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Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications

Author

Listed:
  • E. B. Priyanka

    (Kongu Engineering College)

  • S. Thangavel

    (Kongu Engineering College)

  • K. Martin Sagayam

    (Karunya Institute of Technology and Sciences)

  • Ahmed A. Elngar

    (Beni-Suef University)

Abstract

In the modern evolution, WSN (Wireless Sensor Network) incorporated with data aggregation platform which involves stimulating research area with various modern upgradation of AI (Artificial Intelligence). Many types of research are carried out by undertaking variety of Deep Learning Network and Fuzzy based data aggregation techniques in the interpretation of Wireless Sensor Circumstance. The focal theme of the proposed research paper is to analyze the present concentrated work on Artificial Intelligence-accompanied data aggregation paradigm in wireless communication by elaborating the integration framework. By this proposed AI with data aggregation wireless sensor system has upgraded the innovation with high empowering pillars in analyzing the data with more processing and interpretations. Since it also improves the data transmission rate by providing more security and encryption schemes to the preprocessed data storing and streaming ton the bandwidth channels. This paper enumerates the AI contribution on the computing platform with the various advancement of blockchain technology schematic framework and further its case study experimentation on the aero-engine applications. Further experimental results are provided with numerical analysis by showing the traditional and advanced- Long term Memory technique prediction results by taking aero-engine wireless sensor network environment through MATLAB simulation.

Suggested Citation

  • E. B. Priyanka & S. Thangavel & K. Martin Sagayam & Ahmed A. Elngar, 2022. "Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1254-1267, June.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01425-z
    DOI: 10.1007/s13198-021-01425-z
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    References listed on IDEAS

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    1. Wei-Lun Chang & Arleen N. Diaz & Patrick C. K. Hung, 2015. "Estimating trust value: A social network perspective," Information Systems Frontiers, Springer, vol. 17(6), pages 1381-1400, December.
    2. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
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    Cited by:

    1. Martin Kenyeres & Jozef Kenyeres, 2023. "Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study," Future Internet, MDPI, vol. 15(5), pages 1-24, May.

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