List of Titles

A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing

- Li, Xiaomin, Wan, Jiafu, Dai, Hong-Ning, Imran, Muhammad, Xia, Min, Celesti, Antonio

Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
Date: 2019
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
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Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.

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
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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.

Adaptive transmission optimization in SDN-based industrial internet of things with edge computing

- Li, Xiaomin, Li, Di, Wan, Jiafu, Liu, Chengliang, Imran, Muhammad

Authors: Li, Xiaomin , Li, Di , Wan, Jiafu , Liu, Chengliang , Imran, Muhammad
Date: 2018
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 5, no. 3 (2018), p. 1351-1360
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Description: In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission. © 2018 IEEE.

Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

- Dai, Hong-Ning, Wang, Hao, Xu, Guangquan, Wan, Jiafu, Imran, Muhammad

Authors: Dai, Hong-Ning , Wang, Hao , Xu, Guangquan , Wan, Jiafu , Imran, Muhammad
Date: 2020
Type: Text , Journal article
Relation: Enterprise Information Systems Vol. 14, no. 9-10 (2020), p. 1279-1303
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Description: Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Cloud-based smart manufacturing for personalized candy packing application

- Wang, Shiyong, Wan, Jiafu, Imran, Muhammad, Li, Di, Zhang, Chunhua

Authors: Wang, Shiyong , Wan, Jiafu , Imran, Muhammad , Li, Di , Zhang, Chunhua
Date: 2018
Type: Text , Journal article
Relation: Journal of Supercomputing Vol. 74, no. 9 (2018), p. 4339-4357
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Description: Industry 4.0 has been proposed to address personalized consumption demands by building cyber-physical production systems for smart manufacturing. Although cloud manufacturing and some integrated frameworks for smart factory have been presented in literatures, it still lacks industrial applications. In this paper, we use personalized candy packing application as a demonstration to illustrate our smart factory design. We first describe the component layers of the smart factory, i.e., physical devices, private cloud, client terminals, and network, to enable the smart factory to be integrated with other systems, such as banks and logistical network, to cope with personalized consumption demands. Then, we present a scheme for inter-layered interaction. As for the physical devices, we also design an intra-layered negotiation mechanism to implement dynamic reconfiguration, so that the system can support hybrid production of multi-typed products. Finally, we give experimental results to verify efficiency, self-organized process, and hybrid production paradigm of the proposed system. © 2016, Springer Science+Business Media New York.

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
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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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Mobile crowd sensing for traffic prediction in internet of vehicles

- Wan, Jiafu, Liu, Jianqi, Shao, Zehui, Vasilakos, Athanasios, Imran, Muhammad, Zhou, Keliang

Authors: Wan, Jiafu , Liu, Jianqi , Shao, Zehui , Vasilakos, Athanasios , Imran, Muhammad , Zhou, Keliang
Date: 2016
Type: Text , Journal article
Relation: Sensors (Switzerland) Vol. 16, no. 1 (2016), p.
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Description: The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction. © 2016 by the authors, licensee MDPI, Basel, Switzerland.

Reconfigurable smart factory for drug packing in healthcare industry 4.0

- Wan, Jiafu, Tang, Shenglong, Li, Di, Imran, Muhammad, Zhang, Chunhua

Authors: Wan, Jiafu , Tang, Shenglong , Li, Di , Imran, Muhammad , Zhang, Chunhua
Date: 2019
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 1 (2019), p. 507-516
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Description: Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method. © 2005-2012 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**

Security in software-defined networking : threats and countermeasures

- Shu, Zhaogang, Wan, Jiafu, Li, Di, Lin, Jiaxiang, Vasilakos, Athanasios, Imran, Muhammad

Authors: Shu, Zhaogang , Wan, Jiafu , Li, Di , Lin, Jiaxiang , Vasilakos, Athanasios , Imran, Muhammad
Date: 2016
Type: Text , Journal article
Relation: Mobile Networks and Applications Vol. 21, no. 5 (2016), p. 764-776
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Description: In recent years, Software-Defined Networking (SDN) has been a focus of research. As a promising network architecture, SDN will possibly replace traditional networking, as it brings promising opportunities for network management in terms of simplicity, programmability, and elasticity. While many efforts are currently being made to standardize this emerging paradigm, careful attention needs to be also paid to security at this early design stage. This paper focuses on the security aspects of SDN. We begin by discussing characteristics and standards of SDN. On the basis of these, we discuss the security features as a whole and then analyze the security threats and countermeasures in detail from three aspects, based on which part of the SDN paradigm they target, i.e., the data forwarding layer, the control layer and the application layer. Countermeasure techniques that could be used to prevent, mitigate, or recover from some of such attacks are also described, while the threats encountered when developing these defensive mechanisms are highlighted. © 2016, Springer Science+Business Media New York.

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
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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**

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