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Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 17660

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Prof. Dr. Juan M. Corchado

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1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
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Dr. Sara Rodriguez

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BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; distributed computing; machine learning; bioinformatics
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Dr. Fernando De la Prieta Pintado

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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
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Prof. Dr. Paweł Sitek

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Department of Control and Management Systems, Kielce University of Technology, 25-314 Kielce, Poland
Interests: constraint programming; operation research; optimization; decision support; logistics; manufacturing; supply chain management
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Dr. Vicente Julian Inglada

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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
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Prof. Dr. Rashid Mehmood

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High Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Interests: high-performance computing (HPC); big data; AI and IoT with applications in smart cities, healthcare, transportation, logistics, and toxicology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applying artificial intelligence in distributed environments is becoming an element of high added value and economic potential. Research on intelligent distributed systems has matured over the last decade, and many effective applications are now being deployed in both urban and rural environments. Smart cities are extending their concept to other non-urban areas where distributed computing is able to reach. The wellbeing of all the citizens of a country remains in the variety of its areas. It is therefore necessary and crucial to engage both urban and rural areas to achieve their economic, social, and environmental objectives.

Most computing systems, ranging from personal laptops to cluster/grid /cloud computing systems, are capable of parallel and distributed computing. Intelligent distributed computing performs an increasingly important role in modern signal/data processing, information fusion, and electronics engineering.

The combination of technological solutions based on technologies such as Artificial Intelligence (AI) and Machine Learning (Machine Learning) in distributed systems has allowed the creation of some solutions such as creating “Smart Spaces” regardless of the users' location and that provide flexible and adaptable seamless cloud-edge decision making.

The main objective of this Special Issue is to seek high-quality research that presents emerging solutions and/or applications based on AI techniques, such as Machine Learning, that address recent challenges in intelligent distributed systems. We especially focus on technological problems such as scarce energy and computational resources, frequent changes of conditions, geographical dispersion, and unexpected connectivity problems, but also on problems related to the independence and autonomy of participants, such as the provision of fair solutions that foster collaboration and information exchange and provide benefits for all involved entities.

Topics of interest include but are not limited to:

Intelligent computing spaces
Application of Artificial Intelligence in distributed systems
Application of Machine Learning algorithms in distributed systems
Distributed algorithms
Distributed databases
Computer GRID
Cloud computing
Edge computing
Fog computing
Management solutions for large volumes of data (big data)
Distributed architectures
Multiagent systems
High-performance distributed systems
AI-driven methods for multimodal networks and process modeling

This Special Issue concerns extended research presented during the set of the following conferences: PAAMS, DCAI, PACBB and BLOCKCHAIN.

Prof. Dr. Juan M. Corchado
Prof. Dr. Sara Rodriguez
Dr. Fernando De la Prieta
Prof. Dr. Paweł Sitek
Prof. Dr. Vicente Julian
Prof. Dr. Rashid Mehmood
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

Artificial Intelligence
distributed computing
cloud computing
edge computing
fog computing
machine learning
smart spaces
intelligent agents
user mobility
ubiquitous computing
reasoning mechanism

Published Papers (8 papers)

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Editorial

Jump to: Research

4 pages, 198 KiB

Open AccessEditorial

Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces

by Juan M. Corchado, Sara Rodríguez, Fernando De la Prieta, Paweł Sitek, Vicente Julián and Rashid Mehmood

Electronics 2023, 12(15), 3240; https://doi.org/10.3390/electronics12153240 - 27 Jul 2023

Viewed by 993

Abstract

This editorial presents a summary of the Special Issue on Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces, presented in the “Computer Science & Engineering” section of Electronics (ISSN 2079-9292) [...] Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

Research

Jump to: Editorial

32 pages, 3057 KiB

Open AccessArticle

Driver Stress Detection from Physiological Signals by Virtual Reality Simulator

by Nuria Mateos-García, Ana-Belén Gil-González, Ana Luis-Reboredo and Belén Pérez-Lancho

Electronics 2023, 12(10), 2179; https://doi.org/10.3390/electronics12102179 - 10 May 2023

Cited by 2 | Viewed by 3420

Abstract

One of the many areas in which artificial intelligence (AI) techniques are used is the development of systems for the recognition of vital emotions to control human health and safety. This study used biometric sensors in a multimodal approach to capture signals in the recognition of stressful situations. The great advances in technology have allowed the development of portable devices capable of monitoring different physiological measures in an inexpensive, non-invasive, and efficient manner. Virtual reality (VR) has evolved to achieve a realistic immersive experience in different contexts. The combination of AI, signal acquisition devices, and VR makes it possible to generate useful knowledge even in challenging situations in daily life, such as when driving. The main goal of this work is to combine the use of sensors and the possibilities offered by VR for the creation of a system for recognizing stress during different driving situations in a vehicle. We investigated the feasibility of detecting stress in individuals using physiological signals collected using a photoplethysmography (PPG) sensor incorporated into a commonly used wristwatch. We developed an immersive environment based on VR to simulate experimental situations and collect information on the user’s reactions through the detection of physiological signals. Data collected through sensors in the VR simulations are taken as input to several models previously trained by machine learning (ML) algorithms to obtain a system that performs driver stress detection and high-precision classification in real time. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

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25 pages, 5375 KiB

Open AccessFeature PaperArticle

Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

by Zbigniew Juzoń, Jarosław Wikarek and Paweł Sitek

Electronics 2023, 12(9), 2015; https://doi.org/10.3390/electronics12092015 - 26 Apr 2023

Cited by 2 | Viewed by 996

Abstract

Production optimization is a complex process because it must take into account various resources of the company and its environment. In this process, it is necessary to consider the enterprise as a whole, taking into account the interaction between its key elements, both in the technological and business layer. For this reason, the article proposes the use of enterprise architecture, which facilitates the interaction of these layers in the production optimization process. As a result, a proprietary meta-model of enterprise architecture was presented, which, based on good practices and the assumptions of enterprise architecture, facilitates the construction of detailed optimization models in the area of planning, scheduling, resource allocation, and routing. The production optimization model formulated as a mathematical programming problem is also presented. The model was built taking into account the meta-model. Due to the computational complexity of the optimization model, a method using an artificial neural network (ANN) was proposed to estimate the potential result based on the structure of the model and a given data instance before the start of optimization. The practical application of the presented approach has been shown based on the example of optimization of the production of an exemplary production cell where the cost of storage and the number of unfulfilled orders and maintenance are optimized. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

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21 pages, 2345 KiB

Open AccessArticle

Predicting Model Training Time to Optimize Distributed Machine Learning Applications

by Miguel Guimarães, Davide Carneiro, Guilherme Palumbo, Filipe Oliveira, Óscar Oliveira, Victor Alves and Paulo Novais

Electronics 2023, 12(4), 871; https://doi.org/10.3390/electronics12040871 - 08 Feb 2023

Cited by 1 | Viewed by 2425

Abstract

Despite major advances in recent years, the field of Machine Learning continues to face research and technical challenges. Mostly, these stem from big data and streaming data, which require models to be frequently updated or re-trained, at the expense of significant computational resources. One solution is the use of distributed learning algorithms, which can learn in a distributed manner, from distributed datasets. In this paper, we describe CEDEs—a distributed learning system in which models are heterogeneous distributed Ensembles, i.e., complex models constituted by different base models, trained with different and distributed subsets of data. Specifically, we address the issue of predicting the training time of a given model, given its characteristics and the characteristics of the data. Given that the creation of an Ensemble may imply the training of hundreds of base models, information about the predicted duration of each of these individual tasks is paramount for an efficient management of the cluster’s computational resources and for minimizing makespan, i.e., the time it takes to train the whole Ensemble. Results show that the proposed approach is able to predict the training time of Decision Trees with an average error of 0.103 s, and the training time of Neural Networks with an average error of 21.263 s. We also show how results depend significantly on the hyperparameters of the model and on the characteristics of the input data. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

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18 pages, 1582 KiB

Open AccessArticle

Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE

by Javier Palanca, Jaime Andres Rincon, Carlos Carrascosa, Vicente Javier Julian and Andrés Terrasa

Electronics 2023, 12(3), 659; https://doi.org/10.3390/electronics12030659 - 28 Jan 2023

Cited by 3 | Viewed by 1630

Abstract

Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

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21 pages, 2524 KiB

Open AccessFeature PaperArticle

Development and Assessment of an Indoor Air Quality Control IoT-Based System

by Gleiston Guerrero-Ulloa, Alex Andrango-Catota, Martín Abad-Alay, Miguel J. Hornos and Carlos Rodríguez-Domínguez

Electronics 2023, 12(3), 608; https://doi.org/10.3390/electronics12030608 - 26 Jan 2023

Cited by 5 | Viewed by 2984

Abstract

Good health and well-being are primary goals within the list of Sustainable Development Goals (SDGs) proposed by the United Nations (UN) in 2015. New technologies, such as Internet of Things (IoT) and Cloud Computing, can aid to achieve that goal by enabling people to improve their lifestyles and have a more healthy and comfortable life. Pollution monitoring is especially important in order to avoid exposure to fine particles and to control the impact of human activity on the natural environment. Some of the sources of hazardous gas emissions can be found indoors. For instance, carbon monoxide (CO), which is considered a silent killer because it can cause death, is emitted by water heaters and heaters that rely on fossil fuels. Existing solutions for indoor pollution monitoring suffer from some drawbacks that make their implementation impossible for households with limited financial resources. This paper presents the development of IdeAir, a low-cost IoT-based air quality monitoring system that aims to reduce the disadvantages of existing systems. IdeAir was designed as a proof of concept to capture and determine the concentrations of harmful gases in indoor environments and, depending on their concentration levels, issue alarms and notifications, turn on the fan, and/or open the door. It has been developed following the Test-Driven Development Methodology for IoT-based Systems (TDDM4IoTS), which, together with the tool (based on this methodology) used for the automation of the development of IoT-based systems, has facilitated the work of the developers. Preliminary results on the functioning of IdeAir show a high level of acceptance by potential users. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

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18 pages, 502 KiB

Open AccessArticle

Beyond Benchmarks: Spotting Key Topical Sentences While Improving Automated Essay Scoring Performance with Topic-Aware BERT

by Yongchao Wu, Aron Henriksson, Jalal Nouri, Martin Duneld and Xiu Li

Electronics 2023, 12(1), 150; https://doi.org/10.3390/electronics12010150 - 29 Dec 2022

Cited by 1 | Viewed by 1606

Abstract

Automated Essay Scoring (AES) automatically allocates scores to essays at scale and may help teachers reduce the heavy burden during grading activities. Recently, researchers have deployed neural-based AES approaches to improve upon the state-of-the-art AES performance. These neural-based AES methods mainly take student essays as the sole input and focus on learning the relationship between student essays and essay scores through deep neural networks. However, their only product, the predicted holistic score, is far from providing adequate pedagogical information, such as automated writing evaluation (AWE). In this work, we propose Topic-aware BERT, a new method of learning relations among scores, student essays, as well as topical information in essay instructions. Beyond improving the AES benchmark performance, Topic-aware BERT can automatically retrieve key topical sentences in student essays by probing self-attention maps in intermediate layers. We evaluate the performance of Topic-aware BERT of different variants to (i) perform AES and (ii) retrieve key topical sentences using the open dataset Automated Student Assessment Prize and a manually annotated dataset. Our experiments show that Topic-aware BERT achieves a strong AES performance compared with the previous best neural-based AES methods and demonstrates effectiveness in identifying key topical sentences in argumentative essays. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

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14 pages, 575 KiB

Open AccessArticle

Limitations of Nature-Inspired Algorithms for Pricing on Digital Platforms

by J. Manuel Sanchez-Cartas and Ines P. Sancristobal

Electronics 2022, 11(23), 3927; https://doi.org/10.3390/electronics11233927 - 28 Nov 2022

Cited by 1 | Viewed by 1226

Abstract

Digital platforms have begun to rely more on algorithms to perform basic tasks such as pricing. These platforms must set prices that coordinate two or more sides that need each other in some way (e.g., developers and users or buyers and sellers). Therefore, it is essential to form correct expectations about how both sides behave. The purpose of this paper was to study the effect of different levels of information on two biology-inspired metaheuristics (differential evolution and particle swarm optimization algorithms) that were programmed to set prices on multisided platforms. We assumed that one platform always formed correct expectations (human platform) while the competitor always used a generic version of particle swarm optimization or differential evolution algorithms. We tested different levels of information that modified how expectations were formed. We found that both algorithms might end up in suboptimal solutions, showing that algorithms needed to account for expectation formation explicitly or risk setting nonoptimal prices. In addition, we found regularity in the way algorithms set prices when they formed incorrect expectations that can help practitioners detect cases in need of intervention. Full article

(This article belongs to the Special Issue Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces)

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