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A high-performance data analytics method for significant pattern discovery in cognitive IoT

Author

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  • Vidyapati Jha

    (NIT, Raipur)

  • Priyanka Tripathi

    (NIT, Raipur)

Abstract

Since there has been an increase in research on integrating cognition into the Internet of Things (IoT) design and architecture, a new subfield called cognitive IoT (CIoT) has emerged. The CIoT inherits several features and challenges from IoT. We urgently require fast and scalable cognitively-inspired techniques to extract meaningful insights from the vast amounts of heterogeneous data. Therefore, this research primarily aims to derive a data-centric rhythm from a large dataset that can be utilized widely for information extraction and forecasting features. In the first phase, total variation (TV) regularization is used to mitigate corrupted entries. Subsequently, the golden pair is extracted from massive data, and based on this golden value, the forecasting value is computed. It also uses the plausibility value to find the most plausible golden pair. Further, the mean value of the most plausible golden pair is taken to get the most significant pattern contained in the rhythm. We experimentally evaluate the proposed method (accuracy > 99.10%) using environmental data that spans 21.25 years and cross-validate it using multiple criteria.

Suggested Citation

  • Vidyapati Jha & Priyanka Tripathi, 2025. "A high-performance data analytics method for significant pattern discovery in cognitive IoT," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(5), pages 4679-4701, October.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:5:d:10.1007_s11135-025-02180-0
    DOI: 10.1007/s11135-025-02180-0
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