Netw. 18(5), 818–825 (2016)

    Article  Google Scholar 

  4. Lin, W., Wang, X., Tornatore, M., et al.: Scheduling with machine-learning-based flow detection for packet-switched optical data center networks. IEEE/OSA J. Opt. Commun. Netw. 10(4), 365–375 (2018)

    Article  Google Scholar 

  5. Hu, Z., Yan, Q., Luo, J.: ATME: accurate traffic matrix estimation in both public and private datacenter networks. IEEE Trans. Cloud Comput. 6(1), 60–73 (2018)

    Article  Google Scholar 

  6. Zhang, H., Qian, Z., Liu, J., et al.: Fault detection and repairing for intelligent connected vehicles based on dynamic Bayesian network model. IEEE Internet Things J. 5(4), 2431–2440 (2018)

    Article  Google Scholar 

  7. Zhu, E.Z., Ma, Z.J., Sun, H., et al.: The implementation of a secret information hiding framework based on hash function and invisible ASCII characters replacement. Comput. J. 30(3), 28–43 (2019)

    Google Scholar 

  8. Zhang, S., Li, J., Jiang, M., et al.: Scalable discrete supervised multimedia hash learning with clustering. IEEE Trans. Circuits Syst. Video Technol. 28(10), 2716–2729 (2018)

    Article  Google Scholar 

  9. Dang, H., Kanitkar, Y.H., Stedtfeld, R.D., et al.: Abundance of chlorinated solvent and 1,4-dioxane degrading microorganisms at five chlorinated solvent contaminated sites determined via shotgun sequencing. Environ. Sci. Technol. 52(23), 13914–13924 (2018)

    Article  Google Scholar 

  10. Canzian, L., Demiryurek, U., Schaar, M.D.: Collision detection by networked sensors. IEEE Trans. Signal Inf. Process. Over Netw. 2(1), 1–15 (2016)

    Article  MathSciNet  Google Scholar 

  11. Abbas, S., et al.: Improving security of the Internet of Things via RF fingerprinting based device identification system. Neural Comput. Appl. 33(21), 14753–14769 (2021)

    Article  MathSciNet  Google Scholar 

  12. Khurshid, A., et al.: Secure-CamFlow: a device-oriented security model to assist information flow control systems in cloud environments for IoTs. Concurr. Comput.: Pract. Exp. 31(8), e4729 (2019)

    Article  Google Scholar 

  13. Aziz, M.F., et al.: A lightweight and compromise-resilient authentication scheme for IoTs. Trans. Emerg. Telecommun. Technol. 33, e3813 (2019)

    Google Scholar 

  14. Chen, L., Ye, F., Ruan, Y., et al.: An algorithm for highway vehicle detection based on convolutional neural network. EURASIP J. Image Video Process. 2018(1), 1–7 (2018)

    Article  Google Scholar 

  15. Parham, M., Pouyan, A.A.: An effective privacy-aware Sybil attack detection scheme for secure communication in vehicular ad hoc network. Wirel. Pers. Commun. 113(2), 1149–1182 (2020)

    Article  Google Scholar 

  16. Mashud, A., Hasan, M.R., Hui, M.W., et al.: Non-instantaneous deteriorating inventory model under the joined effect of trade-credit, preservation technology and advertisement policy. Kybernetes 49(6), 1645–1674 (2019)

    Article  Google Scholar 

  17. Watson, E.: #Education: the potential impact of social media and hashtag ideology on the classroom. Res. Soc. Sci. Technol. 5(2), 40–56 (2020)

    Google Scholar 

  18. Eha, B., As, C.: The inhibitive action of Calendula officinal is flower heads extract for mild steel corrosion in 1M HCl solution. J. Mater. Res. 9(6), 13509–13523 (2020)

    Google Scholar 

  19. Rostami, F., Feiznia, S., Aleali, M., et al.: Application of grain-size statistics, lithofacies and architectural element in determining depositional environment of Kashkan formation in Merk watershed, Kermanshah. Int. J. Environ. Sci. Technol. 17(3), 1351–1372 (2020)

    Article  Google Scholar 

  20. Mahashabde, S., Sobolev, A., Bengtsson, A., et al.: A frequency selective surface based focal plane receiver for the OLIMPO balloon-borne telescope. IEEE Trans. Terahertz Sci. Technol. 5(1), 145–152 (2017)

    Article  Google Scholar 

  21. Masoudpanah, S.M., Hasheminisari, M., Ghasemi, A.: Magnetic properties and photocatalytic activity of ZnFe2?xLaxO4nanoparticles synthesized by sol–gel auto combustion method. J. Sol-Gel Sci. Technol. 80(2), 487–494 (2016)

    Article  Google Scholar 

  22. Bolaghi, Z.K., Masoudpanah, S.M., Hasheminiasari, M.: Photocatalytic properties of ZnO powders synthesized by conventional and microwave-assisted solution combustion method. J. Sol-Gel Sci. Technol. 86(3), 711–718 (2018)

    Article  Google Scholar 

  23. Chashchin, V.V.: Substantiation for the implementation of controlled coil cooling technology after hot rolling of strip steel. Steel Transl 50(8), 559–566 (2020)

    Article  Google Scholar 

  24. El-Hashash, E.A., Gomha, S.M., El-Arab, E.E.: Utility of pyrazolylchalcone synthon to synthesize azolopyrimidines under grindstone technology. Chem. Pharm. Bull. 65(1), 90–96 (2017)

    Article  Google Scholar 

  25. Edgerly, S., Thorson, K., Bighash, L., Hannah, M.: Posting about politics: Media as resources for political expression on Facebook. J. Inf. Technol. Polit.: JITP 13(2), 108–125 (2016)

    Article  Google Scholar 

  26. Fang, W., Stedtfeld, R.D., Kim, O.S., et al.: Influence of soil characteristics and proximity to Antarctic research stations on abundance of antibiotic resistance genes in soils. Environ. Sci. Technol. 50(23), 12621 (2016)

    Article  Google Scholar 

  27. Chuah, S., Rauschnabel, P., Krey, N., et al.: Wearable technologies the role of usefulness and visibility in smartwatch adoption. Comput. Hum. Behav. 65(1), 276–284 (2016)

    Article  Google Scholar 

  28. Rauschnabel, P.A., Hein, D.W.E., He, J., et al.: Fashion or technology? A fashnology perspective on the perception and adoption of augmented reality smart glasses. i-com 15(2), 179–194 (2016)

    Article  Google Scholar 

Download references

Funding

This work was supported by The National Natural Science Foundation of China (61472256, 61170277). Science and technology development fund of Shanghai University of Technology (16KJFZ035, 2017KJFZ033).

Author information

Authors and Affiliations

  1. School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Teng Zhan & Shiping Chen

  2. Network and Information Center Office, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Shiping Chen

Authors
  1. Teng Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiping Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TZ: writing, editing, SC: data analysis.

Corresponding author

Correspondence to Teng Zhan.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Consent for publication

The picture materials quoted in this article have no copyright requirements, and the source has been indicated.

Ethical approval

This article is ethical, and this research has been agreed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhan, T., Chen, S. An improved hash algorithm for monitoring network traffic in the internet of things. Cluster Comput 26, 961–976 (2023). https://doi.org/10.1007/s10586-022-03623-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-022-03623-1

Keywords

Search

Navigation