Skip to main content

Advertisement

SpringerLink
Log in

Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural network

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The study is designed to improve the efficiency of Internet of Things (IoT) structure detection and achieve the smooth operation of IoT networks. First, the connection between the IoT network structure and maxdegree is investigated based on analyzing the IoT supertask network structure to find the main influence factor of maxdegree, along with the conditions for obtaining the optimal maxdegree. Second, a structural algorithm model of optimal supertask network is proposed as the foundation for achieving the minimum maxdegree. Finally, the human behavior recognition database is taken as the research object to verify its performance through the specific instance data. The IoT network structure factors are proved to include the task quantity, resource capacity, number of networks, and Communication Calculation Ratio (CCR). The experimental results also show that the principal factor that affects maxdegree is the number of different tasks. Besides, there is a mutually positive interaction between the network structure and the IoT maxdegree, which complement each other and form the core network of IoT. Moreover, the results reflect the good performance on different datasets of the supertask IoT network structure for the human behavior recognition database. There exists the optimal maxdegree of the model under the condition of 40 tasks, 32 resources, 6 networks and CCR of 6. Furthermore, the proposed algorithm has a shorter length and lower complexity than other related algorithms, which is very suitable for the construction of IoT networks. The research results can provide some references and practical value for the construction and data processing of the IoT structure.

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

References

  1. Oteafy SM, Hassanein HS (2018) IoT in the fog: A roadmap for data-centric IoT development. IEEE Commun Mag 56(3):157–163

    Article  Google Scholar 

  2. Sandeep, C.,Naresh kumar, S.,Pramod kumar, P. Significant Role of Security in IOT Development and IOT Architecture. Journal of Mechanics of Continua and Mathematical Sciences. 2020, 15(6): 174–184.

  3. Taivalsaari A, Mikkonen T (2017) A roadmap to the programmable world: software challenges in the IoT era. IEEE Softw 34(1):72–80

    Article  Google Scholar 

  4. Chen Y, Hu S, Mao H et al (2020) Application of the best evacuation model of deep learning in the design of public structures[J]. Image Vis Comput 102:103975–103981

    Article  Google Scholar 

  5. Kumar S, Tiwari P, Zymbler M (2019) Internet of Things is a revolutionary approach for future technology enhancement: a review. Journal of Big Data 6(1):1–21

    Article  Google Scholar 

  6. Uslu B, Eren T, Gür Ş, Özcan E (2019) Evaluation of the difficulties in the Internet of Things (IoT) with multi-criteria decision-making. Processes 7(3):164–172

    Article  Google Scholar 

  7. Dong L, Mingyue R, Guoying M (2017) Application of Internet of Things technology on predictive maintenance system of coal equipment. Procedia engineering 174:885–889

    Article  Google Scholar 

  8. Alam T (2020) Efficient and secure data transmission approach in cloud-MANET-IoT integrated framework. Tanweer Alam efficient and secure data transmission approach in cloud-MANET-IoT integrated framework. J Telecommun Electron Comput Eng 12(1): 254–263

  9. Izonin I, Tkachenko R, Kryvinska N, Zub K, Mishchuk O, Lisovych T (2019) Recovery of incomplete IoT sensed data using high-performance extended-input neural-like structure. Procedia Comput Sci 160:521–526

    Article  Google Scholar 

  10. Zhang K, Zhu Y, Maharjan S, Zhang Y (2019) Edge intelligence and blockchain empowered 5G beyond for the industrial Internet of Things. IEEE Network 33(5):12–19

    Article  Google Scholar 

  11. Boveiri HR, Khayami R, Elhoseny M, Gunasekaran M (2019) An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications. J Ambient Intell Humaniz Comput 10(9):3469–3479

    Article  Google Scholar 

  12. Din S, Paul A, Ahmad A, Gupta BB, Rho S (2018) Service orchestration of optimizing continuous features in industrial surveillance using big data based fog-enabled internet of things. IEEE Access 6:21582–21591

    Article  Google Scholar 

  13. Tian H, Nan F, Chang CC, Huang Y, Lu J, Du Y (2019) Privacy-preserving public auditing for secure data storage in fog-to-cloud computing. J Netw Comput Appl 127:59–69

    Article  Google Scholar 

  14. Li R, Song T, Mei B, Li H, Cheng X, Sun L (2018) Blockchain for large-scale internet of things data storage and protection. IEEE Trans Serv Comput 12(5):762–771

    Article  Google Scholar 

  15. Wang C, Zhu Y, Shi W, Chang V, Vijayakumar P, Liu B, Mao Y, Wang J, Fan Y (2018) A dependable time series analytic framework for cyber-physical systems of IoT-based smart grid. ACM Trans Cyber-Phys Syst 3(1):1–18

    Google Scholar 

  16. Zou J, Xu C (2018) Frequency offset tolerant synchronization signal design in NB-IoT. Sensors 18(11):4077–4083

    Article  Google Scholar 

  17. Li Y, Chen S, Ye W, Lin F (2018) A joint low-power cell search and frequency tracking scheme in NB-IoT systems for green Internet of Things. Sensors 18(10):3274–3284

    Article  Google Scholar 

  18. Seong K, Jung DK, Yoon DH, Han J-S, Kim JE, Kim TTH, Lee W, Baek KH (2020) Time-interleaved SAR ADC with background timing-skew calibration for UWB wireless communication in IoT systems. Sensors 20(8):2430–2436

    Article  Google Scholar 

  19. Ali S, Dmitrieva M, Ghatwary N, Bano S, Polat G, Temizel A, Krenzer A, Hekalo A, Guo YB, Matuszewski B (2020) A translational pathway of deep learning methods in GastroIntestinal Endoscopy. arXiv preprint arXiv. pp 256–261

  20. Misra S, Mondal A, Khajjayam S (2019) Dynamic big-data broadcast in fat-tree data center networks with mobile IoT devices. IEEE Syst J 13(3):2898–2905

    Article  Google Scholar 

  21. Hui Z, Li J, Gao X, Wang X (2021) Progressive perception-oriented network for single image super-resolution. Inf Sci 546:769–786

    Article  MathSciNet  Google Scholar 

  22. Al-Tam F, Correia N (2019) Fractional switch migration in multi-controller software-defined networking. Comput Netw 157:1–10

    Article  Google Scholar 

  23. Fki E, Tazi S, Drira K (2017) Automated and flexible composition based on abstract services for a better adaptation to user intentions. Futur Gener Comput Syst 68:376–390

    Article  Google Scholar 

  24. Zhu Z, Zhang W, Chaturvedi V, Singh AK (2019) Energy minimization for multicore platforms through DVFS and VR phase scaling with comprehensive convex model. IEEE Trans Comput Aided Des Integr Circuits Syst 39(3):686–699

    Article  Google Scholar 

  25. Hendry A, Johnson MH, Holmboe K (2019) Early development of visual attention: change, stability, and longitudinal associations. Ann Rev Dev Psychol 1:251–275

    Article  Google Scholar 

  26. Yu B, Lee Y, Sohn K (2020) Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN). Transp Res Part C: Emerg Technol 114:189–204

    Article  Google Scholar 

  27. Jiang M, Li Z, Zhang S, Wang S, Wang X, Yuan Q, Wei Z (2020) Drug–target affinity prediction using graph neural network and contact maps. RSC Adv 10(35):20701–20712

    Article  Google Scholar 

  28. Xu J, Wang J, Tian Y et al (2020) SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning[J]. PLoS ONE 15(11):e0242629–e0242636

    Article  Google Scholar 

  29. Du W, Pan SL, Zhou N, Ouyang T (2018) From a marketplace of electronics to a digital entrepreneurial ecosystem (DEE): the emergence of a meta-organization in Zhongguancun, China. Inf Syst J 28(6):1158–1175

  30. Childs AM, Maslov D, Nam Y, Ross NJ, Su Y (2018) Toward the first quantum simulation with quantum speedup. Proc Natl Acad Sci 115(38):9456–9461

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, South China University of Technology, Guangzhou, 510006, China

    Xiao Liu, De-yu Qi, Wen-lin Li & Hao-tong Zhang

Authors
  1. Xiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. De-yu Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-lin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao-tong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to De-yu Qi.

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

Liu, X., Qi, Dy., Li, Wl. et al. Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural network. J Supercomput 78, 5029–5049 (2022). https://doi.org/10.1007/s11227-021-04041-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04041-7

Keywords

Search

Navigation