Advertisement

Smart IoT information transmission and security optimization model based on chaotic neural computing

  • Lianbing Deng
  • Daming LiEmail author
  • Zhiming Cai
  • Lin Hong
IAPR-MedPRAI
  • 55 Downloads

Abstract

The improvement of human quality of life is inseparable from the support of information technology, and the development of information technology has made human life more convenient. The current era is the information age, and the level of informatization has gradually become one of the important indicators to measure the comprehensive level of a country. The emergence of the Internet of Things has led to rapid development of technologies such as data perception, wireless data transmission, and intelligent information processing. With the increasing use of information transmission, people gradually realize the impact of security issues on themselves and society. In this paper, a smart IoT information transmission and security optimization model based on chaotic neural computing model is proposed. Simulation and analysis show that the proposed algorithm can ensure the availability and confidentiality of data at the same time.

Keywords

Chaotic neural network Computational model Internet of Things Intelligent system Optimization algorithm Information transmission Network security 

Notes

Acknowledgements

The research is supported by the:

The project was funded by National Key R&D Program of China: International cooperation between governments in scientific and technological innovation (No.YS2017YFGH002008): Horizon 2020 Urban Inclusive and Innovative Nature; the project was funded by China Postdoctoral Science Foundation: Assessment and optimization of urban lifeline resilience based on the big data. The project was funded by the Project of Macau Foundation: Social mutual aid (disaster relief) application.

Compliance with ethical standards

Conflict of interest

There is no conflict of interest.

References

  1. 1.
    Papadimitratos P, Haas Z (2007) Secure data communication in mobile ad hoc networks. IEEE J Sel Areas Commun 24(2):343–356CrossRefGoogle Scholar
  2. 2.
    Othman B (2010) Enhancing data security in ad hoc networks based on multipath routing. J Parallel Distrib Comput 70(3):309–315CrossRefzbMATHGoogle Scholar
  3. 3.
    Papadimitratos P, Haas Z (2006) Secure data communication in mobile ad hoc networks. IEEE J Sel Areas Commun 24(2):343–356CrossRefGoogle Scholar
  4. 4.
    Rabin MO (1989) Efficient dispersal of information for security, load balancing, and fault tolerance. J ACM 36(2):336–348MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Gu D (2016) IoT, cloud computing, building smart city information system. Inf Syst Eng 2016:27Google Scholar
  6. 6.
    Fang W, Yunyong Z, Bingyi F et al (2015) IoT and cloud computing to build a smart city information system. Mobile Commun 15:49–53Google Scholar
  7. 7.
    Mingyi D, Yang L, Changfeng J (2016) Application of internet of things technology in refined urban management. J Beijing Jianzhu Univ 32(03):137–143Google Scholar
  8. 8.
    Zhiqiang MI (2016) Research and practice on the training of talents in the application of high-tech IoT application technology in the logistics industry. Comput Knowl Technol 12(11):121–122Google Scholar
  9. 9.
    Ma F (2015) Safety of internet of things. Dev Br 2015(18):109–112Google Scholar
  10. 10.
    Liu B (2016) Internet of things and its core technology analysis. Electron World 16:12Google Scholar
  11. 11.
    Huai W (2014) Research on Accelerating technology and security of digital signature. University of Electronic Science and Technology of China, ChengduGoogle Scholar
  12. 12.
    Wang Z (2016) Application and analysis of secure chip in intelligent terminal. Internet World 08:19–22Google Scholar
  13. 13.
    Henry DIA (1996) Analysis of observed chaotic data. Springer, New YorkzbMATHGoogle Scholar
  14. 14.
    Qiu Y, Bi Y, Li Y, Wang H (2018) High resolution remote sensing image denoising algorithm based on sparse representation and adaptive dictionary learning. In Computational vision and bio inspired computing. Springer, Cham, pp 892–901Google Scholar
  15. 15.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  16. 16.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. In: Twenty-fourth international joint conference on artificial intelligenceGoogle Scholar
  17. 17.
    Allen TT, Sui Z, Parker NL (2017) Timely decision analysis enabled by efficient social media modeling. Decis Anal 14(4):250–260.  https://doi.org/10.1287/deca.2017.0360 MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path. In: Thirtieth AAAI conference on artificial intelligenceGoogle Scholar
  19. 19.
    Lai H, Pan Y, Liu Y, Yan S (2015) Simultaneous feature learning and hash coding with deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3270–3278Google Scholar
  20. 20.
    Chen Q, Zhang G, Yang X, Li S, Li Y, Wang HH (2018) Single image shadow detection and removal based on feature fusion and multiple dictionary learning. Multimed Tools Appl 77(14):18601–18624CrossRefGoogle Scholar
  21. 21.
    Allen TT, Sui Z, Akbari K (2018) Exploratory text data analysis for quality hypothesis generation. Qual Eng.  https://doi.org/10.1080/08982112.2018.1481216 Google Scholar
  22. 22.
    Zhang H, Xu S, Jiao J, Xie P, Salakhutdinov R, Xing EP (2018) Stackelberg GAN: towards provable minimax equilibrium via multi-generator architectures. arXiv preprint arXiv:1811.08010
  23. 23.
    Jiao J, Gao W, Han Y (2018) The nearest neighbor information estimator is adaptively near minimax rate-optimal. In: Advances in neural information processing systems, pp 3160–3171Google Scholar
  24. 24.
    Sui Z (2017) Hierarchical text topic modeling with applications in social media-enabled cyber maintenance decision analysis and quality hypothesis generation. Doctoral dissertation, The Ohio State University. https://etd.ohiolink.edu/pg_10?0::NO:10:P10_ACCESSION_NUM:osu1499446404436637
  25. 25.
    Jiao J, Venkat K, Weissman T (2018) Mutual information, relative entropy and estimation error in semi-martingale channels. IEEE Trans Inf Theory 64(10):6662–6671MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Parker NN, Allen TT, Sui Z (2017) K-means subject matter expert refined topic model methodology (No. TRAC-M-TR-17-008). TRAC-Monterey Monterey United States. http://www.dtic.mil/docs/citations/AD1028777. Accessed 11 Aug 2017
  27. 27.
    Choi C, Esposito C, Wang H, Liu Z, Choi J (2018) Intelligent power equipment management based on distributed context-aware inference in smart cities. IEEE Commun Mag 56(7):212–217CrossRefGoogle Scholar
  28. 28.
    Zhang S, Wang H, Huang W, You Z (2018) Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik 157:866–872CrossRefGoogle Scholar
  29. 29.
    Zhang S, Huang W, Wang H (2018) Lesion detection of computed tomography and magnetic resonance imaging image based on fully convolutional networks. J Med Imaging Health Inform 8(9):1819–1825CrossRefGoogle Scholar
  30. 30.
    Wang N, Gao X, Tao D, Yang H, Li X (2018) Facial feature point detection: a comprehensive survey. Neurocomputing 275:50–65CrossRefGoogle Scholar
  31. 31.
    Ghorai G, Pal M (2018) A note on “Regular bipolar fuzzy graphs” Neural Computing and Applications 21 (1)(2012) 197–205. Neural Comput Appl 30(5):1569–1572CrossRefGoogle Scholar
  32. 32.
    Niu Z, Hua G, Wang L, Gao X (2018) Knowledge-based topic model for unsupervised object discovery and localization. IEEE Trans Image Process 27(1):50–63MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Bhattacharyya A, Sharma M, Pachori RB, Sircar P, Acharya UR (2018) A novel approach for automated detection of focal EEG signals using empirical wavelet transform. Neural Comput Appl 29(8):47–57CrossRefGoogle Scholar
  34. 34.
    Wang N, Gao X, Li J (2018) Random sampling for fast face sketch synthesis. Pattern Recogn 76:215–227CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Lianbing Deng
    • 1
    • 2
  • Daming Li
    • 1
    • 3
    Email author
  • Zhiming Cai
    • 3
  • Lin Hong
    • 4
  1. 1.The Post-Doctoral Research Center, Zhuhai Da Hengqin Science and Technology Development Co.,LtdHengqinChina
  2. 2.Huazhong University of Science and TechnologyWuhanChina
  3. 3.Macau Big Data Research Centre for Urban GovernanceCity University of MacaoMacauChina
  4. 4.School of ManagementBeijing Normal UniversityZhuhaiChina

Personalised recommendations