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Electronics
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Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart...
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Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 10589

Special Issue Editors

Dr. Fernando De la Prieta Pintado

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Guest Editor
BISITE Research Group, University of Salamanca, Edificio Multiusos I + D + I, 37007 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; cloud computing and distributed systems; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals
Dr. Vicente Julian Inglada

E-Mail Website
Guest Editor
Valencian Research Institute for Artificial Intelligence (VRAIn), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: multi-agent systems; agreement technologies; ambient intelligence; affective computing; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sascha Ossowski

E-Mail Website
Guest Editor
Centre for Intelligent Information Technologies (CETINIA), Universidad Rey Juan Carlos, Móstoles Campus, 28933 Mostoles, Spain
Interests: software systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. José Machado

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Guest Editor
Computer Science and Technology, ALGORITMI Research Centre, University of Minho, 4710-057 Braga, Portugal
Interests: knowledge engineering; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

IoT and machine learning are two of the most exciting disciplines in technology today because of their profound impact on both businesses and individuals. Millions of small devices are already embedded in factories, cities, vehicles, phones, and our homes, collecting the information needed for intelligent decision making in areas such as industrial process optimization, predictive maintenance in buildings, people's mobility, home energy management, and more.

The focus of most of these applications is to gather information from the environment and transmit it over the Internet to powerful remote servers where intelligence and decision making resides. However, applications such as self-driving cars are critical and require accurate real-time responses.

To alleviate some of the above problems, edge computing has emerged, changing the way data is processed, improving response times, and addressing the connectivity, scalability, and security issues inherent to remote servers. Hybrid approaches have also emerged, with the goal of maximizing the benefits of edge and cloud.

All of these technologies are usually integrated in a hybrid architecture, which allows defined agents to perform the tasks assigned to them in a coordinated manner, each working independently and fulfilling the different needs that may arise without surveillance.

This Special Issue invites researchers to submit original quality studies regarding the technologies in the domains of IoT and machine learning, as well as their integration in hybrid architectures, and urges them to address the main subdisciplines, which include, but are not limited to, the following:

  • Edge computing;
  • Edge artificial intelligence;
  • Hybrid edge computing models;
  • Federated learning;
  • Machine and deep learning agent architectures;
  • Distributed problem solving;
  • Agent-based simulation;
  • Multi-agent systems (MAS);
  • Virtual agent organizations (VAO);
  • IoT and MAS;
  • IoT, smart cities, Industry 4.0;
  • Ambient intelligence.

Dr. Fernando De la Prieta Pintado
Dr. Vicente Julian Inglada
Prof. Dr. Sascha Ossowski
Dr. José Machado
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. Electronics 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

  • edge computing
  • artificial intelligence
  • federated learning
  • multi-agent systems
  • IoT
  • ambient intelligence

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Published Papers (6 papers)

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Research

15 pages, 761 KiB  
Article
Federated Learning for Collaborative Robotics: A ROS 2-Based Approach
by Gerardo M. Gutierrez, Jaime A. Rincon and Vicente Julian
Electronics 2025, 14(7), 1323; https://doi.org/10.3390/electronics14071323 - 27 Mar 2025
Viewed by 473
Abstract
This paper presents a federated learning framework for multi-agent robotic systems, leveraging the ROS 2 framework to enable decentralized collaboration in both simulated and real-world environments. Traditional centralized machine learning approaches face challenges such as data privacy concerns, communication overhead, and limited scalability. [...] Read more.
This paper presents a federated learning framework for multi-agent robotic systems, leveraging the ROS 2 framework to enable decentralized collaboration in both simulated and real-world environments. Traditional centralized machine learning approaches face challenges such as data privacy concerns, communication overhead, and limited scalability. To address these issues, we propose a federated reinforcement learning architecture where multiple robotic agents train local models and share their knowledge while preserving data privacy. The framework integrates deep reinforcement learning techniques, utilizing Unity for high-fidelity simulation. Experimental evaluations compare our federated approach against classical centralized learning, demonstrating that our proposal improves model generalization, stabilizes reward distribution, and reduces training variance. Additionally, results indicate that increasing the number of robots enhances task efficiency, reducing the number of steps required for successful navigation while maintaining consistent performance. This study highlights the potential of federated learning in robotics, offering a scalable and privacy-preserving approach to distributed multi-agent learning. Full article
(This article belongs to the Special Issue Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas)
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15 pages, 1522 KiB  
Article
Reducing Computational Cost in MobileViT for Edge-Oriented Models Through Token Merging
by Mitsuhiko Yasukura, Michifumi Yoshioka and Katsufumi Inoue
Electronics 2024, 13(24), 5009; https://doi.org/10.3390/electronics13245009 - 20 Dec 2024
Viewed by 841
Abstract
We focus on developing a lightweight model for resource-constrained devices, building on MobileViT, a hybrid model that combines the strengths of Transformers and CNNs to balance high accuracy and computational efficiency for image classification. Transformers, while effective at capturing global information, often have [...] Read more.
We focus on developing a lightweight model for resource-constrained devices, building on MobileViT, a hybrid model that combines the strengths of Transformers and CNNs to balance high accuracy and computational efficiency for image classification. Transformers, while effective at capturing global information, often have higher computational costs than CNNs due to the complexity of their self-attention mechanism. To address this, we introduce the Token Merging (ToMe) technique into MobileViT to reduce computational costs. However, because the number of tokens changes during merging, ToMe cannot be directly applied to MobileViT without adjustments. We propose simple methods, specifically reshaping features and removing skip connections, to resolve this issue. Additionally, we make adjustments to MobileViT’s structure to better support the application of ToMe. Our approach improves inference efficiency while retaining a competitive level of accuracy. The resulting models achieve a balance between performance and computational speed, offering a practical solution for hybrid architectures. This work shows the potential of ToMe-based techniques to broaden the range of lightweight model options, catering to diverse application requirements. Full article
(This article belongs to the Special Issue Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas)
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28 pages, 8743 KiB  
Article
Preventive Planning of ‘Product-as-a-Service’ Offers Using Genetic Population-Driven Stepping Crawl Threads
by Krzysztof Niemiec, Eryk Szwarc, Grzegorz Bocewicz and Zbigniew Banaszak
Electronics 2024, 13(23), 4710; https://doi.org/10.3390/electronics13234710 - 28 Nov 2024
Viewed by 637
Abstract
Unlike the precise methods implemented in constrained programming environments, the proposed approach to preventive planning of Product-as-a-Service offers implements a competitive solution based on Genetic Population Stepping Crawl Threads (GPSCT).GPSCT techniques are used to determine the so-called stepping crawl threads (SCT) that recreate, [...] Read more.
Unlike the precise methods implemented in constrained programming environments, the proposed approach to preventive planning of Product-as-a-Service offers implements a competitive solution based on Genetic Population Stepping Crawl Threads (GPSCT).GPSCT techniques are used to determine the so-called stepping crawl threads (SCT) that recreate, in subsequent steps, variants of the allocation of sets of leased devices with parameters that meet the expectations of the customers ordering them by means of genetic algorithms. SCTs initiated at a selected point of the Cartesian product space of the functional repertoire of the equipment offered penetrate it in search of offer variants that meet the constraints imposed by the size of the budget and the risk level (i.e., expressed as the likelihood of damaging the device or losing part of its functionality) of individual customers. Two approaches of implementation techniques were used to determine the initial SCT population for the genetic algorithm—branch and bound (BBA) and linear programming (LPA). Many experiments assessed their impact on the computation time and the quality of the obtained solution. The performed computational experiments indicate that the effectiveness of both approaches depends on the specificity of the problem considered each time. Interestingly, for different instances of the problem, an alternative solution can always be selected that is competitive with the exact methods, allowing for a 10-fold increase in scalability. Full article
(This article belongs to the Special Issue Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas)
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18 pages, 1836 KiB  
Article
Robust Scheduling of Multi-Skilled Workforce Allocation: Job Rotation Approach
by Eryk Szwarc, Paulina Golińska-Dawson, Grzegorz Bocewicz and Zbigniew Banaszak
Electronics 2024, 13(2), 392; https://doi.org/10.3390/electronics13020392 - 17 Jan 2024
Cited by 2 | Viewed by 2168
Abstract
This paper addresses scheduling challenges in software development organizations, specifically focusing on a novel version of the software project scheduling problem (SPSP). This enhanced model incorporates the dynamics of learning and forgetting phenomena, crucial in maintaining employee competencies, particularly when unexpected events such [...] Read more.
This paper addresses scheduling challenges in software development organizations, specifically focusing on a novel version of the software project scheduling problem (SPSP). This enhanced model incorporates the dynamics of learning and forgetting phenomena, crucial in maintaining employee competencies, particularly when unexpected events such as absenteeism or shifts in project priorities occur. The paper introduces a new declarative reference model for SPSP, aimed at proactively managing the assignment of versatile programmers to tasks within an portfolio of IT projects, while considering the effects of forgetting. Implemented within a constraints programming environment, this model facilitates decision making in project management for software companies. It serves to find feasible solutions and identify conditions necessary to meet specified expectations. The effectiveness of the proposed SPSP model is demonstrated through numerical examples. Full article
(This article belongs to the Special Issue Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas)
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29 pages, 2717 KiB  
Article
Emotional and Mental Nuances and Technological Approaches: Optimising Fact-Check Dissemination through Cognitive Reinforcement Technique
by Francisco S. Marcondes, Maria Araújo Barbosa, Adelino de C. O. S. Gala, José João Almeida and Paulo Novais
Electronics 2024, 13(1), 240; https://doi.org/10.3390/electronics13010240 - 4 Jan 2024
Viewed by 2067
Abstract
The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a [...] Read more.
The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and X (formerly Twitter). Full article
(This article belongs to the Special Issue Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas)
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20 pages, 11346 KiB  
Article
Towards Agrirobot Digital Twins: Agri-RO5—A Multi-Agent Architecture for Dynamic Fleet Simulation
by Jorge Gutiérrez Cejudo, Francisco Enguix Andrés, Marin Lujak, Carlos Carrascosa Casamayor, Alberto Fernandez and Luís Hernández López
Electronics 2024, 13(1), 80; https://doi.org/10.3390/electronics13010080 - 23 Dec 2023
Cited by 8 | Viewed by 2369
Abstract
In this paper, we propose a multi-agent-based architecture for a Unity3D simulation of dynamic agrirobot-fleet-coordination methods. The architecture is based on a Robot Operating System (ROS) and Agrobots-SIM package that extends the existing package Patrolling SIM made for multi-robot patrolling. The Agrobots-SIM package [...] Read more.
In this paper, we propose a multi-agent-based architecture for a Unity3D simulation of dynamic agrirobot-fleet-coordination methods. The architecture is based on a Robot Operating System (ROS) and Agrobots-SIM package that extends the existing package Patrolling SIM made for multi-robot patrolling. The Agrobots-SIM package accommodates dynamic multi-robot task allocation and vehicle routing considering limited robot battery autonomy. Moreover, it accommodates the dynamic assignment of implements to robots for the execution of heterogeneous tasks. The system coordinates task assignment and vehicle routing in real time and responds to unforeseen contingencies during simulation considering dynamic updates of the data related to the environment, tasks, implements, and robots. Except for the ROS and Agrobots-SIM package, other crucial components of the architecture include SPADE3 middleware for developing and executing multi-agent decision making and the FIVE framework that allows us to seamlessly define the environment and incorporate the Agrobots-SIM algorithms to be validated into SPADE agents inhabiting such an environment. We compare the proposed simulation architecture with the conventional approach to 3D multi-robot simulation in Gazebo. The functioning of the simulation architecture is demonstrated in several use-case experiments. Even though resource consumption and community support are still an open challenge in Unity3D, the proposed Agri-RO5 architecture gives better results in terms of simulation realism and scalability. Full article
(This article belongs to the Special Issue Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas)
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