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Author:

Li, Fangyu (Li, Fangyu.) | Lin, Junnuo (Lin, Junnuo.) | Wang, Yu (Wang, Yu.) | Du, Yongping (Du, Yongping.) | Han, Honggui (Han, Honggui.) (Scholars:韩红桂)

Indexed by:

EI Scopus SCIE

Abstract:

Distributed learning-based high-dimensional temporal modeling for the industrial Internet of Things (IIoT) has become a prevailing trend. However, traditional distributed learning inefficiently extracts information by straightforward architects, resulting in low modeling accuracy and high communication costs. We propose a distributed hierarchical temporal graph learning (DHTGL) approach. In terminal equipment, we construct an adaptive hierarchical dilation convolutional network to dynamically capture spatiotemporal features by adjusting the dilation factor at each layer. Next, we construct the adaptive graphs according to the connection similarity between dimensions to capture implicit connections. In the edge device, we design a node-edge graph distance calculation based on the Gromov-Wasserstein distance to group feature graphs and construct representative cluster feature graphs. Edge devices upload cluster feature graphs to reduce communication costs while minimizing information loss. In the central server, we incorporate graph attention networks into the graph neural networks for edge updating in training models on clustered feature graphs. Experiments using the public IIoT data sets and the self-built IIoT platform demonstrate the effectiveness of DHTGL in comparison with common distributed learning approaches. The results confirm that DHTGL consumes fewer communications while achieving higher accuracies.

Keyword:

graph convolutional network (GCN) Feature extraction Industrial Internet of Things Computational modeling Data models Distance learning Costs industrial Internet of Things (IIoT) Distributed learning Computer aided instruction

Author Community:

  • [ 1 ] [Li, Fangyu]Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intelligen, Engn Res Ctr Digital Community,Minist Educ, Beijing 100124, Peoples R China
  • [ 2 ] [Lin, Junnuo]Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intelligen, Engn Res Ctr Digital Community,Minist Educ, Beijing 100124, Peoples R China
  • [ 3 ] [Du, Yongping]Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intelligen, Engn Res Ctr Digital Community,Minist Educ, Beijing 100124, Peoples R China
  • [ 4 ] [Han, Honggui]Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intelligen, Engn Res Ctr Digital Community,Minist Educ, Beijing 100124, Peoples R China
  • [ 5 ] [Li, Fangyu]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China
  • [ 6 ] [Lin, Junnuo]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China
  • [ 7 ] [Du, Yongping]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China
  • [ 8 ] [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China
  • [ 9 ] [Wang, Yu]Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA

Reprint Author's Address:

  • [Han, Honggui]Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intelligen, Engn Res Ctr Digital Community,Minist Educ, Beijing 100124, Peoples R China;;[Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China;;

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Source :

IEEE INTERNET OF THINGS JOURNAL

ISSN: 2327-4662

Year: 2024

Issue: 17

Volume: 11

Page: 28578-28590

1 0 . 6 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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