Skip to main content
SpringerLink
Log in

Novel QoS optimization paradigm for IoT systems with fuzzy logic and visual information mining integration

  • IAPR-MedPRAI
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The Internet of Things is a new round of information technology revolution after computers, the Internet and mobile communications. Internet of Things technology is an important means to improve the level of social information, which will have a profound impact on economic development and social life. IoT can stimulate the economy, increase employment, improve efficiency and make people’s lives and work more convenient. Since fuzzy control can make good use of expert fuzzy information and effectively deal with the complex process of modeling, fuzzy control has received extensive attention once it has been proposed. Fuzzy logic system has become a research hotspot in academic and application fields due to its wide application. Fuzzy system identification includes structure identification and parameter identification. Fuzzy cognitive graph is a kind of soft computing method. It has stronger semantics than neural network because of its intuitive expression ability and powerful reasoning ability. Due to the widespread popularity of visual data acquisition devices, people can use the device to capture a large number of videos and images and spread them over the network in daily learning, production, life, work and entertainment. Computer science and technology, information computing technology, automated detection technology and Internet of Things technology contribute to the research of visual information data. In this paper, we conduct research on the novel QoS optimization paradigm for the IoT systems based on fuzzy logic and visual information mining integration. The experimental results show that the proposed optimization scheme has higher robustness.

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
Fig. 12

Similar content being viewed by others

References

  1. Gates B, Myhrvold N, Rinearson P (1995) The road ahead, 1st edn. Viking Penguin, New York, pp 5–16

    Google Scholar 

  2. ITU Strategy Policy Unit (SPU) (2005) ITU International Reports 2005: the Internet of Things. International Telecommunication Union (ITU), Geneva, pp 8–20

    Google Scholar 

  3. Antoine de Saint-Exupery (2009) Internet of Things-Strategic Research Roadmap, pp 56–77

  4. Mendel JM, John RI (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  Google Scholar 

  5. Zeng J, Liu ZQ (2006) Type-2 fuzzy hidden Markov models and their application to speech recognition. IEEE Trans Fuzzy Syst 14(3):454–467

    Article  Google Scholar 

  6. Li HX, Tong SC (2003) A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems. IEEE Trans Fuzzy Syst 11(1):24–34

    Article  Google Scholar 

  7. Chen B, Liu XP, Tong SC (2007) Adaptive fuzzy output tracking control of MIMO nonlinear uncertain systems. IEEE Trans Fuzzy Syst 15(2):287–300

    Article  Google Scholar 

  8. Tong SC, Li YM (2013) Adaptive fuzzy output feedback control of MIMO nonlinear systems with unknown dead-zone inputs. IEEE Trans Fuzzy Syst 21(1):134–146

    Article  Google Scholar 

  9. Kamalapurkar R, Walters P, Dixon WE (2016) Model-based reinforcement learning for approximate optimal regulation. Automatica 64:94–104

    Article  MathSciNet  MATH  Google Scholar 

  10. Feng B, Chen W, Sun J (2006) Chaotic time series forecasting with PSO-trained RBF neural network. In: DCABES 2006 proceedings, vol 2, pp 787–790 (ISTP)

  11. Alpaydin E (2014) Introduction to machine learning. MIT Press, Cambridge

    MATH  Google Scholar 

  12. Zhang C, Ma Y (2012) Ensemble machine learning. Springer, New York

    Book  MATH  Google Scholar 

  13. Liu Y, Chen J, Su Z et al (2016) Robust head pose estimation using Dirichlet-tree distribution enhanced random forests. Neurocomputing 173:42–53

    Article  Google Scholar 

  14. Louppe G (2014) Understanding random forests: from theory to practice. arXiv preprint arXiv:1407.7502

  15. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337–407

    Article  MATH  Google Scholar 

  16. Zhang J, Hsu W, Lee M (2001) Image mining: issues, frameworks and techniques. In: Proceedings of the second international workshop on multimedia data mining

  17. Kitamoto A (2002) Spatio-temporal data mining for typhoon image collection. J Intell Inf Syst 19(1):25–41

    Article  Google Scholar 

  18. Guadagnina R, Santanab L, Fernedaa E, Pradoa H (2010) Improving image mining through geoprocessing. J Intell Inf Syst 20(1):81–85

    Article  Google Scholar 

  19. Rajendran P, Madheswaran M (2010) Hybrid medical image classification using association rule mining with decision tree algorithm. J Comput 2(1):127–136

    Google Scholar 

  20. Premchaiswadi W, Tungkatsathan A (2010) On-line content-based image retrieval system using joint querying and relevance feedback scheme. WSEAS Trans Comput 9(5):465–474

    Google Scholar 

  21. Louis J, Dunston PS (2018) Integrating IoT into operational workflows for real-time and automated decision-making in repetitive construction operations. Autom Constr 94:317–327

    Article  Google Scholar 

  22. Thota C, Sundarasekar R, Manogaran G, Varatharajan R, Priyan MK (2018) Centralized fog computing security platform for IoT and cloud in healthcare system. In: Krishna Prasad AV (ed) Exploring the convergence of big data and the internet of things. IGI Global, Hershey, pp 141–154

    Chapter  Google Scholar 

  23. Pattar S, Buyya R, Venugopal KR, Iyengar SS, Patnaik LM (2018) Searching for the IoT resources: fundamentals, requirements, comprehensive review and future directions. IEEE Commun Surv Tutor 20:2101–2132

    Article  Google Scholar 

  24. Guck JW, Van Bemten A, Reisslein M, Kellerer W (2018) Unicast QoS routing algorithms for SDN: a comprehensive survey and performance evaluation. IEEE Commun Surv Tutor 20(1):388–415

    Article  Google Scholar 

  25. Patel A, Kaushik P (2018) Improving QoS of VANET using adaptive CCA range and transmission range both for intelligent transportation system. Wirel Pers Commun 100(3):1063–1098

    Article  Google Scholar 

  26. Aljawarneh SA, Yassein MB, Talafha WA (2018) A multithreaded programming approach for multimedia big data: encryption system. Multimed Tools Appl 77(9):10997–11016. https://doi.org/10.1007/s11042-017-4873-9

    Article  Google Scholar 

  27. Radhakrishna V, Aljawarneh SA, Kumar PV, Choo K-KR (2018) A novel fuzzy Gaussian-based dissimilarity measure for discovering similarity temporal association patterns. Soft Comput 22(6):1903–1919. https://doi.org/10.1007/s00500-016-2445-y

    Article  Google Scholar 

  28. Aljawarneh SA, Vangipuram R, Puligadda VK, Vinjamuri J (2017) G-SPAMINE. Future Gener Comput Syst 74(C):430–443. https://doi.org/10.1016/j.future.2017.01.013

    Article  Google Scholar 

  29. Aljawarneh SA, Elkobaisi MR, Maatuk AM (2017) A new agent approach for recognizing research trends in wearable systems. Comput Electron Eng 61(C):275–286. https://doi.org/10.1016/j.compeleceng.2016.12.003

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by National Natural Science Foundation of China, No. 61402544.

Author information

Authors and Affiliations

  1. College of Electronics and Information Engineering, Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan, 430074, People’s Republic of China

    Xiaoping Jiang, Hao Ding & Chenghua Li

  2. Laboratory of Computer Science, Modeling and Optimization of Systems, University of Clermont Auvergne, Clermont-Ferrand, France

    Hongling Shi

Authors
  1. Xiaoping Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongling Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenghua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hao Ding.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, X., Ding, H., Shi, H. et al. Novel QoS optimization paradigm for IoT systems with fuzzy logic and visual information mining integration. Neural Comput & Applic 32, 16427–16443 (2020). https://doi.org/10.1007/s00521-019-04020-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04020-3

Keywords

Search

Navigation