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Algorithms
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Algorithms for Network Systems and Applications
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Algorithms for Network Systems and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3366

Special Issue Editors

Dr. Marek Bolanowski

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Guest Editor
Department of Complex Systems, Rzeszow University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland
Interests: distributed systems; self-aware and autonomous systems; anomaly detection; Industry 4.0; Internet of Everything; cybersecurity; complex communication systems; SDN networks; PoC systems
Special Issues, Collections and Topics in MDPI journals
Dr. Andrzej Białas

E-Mail Website
Guest Editor
ŁUKASIEWICZ Research Network—Institute of Innovative Technologies EMAG, 40-189 Katowice, Poland
Interests: information security; risk management; knowledge engineering; esp. in IT security domain; common criteria—design and evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Janusz Furtak

E-Mail Website
Guest Editor
Faculty of Cybernetics, Military University of Technology, 00-908 Warsaw, Poland
Interests: computer networks; Internet of Things; security in computer networks
Dr. Andrzej Paszkiewicz

E-Mail Website
Guest Editor
Politechnika Rzeszowska, 35-959 Rzeszow, Poland
Interests: computer network; Industry 4.0; Internet of Things; intelligent manufacturing

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of the journal Algorithms focused on recent research in computer science and intelligence systems. This Special Issue is associated with the “Track 3: Network Systems and Applications” of the 18th Federated Conference on Computer Science and Information Systems (FedCSIS 2023, https://fedcsis.org/) to be held in Warsaw, Poland, from 17–20 September 2023.

The FedCSIS conference serves as a premier international forum for researchers and practitioners to present state-of-the-art research, technologies, theories, and applications in the fields of computer science and information systems.

This Special Issue welcomes high-quality submissions addressing innovative theories, frameworks, methodologies, and solutions in relevant topics including, but not limited to, the following:

  • Graph theory and network algorithm application
  • Algorithms for controlling and monitoring complex computer networks
  • Privacy-enhancing technologies
  • Protocols and algorithms for IoT
  • Artificial intelligence and IoT

We welcome the submission of original research papers and review articles that comprehensively summarize advances in these areas. Contributions are encouraged not only from FedCSIS 2023 attendees, but also from the broader research community.

Dr. Marek Bolanowski
Dr. Andrzej Białas
Dr. Janusz Furtak
Dr. Andrzej Paszkiewicz
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. Algorithms 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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33 pages, 14331 KiB  
Article
A Virtual Machine Platform Providing Machine Learning as a Programmable and Distributed Service for IoT and Edge On-Device Computing: Architecture, Transformation, and Evaluation of Integer Discretization
by Stefan Bosse
Algorithms 2024, 17(8), 356; https://doi.org/10.3390/a17080356 - 15 Aug 2024
Viewed by 1010
Abstract
Data-driven models used for predictive classification and regression tasks are commonly computed using floating-point arithmetic and powerful computers. We address constraints in distributed sensor networks like the IoT, edge, and material-integrated computing, providing only low-resource embedded computers with sensor data that are acquired [...] Read more.
Data-driven models used for predictive classification and regression tasks are commonly computed using floating-point arithmetic and powerful computers. We address constraints in distributed sensor networks like the IoT, edge, and material-integrated computing, providing only low-resource embedded computers with sensor data that are acquired and processed locally. Sensor networks are characterized by strong heterogeneous systems. This work introduces and evaluates a virtual machine architecture that provides ML as a service layer (MLaaS) on the node level and addresses very low-resource distributed embedded computers (with less than 20 kB of RAM). The VM provides a unified ML instruction set architecture that can be programmed to implement decision trees, ANN, and CNN model architectures using scaled integer arithmetic only. Models are trained primarily offline using floating-point arithmetic, finally converted by an iterative scaling and transformation process, demonstrated in this work by two tests based on simulated and synthetic data. This paper is an extended version of the FedCSIS 2023 conference paper providing new algorithms and ML applications, including ANN/CNN-based regression and classification tasks studying the effects of discretization on classification and regression accuracy. Full article
(This article belongs to the Special Issue Algorithms for Network Systems and Applications)
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23 pages, 686 KiB  
Article
Verification of Control System Runtime Using an Executable Semantic Model
by Jan Sadolewski and Bartosz Trybus
Algorithms 2024, 17(7), 273; https://doi.org/10.3390/a17070273 - 22 Jun 2024
Viewed by 930
Abstract
The paper outlines a methodology for validating the accuracy of a control system’s runtime implementation. The runtime takes the form of a virtual machine executing portable code compliant with IEC 61131-3 standards. A formal model, comprising denotational semantics equations, has been devised to [...] Read more.
The paper outlines a methodology for validating the accuracy of a control system’s runtime implementation. The runtime takes the form of a virtual machine executing portable code compliant with IEC 61131-3 standards. A formal model, comprising denotational semantics equations, has been devised to specify machine instruction decoding and operations, including arithmetic functions across various data types, arrays, and subprogram calls. The model also encompasses exception-handling mechanisms for runtime errors, such as division by zero and invalid array index access. This denotational model is translated into executable form using the functional F ♯ language. Verification involves comparing the actual implementation of the virtual machine against this executable model. Any disparities between the model and implementation indicate deviations from the specification. Implemented within the CPDev engineering environment, this approach ensures consistent and predictable control program execution across different target platforms. Full article
(This article belongs to the Special Issue Algorithms for Network Systems and Applications)
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19 pages, 346 KiB  
Article
Gain and Pain in Graph Partitioning: Finding Accurate Communities in Complex Networks
by Arman Ferdowsi and Maryam Dehghan Chenary
Algorithms 2024, 17(6), 226; https://doi.org/10.3390/a17060226 - 23 May 2024
Cited by 1 | Viewed by 851
Abstract
This paper presents an approach to community detection in complex networks by simultaneously incorporating a connectivity-based metric and Max-Min Modularity. By leveraging the connectivity-based metric and employing a heuristic algorithm, we develop a novel complementary graph for the Max-Min Modularity that enhances its [...] Read more.
This paper presents an approach to community detection in complex networks by simultaneously incorporating a connectivity-based metric and Max-Min Modularity. By leveraging the connectivity-based metric and employing a heuristic algorithm, we develop a novel complementary graph for the Max-Min Modularity that enhances its effectiveness. We formulate community detection as an integer programming problem of an equivalent yet more compact counterpart model of the revised Max-Min Modularity maximization problem. Using a row generation technique alongside the heuristic approach, we then provide a hybrid procedure for near-optimally solving the model and discovering high-quality communities. Through a series of experiments, we demonstrate the success of our algorithm, showcasing its efficiency in detecting communities, particularly in extensive networks. Full article
(This article belongs to the Special Issue Algorithms for Network Systems and Applications)
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