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Future Internet
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Internet of Things and Cyber-Physical Systems II
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Internet of Things and Cyber-Physical Systems II

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 14527

Special Issue Editor

Dr. Iwona Grobelna

E-Mail Website
Guest Editor
Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, 65-516 Zielona Góra, Poland
Interests: control systems; formal verification; petri nets; model checking; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current technological progresses have urged the development of new methods relating to smart systems. The vision of using the Internet of Things (IoT) has accelerated the digital transformation of enterprises so that they are better prepared to deal with future challenges. Cyber-physical systems (CPSs) integrate sensing, computation, control, and networking into physical objects and infrastructure, connecting them to the Internet. A lot of different issues thus become significant, including software, hardware, and the link between them.

Following the success of the first edition of the Special Issue, we are now launching a second edition. This Special Issue is dedicated to interdisciplinary research in the area of the Internet of Things and cyber-physical systems. It calls for cutting-edge contributions to fundamental theoretical research, as well as its application in practice. This Special Issue covers, but is not limited to, the following topics:

  • Additive manufacturing and flexible manufacturing systems;
  • Artificial intelligence, big data, and Internet of Things;
  • The cyber-security of industrial systems;
  • Digital twins;
  • Edge computing;
  • Hardware solutions for Industry 4.0;
  • Smart systems;
  • The specification and verification of cyber-physical systems.

Dr. Iwona Grobelna
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. 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.

Published Papers (4 papers)

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Research

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29 pages, 743 KiB  
Article
TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
by Aristeidis Karras, Anastasios Giannaros, Christos Karras, Leonidas Theodorakopoulos, Constantinos S. Mammassis, George A. Krimpas and Spyros Sioutas
Future Internet 2024, 16(2), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/fi16020042 - 25 Jan 2024
Viewed by 1988
Abstract
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed [...] Read more.
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI. Full article
(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems II)
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18 pages, 1560 KiB  
Article
Performance Evaluation of a Lane Correction Module Stress Test: A Field Test of Tesla Model 3
by Jonathan Lancelot, Bhaskar P. Rimal and Edward M. Dennis
Future Internet 2023, 15(4), 138; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15040138 - 31 Mar 2023
Cited by 2 | Viewed by 2155
Abstract
This paper is designed to explicate and analyze data acquired from experimental field tests of a Tesla Model 3 lane correction module within the vehicle’s Autopilot Suite, a component of Tesla OS. The initial problem was discovered during a nominal drive of the [...] Read more.
This paper is designed to explicate and analyze data acquired from experimental field tests of a Tesla Model 3 lane correction module within the vehicle’s Autopilot Suite, a component of Tesla OS. The initial problem was discovered during a nominal drive of the Tesla Model 3, where after a random number of lane correction events, the lane correction module shuts down, issues a visual disable warning on the touchscreen, and control of the vehicle is given to the driver until the next drive. That development was considered problematic, as the driver can be caught off guard or may be medically disabled and unable to respond. During a controlled stress test, a more severe issue was discovered. After a random number of lane correction events, the lane correction module shuts down without warning, then stays activated after the test driver corrects the vehicle’s trajectory. This is considered a fatal error in the system and adds a dangerous element to an otherwise standard feature in a modern automotive vehicle. The results established that the number of events needed to trigger a fatal error without warning is unpredictable. Our results also demonstrate that the system is inconsistent. Full article
(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems II)
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24 pages, 999 KiB  
Article
A TOSCA-Based Conceptual Architecture to Support the Federation of Heterogeneous MSaaS Infrastructures
by Paolo Bocciarelli and Andrea D’Ambrogio
Future Internet 2023, 15(2), 48; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15020048 - 26 Jan 2023
Viewed by 1829
Abstract
Modeling and simulation (M&S) techniques are effectively used in many application domains to support various operational tasks ranging from system analyses to innovative training activities. Any (M&S) effort might strongly benefit from the adoption of service orientation and cloud computing to ease the [...] Read more.
Modeling and simulation (M&S) techniques are effectively used in many application domains to support various operational tasks ranging from system analyses to innovative training activities. Any (M&S) effort might strongly benefit from the adoption of service orientation and cloud computing to ease the development and provision of M&S applications. Such an emerging paradigm is commonly referred to as M&S-as-a-Service (MSaaS). The need for orchestrating M&S services provided by different partners in a heterogeneous cloud infrastructure introduces new challenges. In this respect, the adoption of an effective architectural approach might significantly help the design and development of MSaaS infrastructure implementations that cooperate in a federated environment. In this context, this work introduces a MSaaS reference architecture (RA) that aims to investigate innovative approaches to ease the building of inter-cloud MSaaS applications. Moreover, this work presents ArTIC-MS, a conceptual architecture that refines the proposed RA for introducing the TOSCA (topology and orchestration specification for cloud applications) standard. ArTIC-MS’s main objective is to enable effective portability and interoperability among M&S services provided by different partners in heterogeneous federations of cloud-based MSaaS infrastructure. To show the validity of the proposed architectural approach, the results of concrete experimentation are provided. Full article
(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems II)
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Review

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25 pages, 5686 KiB  
Review
The Future of the Human–Machine Interface (HMI) in Society 5.0
by Dimitris Mourtzis, John Angelopoulos and Nikos Panopoulos
Future Internet 2023, 15(5), 162; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15050162 - 27 Apr 2023
Cited by 11 | Viewed by 8019
Abstract
The blending of human and mechanical capabilities has become a reality in the realm of Industry 4.0. Enterprises are encouraged to design frameworks capable of harnessing the power of human and technological resources to enhance the era of Artificial Intelligence (AI). Over the [...] Read more.
The blending of human and mechanical capabilities has become a reality in the realm of Industry 4.0. Enterprises are encouraged to design frameworks capable of harnessing the power of human and technological resources to enhance the era of Artificial Intelligence (AI). Over the past decade, AI technologies have transformed the competitive landscape, particularly during the pandemic. Consequently, the job market, at an international level, is transforming towards the integration of suitably skilled people in cutting edge technologies, emphasizing the need to focus on the upcoming super-smart society known as Society 5.0. The concept of a Humachine builds on the notion that humans and machines have a common future that capitalizes on the strengths of both humans and machines. Therefore, the aim of this paper is to identify the capabilities and distinguishing characteristics of both humans and machines, laying the groundwork for improving human–machine interaction (HMI). Full article
(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems II)
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