A Subspace Approach to Sparse-Sampling-Based Multi-Attribute Data Aggregation in IoT

doi:10.1109/JIOT.2022.3161720

2022-09-15

PRE

DOI：10.1109/JIOT.2022.3161720

原文链接

原文求助

摘要

En 中文

The emergence of the heterogeneous Internet of Things (IoT) has realized the demand for multi-attribute data collection in response to the increasing demand for information in diverse applications. Sparse sampling has been used to reduce network energy consumption in order to extend the life of energy-constrained networks. Real-time multi-attribute data aggregation under the sparse sensing framework has become a research focus. Therefore, we proposed a sparse-sampling-based IoT data aggregation approach to reduce network energy consumption and enable real-time multi-attribute reconstruction. For data collection, a sparse sampling data collection approach is proposed that can successfully collect and transmit the multi-attribute data to the sink even while certain IoT sensor nodes are in the sleep mode. For data reconstruction, a real-time multi-attribute data reconstruction method based on subspace is proposed. The proposed method arranges multi-attribute data in a tensor form in order to further utilize the correlation of multi-attribute data. Subspaces representing the spatial distributions of the multi-attribute data can be obtained from the previously reconstructed data. Incorporating total variation constraint, the proposed method reconstructs the current time slot multi-attribute data with high precision in real time. The experimental results demonstrate the effectiveness of the proposed method in real-time multi-attribute data reconstruction.

Keyword:

Data gathering

data reconstruction

Internet of Things (IoT)

subspace

Tucker decomposition

期刊

IEEE Internet of Things Journal

IF:

8.9

论文数:

1.4W

被引数:

7.8W

机构

hebei university of technology

学者数:

1.8W

论文数: 1.2W

被引数: 10

引用论文

Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles

Robotics: Science and Systems X

IF0

2014-07-12

OAAI

Christian Forster; Matia Pizzoli; Davide Scaramuzza

Grass Biomass Estimation on Zambian Pastures for Future Climate Change Effects Mitigation and Adaptation Using Satellite Imagery and Neural Network Technique

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

IF0

2018-07-01

PREAI

C. Clementini; F. Del Frate; A. Pomente; G. Salvucci; F. Teillard; H. Kanamaru; M. Fujisawa; A. Mottet; A. Heureux

Sparsest Random Scheduling for Compressive Data Gathering in Wireless Sensor Networks

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

IF10.7

2014-10-01

PREAI

Wu, Xuangou; Xiong, Yan; Yang, Panlong; Wan, Shouhong; Huang, Wenchao

Notice of Removal: SAW resonators for magnetic field sensing with (TbCo₂/FeCo) multilayered IDTs as sensitive layer

2017 IEEE International Ultrasonics Symposium (IUS)

IF0

2017-09-01

PREAI

Harshad Mishra; Vincent Polewczyk; Mohammed Moutaouekkil; Nicolas Tiercelin; Cecile Floer; Michel Hehn; Karine Dumesnil; Sami Hage-Ali; Abdelkrim Talbi; Omar Elmazria

Dual frequency array transducer for ultrasonic-enhanced transcranial thrombolysis

IEEE Symposium on Ultrasonics, 2003

IF0

2024-09-06

PREAI

T. Azuma; S.-I. Umemura; T. Kobayashi; M. Izumi; J. Kubota; A. Sasaki; H. Furuhata

Learning a Single Tucker Decomposition Network for Lossy Image Compression With Multiple Bits-per-Pixel Rates

IEEE TRANSACTIONS ON IMAGE PROCESSING

IF13.7

2020-01-01