Work

Cloud based collaborative data compression technology for power Internet of Things

Year: 2025

Type: article

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.

Source: Egyptian Informatics Journal

DOI: 10.1016/j.eij.2025.100696

Authors: Qiong Wang, Yongbo Zhou, Jianyong Gao

Institution: State Grid Corporation of China (China)

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,200