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Cloud based collaborative data compression technology for power Internet of Things.

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Titel: Cloud based collaborative data compression technology for power Internet of Things.
Autoren: Wang, Qiong1 (AUTHOR) watong48066@163.com, Zhou, Yongbo1 (AUTHOR), Gao, Jianyong1 (AUTHOR)
Quelle: Egyptian Informatics Journal. Jun2025, Vol. 30, pN.PAG-N.PAG. 1p.
Schlagwörter: Wavelet transforms, Internet of things, Parallel processing, Data reduction, Algorithms
Abstract: To address the challenge of explosive data growth in power IoT systems, this study develops a cloud-edge collaborative multi-task computing framework for efficient compression of heterogeneous data. The proposed system builds upon a "microservice-containerization-Kubernetes" architecture that enables parallel processing of multi-source IoT data collected through perception layer devices. At the edge layer, a hybrid performance ontology algorithm first integrates diverse data sources, followed by a two-stage compression approach: wavelet transforms perform initial data aggregation, while tensor Tucker decomposition enables secondary compression for optimized data reduction. Experimental results demonstrate the framework's effectiveness in maintaining stable IoT network operations while achieving compression ratios below 40%, significantly improving upon traditional methods in both efficiency and reliability for power IoT applications. [ABSTRACT FROM AUTHOR]
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Beschreibung
Abstract:To address the challenge of explosive data growth in power IoT systems, this study develops a cloud-edge collaborative multi-task computing framework for efficient compression of heterogeneous data. The proposed system builds upon a "microservice-containerization-Kubernetes" architecture that enables parallel processing of multi-source IoT data collected through perception layer devices. At the edge layer, a hybrid performance ontology algorithm first integrates diverse data sources, followed by a two-stage compression approach: wavelet transforms perform initial data aggregation, while tensor Tucker decomposition enables secondary compression for optimized data reduction. Experimental results demonstrate the framework's effectiveness in maintaining stable IoT network operations while achieving compression ratios below 40%, significantly improving upon traditional methods in both efficiency and reliability for power IoT applications. [ABSTRACT FROM AUTHOR]
ISSN:11108665
DOI:10.1016/j.eij.2025.100696