- Title
- Improving cognitive ability of edge intelligent IIoT through machine learning
- Creator
- Chen, Baotong; Wan, Jiafu; Lan, Yanting; Imran, Muhammad; Li, Di; Guizani, Nadra
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/182993
- Identifier
- vital:16226
- Identifier
-
https://doi.org/10.1109/MNET.001.1800505
- Identifier
- ISBN:0890-8044 (ISSN)
- Abstract
- Computer-integrated manufacturing is a notable feature of Industry 4.0. Integrating machine learning (ML) into edge intelligent Industrial Internet of Things (IIoT) is a key enabling technology to achieve intelligent IIoT. To realize novel intelligent applications of edge-enhanced IIoT, ML methods are proposed to improve the cognitive ability of edge intelligent IIoT in this article. First, an ML-enabled framework of the cognitive IIoT is proposed. Second, the ML methods are presented to enhance the cognitive ability of IIoT including the ML model of IIoT, data-driven learning and reasoning, and coordination with cognitive methods. Finally, with a focus on the reconfigurable production line, a scenario-aware dynamic adaptive planning (DAP) with deep reinforcement learning (DRL) was conducted. The experimental results show that the DRL-based dynamic adaptive planning (DRL-based DAP) had good performance in an observable IIoT environment. The main purpose of this work is to point out the effects of ML-based optimization methods on the analysis of industrial IoT from the macroscopic view. © 1986-2012 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Network Vol. 33, no. 5 (2019), p. 61-67
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2019 IEEE
- Subject
- 4006 Communications Engineering; 4606 Distributed Computing and Systems Software
- Reviewed
- Funder
- This research work is partially supported by the National Key Research and Development Program of China (No. 2017YFE0101000), the National Natural Science Foundation of China (No. U1801264), the Science and Technology Program of Guangzhou, China (No. 201802030005), Guangdong Province Key Areas R & D Program (Nos. 2019B090919002, 2019B010150002), and the Key Program of Natural Science Foundation of Guangdong Province, China (No. 2017B030311008). Muhammad Imran’s work is supported by the Deanship of Scientific Research through research group number RG-1435-051.
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