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
Full Text: false
Reviewed:
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 novel real-time deterministic scheduling mechanism in industrial cyber-physical systems for energy internet

- Peng, Yuhuai, Jolfaei, Alireza, Yu, Keping

Authors: Peng, Yuhuai , Jolfaei, Alireza , Yu, Keping
Date: 2022
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5670-5680
Full Text: false
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Description: As an effective distributed renewable energy utilization paradigm, a microgrid is expected to realize the high integration of the industrial cyber-physical systems (CPS), which has attracted extensive attention from academia and industry. However, the real-time interaction and feedback loop between physical systems and cyber systems have posed severe challenges to the reliability, determinacy, and energy efficiency of the multiway flow of information and communication transmission. In order to solve the problem of slot scheduling and data transmission (SSDT) in the microgrid, a novel real-time deterministic scheduling (RTDS) scheme for industrial CPS is proposed in this article. First, the SSDT is formulated as a multiway flow scheduling problem, and it is theoretically proved that the SSDT problem is NP-hard. Then, the RTDS scheme designs two heuristic algorithms: scheduling request preprocessing and greedy-based multichannel time slot allocation for an optimal scheduling solution. Practical experimental results demonstrate that the proposed RTDS scheme has significant advantages in packet loss rate, deadline guarantee rate, and energy consumption compared with the traditional schemes, and thus, is more suitable for deployment in microgrid systems. © 2005-2012 IEEE.

A blockchain-based solution for enhancing security and privacy in smart factory

- Wan, Jafu, Li, Jiapeng, Imran, Muhammad, Li, Di

Authors: Wan, Jafu , Li, Jiapeng , Imran, Muhammad , Li, Di
Date: 2019
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 6 (2019), p. 3652-3660
Full Text: false
Reviewed:
Description: Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development. © 2005-2012 IEEE.

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A shared bus profiling scheme for smart cities based on heterogeneous mobile crowdsourced data

- Kong, Xiangjie, Xia, Feng, Li, Jianxin, Hou, Mingliang, Li, Menglin, Xiang, Yong

Authors: Kong, Xiangjie , Xia, Feng , Li, Jianxin , Hou, Mingliang , Li, Menglin , Xiang, Yong
Date: 2020
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 2 (2020), p. 1436-1444
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Description: Mobile crowdsourcing (MCS), as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data that can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining the advantages of convenience and economy. In this article, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the aforementioned data scarcity issue. Then, we propose a travel profiling to profile resident travel and design a method called multiconstraint evolution algorithm to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers' travel requirements. © 2005-2012 IEEE.

Lightweight searchable encryption protocol for industrial internet of things

- Zhang, Ke, Long, Jiahuan, Wang, Xiaofen, Dai, Hong-Ning, Liang, Kaitai, Imran, Muhammad

Authors: Zhang, Ke , Long, Jiahuan , Wang, Xiaofen , Dai, Hong-Ning , Liang, Kaitai , Imran, Muhammad
Date: 2021
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 6 (2021), p. 4248-4259
Full Text: false
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Description: Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes. © 2005-2012 IEEE.

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
Full Text: false
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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**

A distributed and anonymous data collection framework based on multilevel edge computing architecture

- Usman, Muhammad, Jan, Mian, Jolfaei, Alireza, Xu, Min, He, Xiangjian, Chen, Jinjun

Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza , Xu, Min , He, Xiangjian , Chen, Jinjun
Date: 2020
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6114-6123
Full Text: false
Reviewed:
Description: Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. © 2005-2012 IEEE.

Model compression for IoT applications in industry 4.0 via multiscale knowledge transfer

- Fu, Shipeng, Li, Zhen, Liu, Kai, Din, Sadia, Imran, Muhammad, Yang, Xiaomin

Authors: Fu, Shipeng , Li, Zhen , Liu, Kai , Din, Sadia , Imran, Muhammad , Yang, Xiaomin
Date: 2020
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6013-6022
Full Text: false
Reviewed:
Description: Recently, Industry 4.0 has attracted much attention. It has close relations with the Internet of Things (IoT). On the other hand, convolutional neural networks (CNNs) have shown promising performance in many foundational services of the IoT applications. For the IoT applications with high-speed data streams and the requirement of time-sensitive actions, fast processing is demanded on small-scale platforms or even on IoT devices themselves. Therefore, it is inappropriate to employ cumbersome CNNs in IoT applications, making the study of model compression necessary. In knowledge transfer, it is common to employ a deep, well-trained network, called teacher, to guide a shallow, untrained network, called student, to have better performance. Previous works have made many attempts to transfer single-scale knowledge from teacher to student, leading to degradation of generalization ability. In this article, we introduce multiscale representations to knowledge transfer, which facilitates the generalization ability of student. We divide student and teacher into several stages. Student learns from multiscale knowledge provided by teacher at the end of each stage. Extensive experiments demonstrate the effectiveness of our proposed method both on image classification and on single image super-resolution. The huge performance gap between student and teacher is significantly narrowed down by our proposed method, making student suitable for IoT applications. © 2005-2012 IEEE.

Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment

- Qi, Lianyong, Hu, Chunhua, Zhang, Xuyun, Khosravi, Mohammad, Pang, Shaoning

Authors: Qi, Lianyong , Hu, Chunhua , Zhang, Xuyun , Khosravi, Mohammad , Pang, Shaoning
Date: 2021
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 6 (2021), p. 4159-4167
Full Text: false
Reviewed:
Description: As one of the cyber-physical-social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial-temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users' context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones. © 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 “Shaoning Pang” is provided in this record**

A holistic power management strategy of microgrids based on model predictive control and particle swarm optimization

- Shan, Yinghao, Hu, Jiefeng, Liu, Huashan

Authors: Shan, Yinghao , Hu, Jiefeng , Liu, Huashan
Date: 2022
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5115-5126
Full Text: false
Reviewed:
Description: Power control and optimization are both crucial for the proper operation of a microgrid. However, in existing research, they are usually studied separately. Active and reactive powers are either maintained to constant values at device level or optimized at system level without considering frequency and voltage control of distributed converters. In this article, a holistic power control and optimization strategy is proposed for microgrids. Specifically, a model predictive control incorporated with the droop method is developed at device level to achieve load sharing and flexible power dispatching among distributed energy resources, which is feasible for both islanded and grid-connected modes. In addition, an evolutionary particle swarm optimization algorithm is designed at system level to generate the optimal active and reactive power setpoints, which are then sent to the device level for controlling inverters. The proposed power optimization scheme is able to mitigate voltage deviations and minimize the operational cost of the microgrid. Comprehensive case studies and real-time simulator test are provided to demonstrate the feasibility and efficacy of the proposed power control and optimization strategy. © 2005-2012 IEEE.

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