List of Titles

Toward dynamic resources management for IoT-based manufacturing

- Wan, Jiafu, Chen, Baotong, Imran, Muhammad, Tao, Fei, Li, Di

Authors: Wan, Jiafu , Chen, Baotong , Imran, Muhammad , Tao, Fei , Li, Di
Date: 2018
Type: Text , Journal article
Relation: IEEE Communications Magazine Vol. 56, no. 2 (2018), p. 52-59
Full Text: false
Reviewed:
Description: The cyber-physical production system (CPPS), which combines information communication technology, cyberspace virtual technology, and intelligent equipment technology, is accelerating the path of Industry 4.0 to transform manufacturing from traditional to intelligent. The Industrial Internet of Things integrates the key technologies of industrial communication, computing, and control, and is providing a new way for a wide range of manufacturing resources to optimize management and dynamic scheduling. In this article, OLE for process control technology, software defined industrial network, and device-To-device communication technology are proposed to achieve efficient dynamic resource interaction. Additionally, the integration of ontology modeling with multiagent technology is introduced to achieve dynamic management of resources. We propose a load balancing mechanism based on Jena reasoning and Contract-Net Protocol technology that focuses on intelligent equipment in the smart factory. Dynamic resources management for IoT-based manufacturing provides a solution for complex resource allocation problems in current manufacturing scenarios, and provides a technical reference point for the implementation of intelligent manufacturing in Industry 4.0. © 1979-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran" is provided in this record**

A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence

- Tang, Hao, Li, Di, Wan, Jiafu, Imran, Muhammad, Shoaib, Muhammad

Authors: Tang, Hao , Li, Di , Wan, Jiafu , Imran, Muhammad , Shoaib, Muhammad
Date: 2020
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4248-4259
Full Text: false
Reviewed:
Description: The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness. © 2014 IEEE.

Improving cognitive ability of edge intelligent IIoT through machine learning

- Chen, Baotong, Wan, Jiafu, Lan, Yanting, Imran, Muhammad, Li, Di, Guizani, Nadra

Authors: Chen, Baotong , Wan, Jiafu , Lan, Yanting , Imran, Muhammad , Li, Di , Guizani, Nadra
Date: 2019
Type: Text , Journal article
Relation: IEEE Network Vol. 33, no. 5 (2019), p. 61-67
Full Text: false
Reviewed:
Description: 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.

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