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IIoT-Enhancing the Industrial World and Business Processes
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IIoT-Enhancing the Industrial World and Business Processes

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 21643

Special Issue Editor

Prof. Dr. Stefan Schönig

E-Mail Website
Guest Editor
Institute for Management Information Systems, University of Regensburg, Universitätsstraße 31, 93053 Regensburg, Germany.
Interests: business process management, Internet of Things, process mining

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a network that connects uniquely identifiable things to the internet. Through the exploitation of unique identification and sensing, information about the thing can be collected and the state can be changed from anywhere, anytime, by anything. In recent decades, the IoT has seen a proliferation into all aspects of everyday life. Especially for industrial organizations, the so-called Industrial Internet of Things (IIoT) is progressively used for efficient management and controlling of industrial processes and assets to increase productivity and reduce operational costs. IIoT is therefore a powerful paradigm to enhance the industrial world and to improve all kinds of business processes along the industrial value chain. Research on novel IIoT technology can enable the realization of new applications that have disruptive influence on industrial organizations. In addition, it is also necessary to analyze existing applications and fundamental IoT capabilities to achieve an optimization and further diffusion of existing technologies. This is highly relevant, as past research has already enabled a great variety of different IIoT applications, and a systematic review can even provide further insights and perceptions. We invite authors to submit articles that deal with novel IIoT applications or existing applications, IIoT capabilities, value propositions, and their influence on industrial and business processes along the value chain.

Prof. Dr. Stefan Schönig
Guest Editor

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
  • Internet of Things
  • Industry 4.0
  • business processes
  • process improvement
  • industrial value chain

Published Papers (11 papers)

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Research

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29 pages, 7984 KiB  
Article
Architecting an Open-Source IIoT Framework for Real-Time Control and Monitoring in the Bioleaching Industry
by Marta I. Tarrés-Puertas, Lluís Brosa, Albert Comerma, Josep M. Rossell and Antonio D. Dorado
Appl. Sci. 2024, 14(1), 350; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010350 - 29 Dec 2023
Cited by 1 | Viewed by 910
Abstract
Electronic waste (e-waste) contains toxic elements causing an important impact on environmental and human health. However, the presence of valuable metals, such as copper or gold, among others, make recycling a necessity for obtaining an alternative source of raw materials. Conventional metal recovery [...] Read more.
Electronic waste (e-waste) contains toxic elements causing an important impact on environmental and human health. However, the presence of valuable metals, such as copper or gold, among others, make recycling a necessity for obtaining an alternative source of raw materials. Conventional metal recovery methods are environmentally unsound, prompting the exploration of greener alternatives like bioleaching, which utilizes the activity of microorganisms for a more sustainable recovery. However, the mechanisms involved in the process and the conditions to optimize the metabolic paths are still not completely known. Monitorization and automatization of the different stages composing the global process are crucial for advancing in the implementation of this novel technology at an industrial scale. For the first time, an open-source industrial IoT system is designed to enhance and regulate bioleaching by implementing real-time monitoring and control within the plant’s infrastructure. This system includes an Android app that displays real-time plant data from sensors and a robust server featuring a flexible application programming interface (API) for future applications. The app caters to specific needs such as remote sensor reading, actuator control, and real-time bioleaching alerts, ensuring secure access and proactive event management. By utilizing collected data, it minimizes downtime, equipment failures, and supply chain disruptions. The server maintains seamless communication with the plant controller, enabling efficient pump activation and sensor data transmission. A telegram bot demonstrates the API’s flexibility by forwarding plant alerts to users. During validation with concurrent remote user access, the application demonstrated its ability to prevent irreversible plant failures through an advanced alarm system. Ultimately, this IIoT system amplifies plant performance, safety, and efficiency by optimizing processes and decision-making capabilities. It emerges as a pivotal open-source tool, securing remote oversight and management of large-scale bioleaching plants, promising adaptability for future enhancements. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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27 pages, 1011 KiB  
Article
A Combinatory Framework for Link Prediction in Complex Networks
by Paraskevas Dimitriou and Vasileios Karyotis
Appl. Sci. 2023, 13(17), 9685; https://0-doi-org.brum.beds.ac.uk/10.3390/app13179685 - 27 Aug 2023
Cited by 1 | Viewed by 1512
Abstract
Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms can be [...] Read more.
Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms can be applied, being less or more effective in the relevant scenarios. In this work, we develop a novel framework that attempts to compose the best features of link prediction algorithms when applied to a network, in order to have even more reliable predictions, especially in topologies emerging in Industrial Internet of Things (IIoT) environments. According to the proposed framework, we first apply appropriate link prediction algorithms that we have chosen for an analyzed network (basic algorithms). Each basic algorithm gives us a numerical estimate for each missing edge in the network. We store the results of each basic algorithm in appropriate structures. Then we provide them as input to a developed genetic algorithm. The genetic algorithm evaluates the results of the basic algorithms for each missing edge of the network. At each missing edge of the network and from generation to generation, it composes the estimates of the basic algorithms regarding each edge and produces a new optimized estimate. This optimization results in a vector of weights where each weight corresponds to the effectiveness of the prediction for each of the basic algorithms we have employed. With these weights, we build a new enhanced predictor tool, which can obtain new optimized estimates for each missing edge in the network. The enhanced predictor tool applies to each missing edge the basic algorithms, normalizes the basic algorithms’ estimates, and, using the weights of the estimates derived from the genetic algorithm, returns a new estimate of whether or not an edge will be added in the future. According to the results of our experiments on several types of networks with five well-known link prediction algorithms, we show that the new enhanced predictor tool yields in every case better predictions than each individual algorithm, therefore providing an accuracy-targeting alternative in the existing state of the art. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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15 pages, 964 KiB  
Article
LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant
by Chiara Toffanin, Federico Di Palma, Francesca Iacono and Lalo Magni
Appl. Sci. 2023, 13(13), 7461; https://0-doi-org.brum.beds.ac.uk/10.3390/app13137461 - 24 Jun 2023
Cited by 1 | Viewed by 766
Abstract
The activated sludge process is a well-known method used to treat municipal and industrial wastewater. In this complex process, the oxygen concentration in the reactors plays a key role in the plant efficiency. This paper proposes the use of a Long Short-Term Memory [...] Read more.
The activated sludge process is a well-known method used to treat municipal and industrial wastewater. In this complex process, the oxygen concentration in the reactors plays a key role in the plant efficiency. This paper proposes the use of a Long Short-Term Memory (LSTM) network to identify an input–output model suitable for the design of an oxygen concentration controller. The model is identified from easily accessible measures collected from a real plant. This dataset covers almost a month of data collected from the plant. The performances achieved with the proposed LSTM model are compared with those obtained with a standard AutoRegressive model with eXogenous input (ARX). Both models capture the oscillation frequencies and the overall behavior (ARX Pearson correlation coefficient ρ = 0.833 , LSTM ρ = 0.921), but, while the ARX model fails to reach the correct amplitude (index of fitting FIT = 41.20%), the LSTM presents satisfactory performance (FIT = 60.56%). Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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18 pages, 1192 KiB  
Article
Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm
by Izabela Rojek, Piotr Prokopowicz, Piotr Kotlarz and Dariusz Mikołajewski
Appl. Sci. 2023, 13(11), 6396; https://0-doi-org.brum.beds.ac.uk/10.3390/app13116396 - 24 May 2023
Viewed by 812
Abstract
Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm. This applies not only to newly built solutions but also to the digitalization, automation, and robotization of existing factories and production or repair lines. In particular, [...] Read more.
Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm. This applies not only to newly built solutions but also to the digitalization, automation, and robotization of existing factories and production or repair lines. In particular, technologists’ extensive experience and know-how are necessary to design correct technological processes to minimize losses during production and product costs. That is why the proper selection of tools, machine tools, and production parameters during the manufacturing process is so important. Properly developed technology affects the entire production process. This paper presents an attempt to develop a post-hoc model of already existing manufacturing processes with the increased requirements and expectations resulting from the introduction of the Industry 4.0 paradigm. In particular, we relied on fuzzy logic to support the description of uncertainties, incomplete data, and discontinuities in the manufacturing process. This translates into better controls compared to conventional systems. An analysis of the proposed solution’s limitations and proposals for further development constitute the novelty and contribution of the article. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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17 pages, 3451 KiB  
Article
Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
by Zehua Liu, Xuefeng Ding, Jun Tang, Yuming Jiang and Dasha Hu
Appl. Sci. 2022, 12(24), 12917; https://0-doi-org.brum.beds.ac.uk/10.3390/app122412917 - 15 Dec 2022
Cited by 1 | Viewed by 1134
Abstract
At present, many manufacturing enterprises have business systems such as MES, SPC, etc. In the manufacturing process, a large amount of data with periodic time series will be generated. How to evaluate the timeliness of periodically generated data according to a large number [...] Read more.
At present, many manufacturing enterprises have business systems such as MES, SPC, etc. In the manufacturing process, a large amount of data with periodic time series will be generated. How to evaluate the timeliness of periodically generated data according to a large number of time series is important content in the field of data quality research. At the same time, it can solve the demand of abnormal monitoring of production process faced by manufacturing enterprises based on the regularity change for periodic data timeliness. Most of the existing data timeliness evaluation models are based on a single fixed time stamp, which is not suitable for effective evaluation of periodic data with time series. In addition, the existing data timeliness evaluation methods cannot be applied to the field of process anomaly monitoring. In this paper, the Anomaly Monitoring of Process based on Recurrent Timeliness Rules (AMP-RTR) is proposed to meet the needs of periodic data timeliness evaluation and production process anomaly monitoring. RTR is the Rules defined to evaluate the timeliness of periodically generated data. AMP is to infer the abnormality of the product production process through the abnormality of the regularity change for periodic data timeliness based on RTR. The AMP-RTR model evaluates the timeliness of data in each cycle according to the time series generated periodically. At the same time, after the updated data arrives, the initial timeliness score of the next cycle is calculated. There are two cases in which the evaluation value of timeliness is abnormal. The first case is that the timeliness score value is less than the lower limit after updating. The second case is that the number of times the timeliness score exceeds the upper limit meets the set threshold. The user can dynamically adjust the production process according to the abnormal warning of the model. Finally, in order to verify the applicability of the AMP-RTR, we conducted simulation experiments on synthetic datasets and semiconductor manufacturing datasets. The experimental results show that the AMP-RTR can effectively monitor the abnormal conditions of various production processes in the manufacturing industry by adjusting the parameters of the model. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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15 pages, 631 KiB  
Article
The Evaluation of Creditworthiness of Trade and Enterprises of Service Using the Method Based on Fuzzy Logic
by Ulzhan Makhazhanova, Seyit Kerimkhulle, Ayagoz Mukhanova, Aigulim Bayegizova, Zhankeldi Aitkozha, Ainur Mukhiyadin, Bolat Tassuov, Ainur Saliyeva, Roman Taberkhan and Gulmira Azieva
Appl. Sci. 2022, 12(22), 11515; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211515 - 13 Nov 2022
Cited by 14 | Viewed by 1750
Abstract
This article considered the problem of determining the creditworthiness of an enterprise operating in the field of trade and services. The assessment of the creditworthiness of borrowers, particularly small businesses, needs to be more careful: the level of development of small enterprises and [...] Read more.
This article considered the problem of determining the creditworthiness of an enterprise operating in the field of trade and services. The assessment of the creditworthiness of borrowers, particularly small businesses, needs to be more careful: the level of development of small enterprises and their specific activities must be considered, as well as the uncertainty in obtaining any financial result. A method for assessing the creditworthiness of enterprises (trade and services) is proposed, based on the use of the mathematical apparatus of the theory of fuzzy sets. This article analyzes the indicators of industry and regional specifics, indicators of the activity of a small enterprise, and financial and economic indicators typical for the service sector and trade. The rules on the basis of which decisions are made are formed in the form of logical formulas containing parameters. In its most general form, one parameter is predicted, called the creditworthiness index, which varies from 0 to 1 and has a natural interpretation. On the basis of the proposed method, examples of calculating the assessment of the creditworthiness of enterprises operating in the field of trade and services are given. The proposed scientific approach can be used as a basis for creating expert decision support systems for lending to small businesses. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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24 pages, 3469 KiB  
Article
Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database
by Jaciel David Hernandez-Resendiz, Edgar Tello-Leal, Ulises Manuel Ramirez-Alcocer and Bárbara A. Macías-Hernández
Appl. Sci. 2022, 12(21), 10832; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110832 - 26 Oct 2022
Cited by 1 | Viewed by 2065
Abstract
Process mining is a novel alternative that uses event logs to discover, monitor, and improve real business processes through knowledge extraction. Event logs are a prerequisite for any process mining technique. The extraction of event data and event log building is a complex [...] Read more.
Process mining is a novel alternative that uses event logs to discover, monitor, and improve real business processes through knowledge extraction. Event logs are a prerequisite for any process mining technique. The extraction of event data and event log building is a complex and time-intensive process, with human participation at several stages of the procedure. In this paper, we propose a framework to semi-automatically build an event log based on the XES standard from relational databases. The framework comprises the stages of requirements identification, event log construction, and event log evaluation. In the first stage, the data is interpreted to identify the relationship between the columns and business process activities, then the business process entities are defined. In the second stage, the hierarchical structure of the event log is specified. Likewise, a formal rule set is defined to allow mapping the database columns with the attributes specified in the event log structure, enabling the extraction of attributes. This task is implemented through a correlation method at the case, event, and activity levels, to automatic event log generation. We validate the event log through quality metrics, statistical analysis, and business process discovery. The former allows for determining the quality of the event log built using the metrics of accuracy, completeness, consistency, and uniqueness. The latter evaluates the business process models discovered through precision, coverage, and generalization metrics. The proposed approach was evaluated using the autonomous Internet of Things (IoT) air quality monitoring system’s database and the patient admission and healthcare service delivery database, reaching acceptable values both in the event log quality and in the quality of the business process models discovered. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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22 pages, 7305 KiB  
Article
Improving Delivery Performance in High-Mix Low-Volume Manufacturing by Model-Based and Data-Driven Methods
by István Gödri
Appl. Sci. 2022, 12(11), 5618; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115618 - 01 Jun 2022
Cited by 3 | Viewed by 3053
Abstract
In a high-mix and low-volume (HMLV) manufacturing environment where demand fluctuation is the rule rather than the exception, daily production management in face of conflicting key performance indicators such as high delivery precision, short lead time, and maximal resource utilization is a most [...] Read more.
In a high-mix and low-volume (HMLV) manufacturing environment where demand fluctuation is the rule rather than the exception, daily production management in face of conflicting key performance indicators such as high delivery precision, short lead time, and maximal resource utilization is a most challenging task. This situation may even be hampered by unreliable supplier performance. This paper presents a generic decision support workflow, which first identifies the most critical external and internal factors which have a serious impact on delivery performance. Next, it suggests a method which combines traditional manufacturing system simulation with advanced machine learning techniques to support the improved daily routine lot-sizing and production scheduling activities in a HMLV company. Argumentation is motivated and illustrated by a detailed industrial case study. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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16 pages, 6157 KiB  
Article
Human Vital Signs Detection: A Concurrent Detection Approach
by Tjahjo Adiprabowo, Ding-Bing Lin, Tse-Hsuan Wang, Ariana Tulus Purnomo and Aloysius Adya Pramudita
Appl. Sci. 2022, 12(3), 1077; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031077 - 20 Jan 2022
Cited by 2 | Viewed by 2983
Abstract
The measurement of heartbeat rate and breathing rate for patients with sensitive skin, such as skin with burns, is very difficult to do, especially if the number of patients is large and medical personnel is limited. Therefore, this study seeks to propose a [...] Read more.
The measurement of heartbeat rate and breathing rate for patients with sensitive skin, such as skin with burns, is very difficult to do, especially if the number of patients is large and medical personnel is limited. Therefore, this study seeks to propose a preliminary solution to this problem by proposing a device that can measure the vital signs of several people concurrently, especially the heartbeat rate and breathing rate, without attaching sensors to their skin. This is done using an FMCW (frequency-modulated continuous wave) radar that operates at 77–81 GHz. FMCW radar emits electromagnetic waves towards the chest of several targets and picks up the reflected waves. Then, using signal processing of these reflected waves, each target’s heartbeat rate and breathing rate can be obtained. Our experiment managed to perform concurrent detection for four targets. The experimental results are between 52 and 82 beats per minute for the heartbeat rates and between 10 and 35 breaths per minute for the breathing rates of four targets. These results are in accordance with normal heartbeat rate and normal breathing rate; thus, our research succeeded in proposing a preliminary solution to this problem. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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Review

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26 pages, 2389 KiB  
Review
Advanced Electronic and Optoelectronic Sensors, Applications, Modelling and Industry 5.0 Perspectives
by Alessandro Massaro
Appl. Sci. 2023, 13(7), 4582; https://0-doi-org.brum.beds.ac.uk/10.3390/app13074582 - 04 Apr 2023
Cited by 8 | Viewed by 2506
Abstract
This review will focus on advances in electronic and optoelectronic technologies by through the analysis of a full research and industrial application scenario. Starting with the analysis of nanocomposite sensors, and electronic/optoelectronic/mechatronic systems, the review describes in detail the principles and the models [...] Read more.
This review will focus on advances in electronic and optoelectronic technologies by through the analysis of a full research and industrial application scenario. Starting with the analysis of nanocomposite sensors, and electronic/optoelectronic/mechatronic systems, the review describes in detail the principles and the models for finding possible implementations of Industry 5.0 applications. The study then addresses production processes and advanced detection systems integrating Artificial Intelligence (AI) algorithms. Specifically, the review introduces new research topics in Industry 5.0 about AI self-adaptive systems and processes in electronics, robotics and production management. The paper proposes also new Business Process Modelling and Notation (BPMN) Process Mining (PM) workflows, and a simulation of a complex Industry 5.0 manufacturing framework. The performed simulation estimates the diffusion heat parameters of a hypothesized production-line layout, describing the information flux of the whole framework. The simulation enhances the technological key elements, enabling an industrial upscale in the next digital revolution. The discussed models are usable in management engineering and informatics engineering, as they merge the perspectives of advanced sensors with Industry 5.0 requirements. The goal of the paper is to provide concepts, research topics and elements to design advanced production network in manufacturing industry. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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Other

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21 pages, 1180 KiB  
Hypothesis
Internet of Things Adoption in the Manufacturing Sector: A Conceptual Model from a Multi-Theoretical Perspective
by Sehnaz Ahmetoglu, Zaihisma Che Cob and Nor’Ashikin Ali
Appl. Sci. 2023, 13(6), 3856; https://0-doi-org.brum.beds.ac.uk/10.3390/app13063856 - 17 Mar 2023
Cited by 3 | Viewed by 2982
Abstract
The manufacturing sector (MS) is considered one of the most important national economic sectors; therefore, global manufacturers strive to apply cutting-edge technologies to gain competitive advantages. The Internet of Things (IoT) has an inherent potential to enhance MS economic growth and maintain its [...] Read more.
The manufacturing sector (MS) is considered one of the most important national economic sectors; therefore, global manufacturers strive to apply cutting-edge technologies to gain competitive advantages. The Internet of Things (IoT) has an inherent potential to enhance MS economic growth and maintain its dominance in global markets by using a vast network of smart sensors; nevertheless, IoT technology adoption in the MS remains in the early phase. This research aims to define the antecedents that affect IoT adoption in the MS and propose a conceptual model to explain the adoption intention. Based on an extensive literature review, the proposed model was constructed by three main antecedents: perceived value, perceived benefits, and perceived challenges, and 11 related variables. The model development used a multi-theoretical perspective by integrating three theories: the value-based adoption model, the diffusion of innovation theory, and the technology–organization–environment framework. This study provides decision-makers with valuable insight that promotes IoT adoption in MS and enriches the literature with a new perspective that encourages more studies on IoT adoption in organizations. Full article
(This article belongs to the Special Issue IIoT-Enhancing the Industrial World and Business Processes)
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