Multivariate Data Acquisition Strategy by Employing Signal Processing on Graph in Edge Computing Based Industrial Internet of Things
- Resource Type
- Conference
- Authors
- Yang, Lishan; Zhang, Yining; Bai, Chenglin; Xu, Hengying; Wang, Guizhen; Wang, Xuezhen
- Source
- 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE) Advanced Algorithms and Control Engineering (ICAACE), 2024 7th International Conference on. :572-576 Mar, 2024
- Subject
- Components, Circuits, Devices and Systems
Robotics and Control Systems
Signal Processing and Analysis
Correlation coefficient
Sufficient conditions
Simulation
Data acquisition
Signal processing algorithms
Signal processing
Reconstruction algorithms
Industrial Internet of Things
edge computing
graph signal processing
- Language
Multivariate data acquisition strategy is a crucial issue in edge computing based Industrial Internet of Things (EC-IIoT), which should be carefully designed to improve energy efficiency and prolong network lifetime of EC-IIoT. Sampling on graph provides a promising opportunity to solve resource limited sensor selection problem via forming the appropriate sampling set and reconstructing original graph signal. In this paper, we translate the multivariate data acquisition strategy to the graph sampling set forming problem, which could exploit the adjacent correlation of the vertices in the spatial and variable domain. The simulation result demonstrates the effectiveness of the proposed data acquisition strategy.