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Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (25 December 2021) | Viewed by 29578

Special Issue Editors

Prof. Dr. Jesús Peral

E-Mail Website
Guest Editor
Department of Software and Computing Systems, University of Alicante, Spain
Interests: multidimensional databases, business intelligence, data mining and information integration; natural language processing (NLP), specifically in syntactic analysis and solving linguistic phenomena (i.e., ellipsis and anaphora)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hadi Moradi

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
Interests: machine learning and intelligent systems (such as video games, IoT, and intelligent toys) for cognitive screening, rehabilitation, and empowerment; machine learning methods (such as deep learning, SVM, and random forest) for detecting earthquakes for early warning to vulnerable areas
Prof. Dr. Javi Medina Quero

E-Mail Website
Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: intelligent systems for Internet of Things, which encompasses knowledge base systems with fuzzy logic, deep learning for temporal processing and fusion of sensor data and advanced architectures for ubiquitous computing and ambient intelligence in e-Health
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Lian

E-Mail Website
Guest Editor
Department of Computer Science, Shanghai Normal University, China
Interests: spatio-temporal data mining; deep learning; big data
Prof. Dr. David Gil

E-Mail Website
Guest Editor
Department of Computing Technology and Data Processing, University of Alicante, Spain
Interests: artificial intelligence applications (such as artificial neural networks, support vector machines, decision trees, and so on); data mining applications; Big Data; Internet of things; diagnosis and decision support system in medical and cognitive sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Massive volumes of data are already present and still rapidly growing as a result of diverse data sources, including all type of smart devices and sensors (Internet of Things) and social networks. This fact has led to an increasing interest in incorporating these huge amounts of external and unstructured data, normally referred to as "Big Data", into traditional applications. This requirement has made that traditional database systems and processing need to evolve and accommodate them.

However, there are important limitations for a large-scale achievement in this revolution. Furthermore, IoT allows developing big data architectures based on services. Of course, in IoT the information varies broadly in structure, complexity and type. This leads to a need for integration, one of the most complex as well as challenging issues of Big Data, which can be defined as a set of complex techniques used to combine data from disparate sources into meaningful and valuable information.

To effectively synthesize big data and communicate among devices using IoT, machine learning techniques are employed. Machine learning extracts meaning from big data using different kind of techniques (clustering, Bayesian methods, decision trees, SVM, deep learning, etc.).

The purpose of this special issue is to publish high-quality research papers as well as review articles addressing recent advances in handling of architectures, big data, data integration, and machine learning techniques for complex IoT systems. Theoretical studies and state-of-the-art practical applications are welcome for submission.

Prof. Dr. Jesús Peral
Prof. Dr. Hadi Moradi
Prof. Dr. Javi Medina Quero
Dr. Jie Lian
Prof. Dr. David Gil
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. Sustainability 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

  • Complex IoT systems
  • Big Data architectures
  • Data Mining with Big Data
  • Machine learning techniques for Big Data analysis
  • Data visualization and integration
  • Deep learning
  • Cognitive systems

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

Published Papers (6 papers)

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Research

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10 pages, 738 KiB  
Article
A Sensorized Toy Car for Autism Screening Using Multi-Modal Features
by Bijan Mehralizadeh, Bahar Baradaran, Shahab Nikkhoo, Pegah Soleiman and Hadi Moradi
Sustainability 2023, 15(10), 7790; https://doi.org/10.3390/su15107790 - 10 May 2023
Cited by 4 | Viewed by 2424
Abstract
Early screening, which can lead to early diagnosis and intervention for children with autism (ASD), can significantly improve their life quality. Consequently, the observational process of the ASD diagnosis and the lack of experts make the technology-based ASD screening methods more demanding. On [...] Read more.
Early screening, which can lead to early diagnosis and intervention for children with autism (ASD), can significantly improve their life quality. Consequently, the observational process of the ASD diagnosis and the lack of experts make the technology-based ASD screening methods more demanding. On the other hand, early ASD screening based on behavioral patterns is one of the most reliable methods that could be accomplished by analyzing children’s playing patterns. Thus, in this paper, we present an extension of our initial sensorized toy car functionalities by adding shaft encoders to detect attention to details and interest in rotating objects in children with ASD. Using the two modalities to detect different ASD symptoms improved our screening accuracy by more than 10%. Having such a sensorized toy car, which can be used at homes or clinics for screening ASD without high expertise, can improve the life of children with ASD and their families by providing an initial screening for further evaluation. This is essential for sustainable development, especially considering the increased rate of children with ASD around the world. Full article
(This article belongs to the Special Issue Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems)
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15 pages, 533 KiB  
Article
Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates
by Guillermo Almonacid-Olleros, Gabino Almonacid, David Gil and Javier Medina-Quero
Sustainability 2022, 14(5), 3092; https://doi.org/10.3390/su14053092 - 7 Mar 2022
Cited by 4 | Viewed by 2063
Abstract
New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer [...] Read more.
New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models. Full article
(This article belongs to the Special Issue Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems)
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17 pages, 6023 KiB  
Article
The Application of a Pavement Distress Detection Method Based on FS-Net
by Yun Hou, Yuanshuai Dong, Yanhong Zhang, Zuofeng Zhou, Xinlong Tong, Qingquan Wu, Zhenyu Qian and Ran Li
Sustainability 2022, 14(5), 2715; https://doi.org/10.3390/su14052715 - 25 Feb 2022
Cited by 13 | Viewed by 2285
Abstract
In order to solve the problem of difficulties in pavement distress detection in the field of pavement maintenance, a pavement distress detection algorithm based on a new deep learning method is proposed. Firstly, an image data set of pavement distress is constructed, including [...] Read more.
In order to solve the problem of difficulties in pavement distress detection in the field of pavement maintenance, a pavement distress detection algorithm based on a new deep learning method is proposed. Firstly, an image data set of pavement distress is constructed, including large-scale image acquisition, expansion and distress labeling; secondly, the FReLU structure is used to replace the leaky ReLU activation function to improve the ability of two-dimensional spatial feature capture; finally, in order to improve the detection ability of this model for long strip pavement distress, the strip pooling method is used to replace the maximum pooling method commonly used in the existing network, and a new method is formed which integrates the FReLU structure and the strip pooling method, named FS-Net in this paper. The results show that the average accuracy of the proposed method is 4.96% and 3.67% higher than that of the faster R-CNN and YOLOv3 networks, respectively. The detection speed of 4 K images can reach about 12 FPS. The accuracy and computational efficiency can meet the actual needs in the field of road detection. A set of lightweight detection equipment for highway pavement was formed in this paper by purchasing hardware, developing software, designing brackets and packaging shells, and the FS-Net was burned into the equipment. The recognition rate of pavement distress is more than 90%, and the measurement error of the crack width is within ±0.5 mm through application testing. The lightweight detection equipment for highway pavement with burning of the pavement distress detection algorithm based on FS-Net can detect pavement conditions quickly and identify the distress and calculate the distress parameters, which provide a large amount of data support for the pavement maintenance department to make maintenance decisions. Full article
(This article belongs to the Special Issue Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems)
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20 pages, 8728 KiB  
Article
Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data
by Jose Manuel Barrera, Alejandro Reina, Alejandro Maté and Juan Carlos Trujillo
Sustainability 2020, 12(17), 6915; https://doi.org/10.3390/su12176915 - 25 Aug 2020
Cited by 42 | Viewed by 8313
Abstract
With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable [...] Read more.
With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature. Full article
(This article belongs to the Special Issue Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems)
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Review

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26 pages, 2946 KiB  
Review
Smart Grids and Their Role in Transforming Human Activities—A Systematic Literature Review
by Ángeles Verdejo-Espinosa, Macarena Espinilla-Estévez and Francisco Mata Mata
Sustainability 2020, 12(20), 8662; https://doi.org/10.3390/su12208662 - 19 Oct 2020
Cited by 7 | Viewed by 3931
Abstract
In this work, a systematic review of the literature has been carried out to analyse the design of intelligent networks in environments inhabited by people and the applications of sensors to improve quality of life and aid human activities. This study aims to [...] Read more.
In this work, a systematic review of the literature has been carried out to analyse the design of intelligent networks in environments inhabited by people and the applications of sensors to improve quality of life and aid human activities. This study aims to answer three research questions. The first question is whether the design of smart grids is made with people in mind. The second question focuses on whether intelligent networks are being taken account of in the research on human activity recognition, the Internet of Things, and the recognition of activities of daily living. The third question looks at whether there are synergies and multidisciplinary teams studying state-of-the-art technologies applied to environments inhabited by elderly or disabled people. Installations with sensors deployed for the improvement of the quality of human life will also help to improve the quality of the intelligent network, thus integrating the Human–Technology binomial. This study concludes with an analysis of the results of the sources examined, putting forward a protocol of seven proposals to guide future work. Full article
(This article belongs to the Special Issue Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems)
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41 pages, 5287 KiB  
Review
Modeling and Management Big Data in Databases—A Systematic Literature Review
by Diana Martinez-Mosquera, Rosa Navarrete and Sergio Lujan-Mora
Sustainability 2020, 12(2), 634; https://doi.org/10.3390/su12020634 - 15 Jan 2020
Cited by 26 | Viewed by 9178
Abstract
The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question [...] Read more.
The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question is how the number of published papers about Big Data modeling and management has evolved over time. The second question is whether the research is focused on semi-structured and/or unstructured data and what techniques are applied. Finally, the third question determines what trends and gaps exist according to three key concepts: the data source, the modeling and the database. As result, 36 studies, collected from the most important scientific digital libraries and covering the period between 2010 and 2019, were deemed relevant. Moreover, we present a complete bibliometric analysis in order to provide detailed information about the authors and the publication data in a single document. This SLR reveal very interesting facts. For instance, Entity Relationship and document-oriented are the most researched models at the conceptual and logical abstraction level respectively and MongoDB is the most frequent implementation at the physical. Furthermore, 2.78% studies have proposed approaches oriented to hybrid databases with a real case for structured, semi-structured and unstructured data. Full article
(This article belongs to the Special Issue Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems)
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