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Future Internet
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Collaborative and Intelligent Networks and Decision Systems and Services for...
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Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management II

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7015

Special Issue Editors

Dr. Leonilde Varela

E-Mail Website
Guest Editor
Department of Production and Systems, School of Engineering, University of Minho, 4804-533 Guimarães, Portugal
Interests: manufacturing management; collaborative networks and platforms; decision-support models and systems; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Goran D. Putnik

E-Mail Website
Guest Editor
Department of Production and Systems, School of Engineering, University of Minho, 4804-533 Guimarães, Portugal
Interests: integrated; distributed; agile; and virtual manufacturing systems and enterprises; cloud and ubiquitous manufacturing; learning organizations; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Collaborative networks and systems (CNS) have received much attention in recent decades, in order to reach competitive advantage in their application domain. Many contributions have arisen from the industrial context to service-oriented companies, for instance, in the scope of artificial intelligence. Therefore, many contributions have been put forward related to collaborative and intelligent networks and systems.

In spite of the wide range of existing work in this area, however, it continues to be imperative for companies to understand and anticipate the importance of CNS in manufacturing to enable them to reach a competitive advantage in the current global market and Industry 4.0-oriented scenario.

These main topics strengthen the specific characteristics of CN through collaboration to deliver products and services; the decentralization of decision-making; and inter- and intra-organizational integration to meet imposed performance requirements in competitive global markets.

Moreover, in the context of CNS, normalization is a crucial step in all decision models, to produce comparable and dimension less data from heterogeneous data. Therefore, it is of upmost importance to use appropriate data normalization techniques for each application scenario, for instance, according to the kind of multicriteria or multiobjective optimization methods or algorithms used for networked supply and operations management. This is even more important in the upcoming increasingly digital era of the I4.0, along with the perceived need for big data processing, regarding the need for vertical and horizontal integration of data and manufacturing processes.

This Special Issue intends to provide a contribution to the domain of collaborative and intelligent networks and systems for supporting engineering and production management to fill the gap in theories and practical applications for supporting industrial companies through suitable methods and solutions applicable to a wide range of instances. Therefore, this Special Issue aims to bring together researchers from a wide range of disciplines to provide potential contributions to the main topics underlying this proposal, although not limited to the following:

  • Collaboration strategies;
  • Learning organizations;
  • Chaos and complexity management;
  • Game theory models and aproaches for supporting production management;
  • Blockchain tecnology applied to manufacturing and management;
  • Intelligent models, methods, and tools;
  • Dynamic and real-time based decision-support approaches;
  • Decentralized and distributed decision-support networks and models;
  • Social network based models, methods and tools;
  • Hybrid intelligent decision-support and recommendation systems;
  • Multiagents models and platforms;
  • Machine and deep learning based approaches and systems;
  • Bio-inspired models and algorithms applications;
  • Negotiation and group decision-making approaches;
  • Multicriteria and multiobjective models;
  • Uncertainty treatment;
  • Data normalization and data fusion methods and techniques;
  • Data analytics for manufacturing systems and processes;
  • Cloud computing, manufacturing and big data processing;
  • Learning and data mining, and other data science oriented approaches;
  • Data visualization for the digital factory;
  • Real time machine and process monitoring, diagnostics, and prognostics;
  • Real-time management;
  • Manufacturing execution systems;
  • Open source software applications for digital or cyber manufacturing;
  • Internet of Things for cyber manufacturing and management.

Dr. Leonilde Varela
Dr. Goran D. Putnik
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • collaborative and intelligent manufacturing and management models, systems and networks
  • Industry 4.0
  • cyberphysical systems
  • chaos and complexity management
  • real-time-based decision making

Published Papers (3 papers)

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Research

14 pages, 1566 KiB  
Article
Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling
by Mansoor Iqbal, Zahid Ullah, Izaz Ahmad Khan, Sheraz Aslam, Haris Shaheer, Mujtaba Humayon, Muhammad Asjad Salahuddin and Adeel Mehmood
Future Internet 2023, 15(3), 104; https://doi.org/10.3390/fi15030104 - 08 Mar 2023
Cited by 2 | Viewed by 2786
Abstract
Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts [...] Read more.
Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field. Full article
(This article belongs to the Special Issue Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management II)
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16 pages, 2777 KiB  
Article
Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags
by Ganjar Alfian, Muhammad Syafrudin, Norma Latif Fitriyani, Sahirul Alam, Dinar Nugroho Pratomo, Lukman Subekti, Muhammad Qois Huzyan Octava, Ninis Dyah Yulianingsih, Fransiskus Tatas Dwi Atmaji and Filip Benes
Future Internet 2023, 15(3), 103; https://doi.org/10.3390/fi15030103 - 08 Mar 2023
Cited by 4 | Viewed by 2132
Abstract
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction [...] Read more.
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically. Full article
(This article belongs to the Special Issue Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management II)
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22 pages, 822 KiB  
Article
Graph-Based Taxonomic Semantic Class Labeling
by Tajana Ban Kirigin, Sanda Bujačić Babić and Benedikt Perak
Future Internet 2022, 14(12), 383; https://doi.org/10.3390/fi14120383 - 19 Dec 2022
Viewed by 1684
Abstract
We present a graph-based method for the lexical task of labeling senses of polysemous lexemes. The labeling task aims at generalizing sense features of a lexical item in a corpus using more abstract concepts. In this method, a coordination dependency-based lexical graph is [...] Read more.
We present a graph-based method for the lexical task of labeling senses of polysemous lexemes. The labeling task aims at generalizing sense features of a lexical item in a corpus using more abstract concepts. In this method, a coordination dependency-based lexical graph is first constructed with clusters of conceptually associated lexemes representing related senses and conceptual domains of a source lexeme. The label abstraction is based on the syntactic patterns of the x is_a y dependency relation. For each sense cluster, an additional lexical graph is constructed by extracting label candidates from a corpus and selecting the most prominent is_a collocates in the constructed label graph. The obtained label lexemes represent the sense abstraction of the cluster of conceptually associated lexemes. In a similar graph-based procedure, the semantic class representation is validated by constructing a WordNet hypernym relation graph. These additional labels indicate the most appropriate hypernym category of a lexical sense community. The proposed labeling method extracts hierarchically abstract conceptual content and the sense semantic features of the polysemous source lexeme, which can facilitate lexical understanding and build corpus-based taxonomies. Full article
(This article belongs to the Special Issue Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management II)
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