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Applied Sciences
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Real-Time Systems and Industrial Internet of Things
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Real-Time Systems and Industrial Internet of Things

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 9634

Special Issue Editors

Prof. Dr. Xi Jin

E-Mail Website
Guest Editor
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Interests: Internet of things; cyber-physical systems; real-time scheduling; industrial network
Dr. Changqing Xia

E-Mail Website
Guest Editor
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Interests: Internet of Things; real-time scheduling; edge computing
Dr. Chi Xu

E-Mail Website
Guest Editor
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Interests: cognitive radio networks; industrial wireless networks; 5G URLLC; tactile internet

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) connects a massive number physical objects to the Internet or other communication networks so that these objects can exchange data and so that more information can be used to assist decision making. For industrial applications, the IoT is an important technology to enhance manufacturing and industrial processes. This is the so-called Industrial Internet of Things (IIoT).

The IIoT must guarantee the real-time performance of industrial processes. If not, a product failure may occur. The real-time performance of the IIoT is affected by many technologies, such as data processing, communication, production control, and operations research.

This Special Issue focuses on state-of-the-art technologies that address the real-time problems of the IIoT. The topic is open, but special focus will be given to real-time data processing based on knowledge graphs, scheduling for deterministic networks, offloading in edge computing, and real-time location tracking and automatic navigation for mobile objects.

Prof. Dr. Xi Jin
Dr. Changqing Xia
Dr. Chi Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • industrial IoT
  • real-time systems
  • knowledge graph
  • scheduling
  • computation offloading
  • tracking
  • automatic navigation

Published Papers (6 papers)

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Research

16 pages, 5865 KiB  
Article
Research on a Real-Time Control System for Discrete Factories Based on Digital Twin Technology
by Shousong Jin, Fengyi Yu, Boyu Wang, Min Zhang and Yaliang Wang
Appl. Sci. 2024, 14(10), 4076; https://doi.org/10.3390/app14104076 - 10 May 2024
Viewed by 276
Abstract
Gear factories are most typical discrete manufacturing factories. Many gear factories are striving to explore how to achieve intelligent manufacturing in order to improve efficiency and reduce costs. Digital twin technology is currently one of the most reliable ways to achieve intelligent manufacturing. [...] Read more.
Gear factories are most typical discrete manufacturing factories. Many gear factories are striving to explore how to achieve intelligent manufacturing in order to improve efficiency and reduce costs. Digital twin technology is currently one of the most reliable ways to achieve intelligent manufacturing. This article aims to establish a real-time control system in order to promote intelligent manufacturing for discrete manufacturing factories. Firstly, a model for a digital twin gear factory is put forward based on the characteristics of gear factories, and the composition of a real-time control system for gear factories is clarified. Then, a human–computer interaction architecture for the real-time control system is proposed. The real-time control system consists of three parts as follows: a monitoring module, a virtualizing module, and a controlling module. At work, it appears as a kind of human–machine interaction form with the three following interfaces: a monitoring window, a virtualizing window, and a controlling window. Finally, a gear factory, which is specialized in manufacturing the intermediate shaft dual gear of a new energy vehicle gearbox, develops a set of software for the real-time control system. The prototype software is obtained through some development activities such as 3D MAX and WebGL virtualization modeling and OPC UA and REST communication design. Full article
(This article belongs to the Special Issue Real-Time Systems and Industrial Internet of Things)
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20 pages, 686 KiB  
Article
Global Resource Scheduling for Distributed Edge Computing
by Aiping Tan, Yunuo Li, Yan Wang and Yujie Yang
Appl. Sci. 2023, 13(22), 12490; https://doi.org/10.3390/app132212490 - 19 Nov 2023
Viewed by 787
Abstract
Recently, there has been a surge in interest surrounding the field of distributed edge computing resource scheduling. Notably, applications like intelligent traffic systems and Internet of Things (IoT) intelligent monitoring necessitate the effective scheduling and migration of distributed resources. In addressing this challenge, [...] Read more.
Recently, there has been a surge in interest surrounding the field of distributed edge computing resource scheduling. Notably, applications like intelligent traffic systems and Internet of Things (IoT) intelligent monitoring necessitate the effective scheduling and migration of distributed resources. In addressing this challenge, distributed resource scheduling must weigh the costs associated with resource scheduling, aiming to identify an optimal strategy amid various feasible solutions. Different application scenarios introduce diverse optimization objectives, including considerations such as cost, transmission delay, and energy consumption. While current research predominantly focuses on the optimization problem of local resource scheduling, there is a recognized need for increased attention to global resource scheduling. This paper contributes to the field by defining a global resource scheduling problem for distributed edge computing, demonstrating its NP-Hard nature through proof. To tackle this complex problem, the paper proposes a heuristic solution strategy based on the ant colony algorithm (ACO), with optimization of ACO parameters achieved through the use of particle swarm optimization (PSO). To assess the effectiveness of the proposed algorithm, an experimental comparative analysis is conducted. The results showcase the algorithm’s notable accuracy and efficient iteration cost performance, highlighting its potential applicability and benefits in the realm of distributed edge computing resource scheduling. Full article
(This article belongs to the Special Issue Real-Time Systems and Industrial Internet of Things)
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17 pages, 1895 KiB  
Article
Hybrid Traffic Scheduling in 5G and Time-Sensitive Networking Integrated Networks for Communications of Virtual Power Plants
by Junmin Wu, Chuan Liu, Jing Tao, Shidong Liu and Wei Gao
Appl. Sci. 2023, 13(13), 7953; https://doi.org/10.3390/app13137953 - 7 Jul 2023
Cited by 4 | Viewed by 1610
Abstract
The virtual power plant is one of the key technologies for the integration of various distributed energy resources into the power grid. To enable its smooth and reliable operation, the network infrastructure that connects the components for critical communications becomes a research challenge. [...] Read more.
The virtual power plant is one of the key technologies for the integration of various distributed energy resources into the power grid. To enable its smooth and reliable operation, the network infrastructure that connects the components for critical communications becomes a research challenge. Current communication networks based on the traditional Ethernet and long-term evolution cannot provide the required deterministic low latency or reliable communication services. This paper presents a three-layer virtual power plant communication architecture with 5G and time-sensitive networking integrated networks for both determinism and mobility. The service types and traffic requirements of the virtual power plant are analyzed and mapped between 5G and time-sensitive networking to guarantee their quality of service. This paper proposes a semi-persistent scheduling with reserved bandwidth sharing and a pre-emption mechanism for time-critical traffic to guarantee its bounded latency and reliability while improving the bandwidth utilization. The performance evaluation results show that the proposed mechanism effectively reduces the end-to-end delay for both time-triggered traffic and event-triggered traffic compared with the dynamic scheduling method. For event-triggered traffic, the proposed mechanism has comparable end-to-end delay performance to the static scheduling method. It largely improves the resource utilization compared to the static scheduling method when the network load becomes heavy. It achieves an optimum performance tradeoff between delay and resource utilization when the percentage of the reserved resource blocks is 30% in the simulation. Full article
(This article belongs to the Special Issue Real-Time Systems and Industrial Internet of Things)
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12 pages, 740 KiB  
Article
Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization
by Meng-Yue Zhang, Shi-Chun Yang, Xin-Jie Feng, Yu-Yi Chen, Jia-Yi Lu and Yao-Guang Cao
Appl. Sci. 2022, 12(22), 11817; https://doi.org/10.3390/app122211817 - 21 Nov 2022
Cited by 3 | Viewed by 2889
Abstract
Route planning for autonomous driving is a global road planning method based on a given starting point and target point combined with current traffic flow information. The optimal global route can reduce traffic jams and improve the safety and economy of autonomous vehicles. [...] Read more.
Route planning for autonomous driving is a global road planning method based on a given starting point and target point combined with current traffic flow information. The optimal global route can reduce traffic jams and improve the safety and economy of autonomous vehicles. The current optimization method of route planning for autonomous driving only considers a single objective or a chain of single objectives, which cannot meet the requirements of drivers. In this paper, we devise a general framework for the route planning method based on multi-objective optimization. Different from planning optimization based on not only traffic information, the framework considers travel time, distance, cost and personal preference, but focuses more on vehicle status and driver requirements. We use an improved depth-first search algorithm to find the optimal route. The evaluations of our method on real-world traffic data indicate the feasibility and applicability of the framework. Our study contributes to a better understanding of route planning and reveals that exploitation of personal preference can more flexibly configure the corresponding route according to the driver’s requirements. Full article
(This article belongs to the Special Issue Real-Time Systems and Industrial Internet of Things)
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13 pages, 2420 KiB  
Article
TBRm: A Time Representation Method for Industrial Knowledge Graph
by Keyan Cao and Chuang Zheng
Appl. Sci. 2022, 12(22), 11316; https://doi.org/10.3390/app122211316 - 8 Nov 2022
Viewed by 1544
Abstract
With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things [...] Read more.
With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technologies are mainly concentrated in the field of static knowledge graphs, which are composed of triples. In fact, many graphs also contain some dynamic information, such as time changes at points and time changes at edges; such knowledge graphs are called Temporal Knowledge Graphs (TKGs). We consider the temporal knowledge graph based on the projection and change of space. In order to combine the temporal information, we propose a new representation of the temporal knowledge graph, namely TBRm, which increases the temporal dimension of the translational distance model and utilizes relational predicates in time add representation in time dimension. We evaluate the proposed method on knowledge graph completion tasks using four benchmark datasets. Experiments demonstrate the effectiveness of TBRm representation in the temporal dimension. At the same time, it is also practiced on a network security data set of the Industrial Internet of Things. The practical results prove that the TBRm method can achieve good performance in terms of the degree of harm to IIoT network security. Full article
(This article belongs to the Special Issue Real-Time Systems and Industrial Internet of Things)
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18 pages, 940 KiB  
Article
Network Calculus Approach for Packet Delay Variation Analysis of Multi-Hop Wired Networks
by Rahul Nandkumar Gore, Elena Lisova, Johan Åkerberg and Mats Björkman
Appl. Sci. 2022, 12(21), 11207; https://doi.org/10.3390/app122111207 - 4 Nov 2022
Viewed by 1227
Abstract
The Industrial Internet of Things (IIoT) has revolutionized businesses by changing the way data are used to make products and services more efficient, reliable, and profitable. To achieve the improvement goals, the IIoT must guarantee the real-time performance of industrial applications such as [...] Read more.
The Industrial Internet of Things (IIoT) has revolutionized businesses by changing the way data are used to make products and services more efficient, reliable, and profitable. To achieve the improvement goals, the IIoT must guarantee the real-time performance of industrial applications such as motion control, by providing stringent quality of service (QoS) assurances for their (industrial applications) communication networks. An application or service may malfunction without adequate network QoS, resulting in potential product failures. Since an acceptable end-to-end delay and low jitter or packet delay variation (PDV) are closely related to quality of service (QoS), their impact is significant in ensuring the real-time performance of industrial applications. Although a communication network topology ensures certain jitter levels, its real-life performance is affected by dynamic traffic due to the changing number of devices, services, and applications present in the communication network. Hence, it is essential to study the jitter experienced by real-time traffic in the presence of background traffic and how it can be maintained within the limits to ensure a certain level of QoS. This paper presents a probabilistic network calculus approach that uses moment-generating functions to analyze the delay and PDV incurred by the traffic flows of interest in a wired packet switched multi-stage network. The presented work derives closed-form, end-to-end, probabilistic performance bounds for delay and PDV for several servers in series in the presence of background traffic. The PDV analysis conducted with the help of a Markovian traffic model for background traffic showed that the parameters from the background traffic significantly impact PDV and that PDV can be maintained under the limits by controlling the shape of the background traffic. For the studied configurations, the model parameters can change the PDV bound from 1 ms to 100 ms. The results indicated the possibility of using the model parameters as a shaper of the background traffic. Thus, the analysis can be beneficial in providing QoS assurances for real-time applications. Full article
(This article belongs to the Special Issue Real-Time Systems and Industrial Internet of Things)
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