Special Issue "Advances in Machine Learning, IoT and Big Data for Sustainable Communities"

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

Deadline for manuscript submissions: 30 June 2023 | Viewed by 4025

Special Issue Editors

Dr. Simona-Vasilica Oprea
E-Mail Website
Guest Editor
Faculty of Cybernetics, Statistics, and Economic Informatics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
Interests: power system; RES integration; grid planning studies; energy efficiency; database; big data; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Fabrizio Marozzo
E-Mail Website
Guest Editor
Department of Informatics, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria, 87036 Rende, Italy
Interests: big data analysis; social media analysis; cloud computing; data mining; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaolong Jin
E-Mail Website
Guest Editor
Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark
Interests: multi-vector energy systems; smart buildings; local flexibility markets

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is to address, by using recent advances in artificial intelligence, machine learning and big data, the challenges of sustainable communities that generate large volumes of data from sensors, smart meters, IoT devices and smartphones. Sustainable communities imply a consistent use of technology for the benefit of citizens: smart grids, smart water management facilities, smart waste management systems, smart traffic and transportation systems, smart security systems or e-governance structures in such communities. These components ingest and generate a multitude of structured, semi-structured or unstructured data that require processing in real time or retrospect. Based on these big data, machine learning algorithms can be developed to aid in preventing problems (e.g., frauds, malfunctions, space scarcity, supply shortages) or identifying patterns and trends (e.g., analyzing electricity consumption, forecasting growth areas or equipment status). Numerous research opportunities can be explored for the safe operation of these components, flexibly running algorithms and avoiding big data bias, off-the-shelf machine learning algorithms and big data hubris.

Topics:

  • Machine learning theory and applications;
  • Big data management, processing and analytics;
  • Smart cities and sustainable communities;
  • IoT management and integration with utilities;
  • Cloud, distributed and parallel computing;
  • IoT, mobile-embedded and multimedia solutions.

New papers, or extended versions of papers presented at the 21st International Conference on Informatics on Economy (IE2022), are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of the Electronics journal, and accepted based on quality, originality, novelty, and relevance to the theme of the Special Issue.

Dr. Simona-Vasilica Oprea
Dr. Fabrizio Marozzo
Dr. Xiaolong Jin
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 2000 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

  • machine learning theory and applications
  • big data management, processing and analytics
  • smart cities and sustainable communities
  • IoT management and integration with utilities
  • cloud, distributed and parallel computing
  • IoT, mobile-embedded and multimedia solutions

Published Papers (3 papers)

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Research

Article
Building Integrated Photovoltaics 4.0: Digitization of the Photovoltaic Integration in Buildings for a Resilient Infra at Large Scale
Electronics 2022, 11(17), 2700; https://doi.org/10.3390/electronics11172700 - 29 Aug 2022
Cited by 1 | Viewed by 1059
Abstract
Building integrated photovoltaic (BIPV) systems have gained a lot of attention in recent years as they support the United Nations’ sustainable development goals of renewable energy generation and construction of resilient infrastructure. To make the BIPV system infra resilient, there is a need [...] Read more.
Building integrated photovoltaic (BIPV) systems have gained a lot of attention in recent years as they support the United Nations’ sustainable development goals of renewable energy generation and construction of resilient infrastructure. To make the BIPV system infra resilient, there is a need to adopt digital technologies such as the internet of things (IoT), artificial intelligence (AI), edge computing, unmanned aerial vehicles (UAV), and robotics. In this study, the current challenges in the BIPV system, such as the rise in the temperature of the PV modules, the occurrence of various faults, and the accumulation of dust particles over the module surface, have been identified and discussed based on the previous literature. To overcome the challenges, the significance and application of the integration of these digital technologies in the BIPV system are discussed along with the proposed architecture. Finally, the study discusses the vital recommendations for future directions, such as ML and DL for image enhancement and flaws detection in real-time image data; edge computing to implement DL for intelligent BIPV data analytics; fog computing for 6G assisted IoT network in BIPV; edge computing integration in UAV for intelligent automation and detection; augmented reality, virtual reality, and digital twins for virtual BIPV systems with research challenges of real-time implementation in the BIPV. Full article
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Article
Secure and Anonymous Voting D-App with IoT Embedded Device Using Blockchain Technology
Electronics 2022, 11(12), 1895; https://doi.org/10.3390/electronics11121895 - 16 Jun 2022
Cited by 2 | Viewed by 1154
Abstract
The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures [...] Read more.
The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures complete voter anonymity and end-to-end security for all entities participating in the electronic voting process. The paper outlines the solution’s two layers. Implementation details are presented for the e-voting application, which was deployed inside of an IoT embedded device. The solution and presented protocols provide two major properties: privacy and verifiability. To ensure privacy, the proposed solution protects the secrecy of each electronic vote. As for implementing verifiability, the solution prevents a corrupt authority from faking in any way the process of counting the votes. Both properties are achieved in the presented solution e-VoteD-App. Full article
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Article
Smart Cities and Awareness of Sustainable Communities Related to Demand Response Programs: Data Processing with First-Order and Hierarchical Confirmatory Factor Analyses
Electronics 2022, 11(7), 1157; https://doi.org/10.3390/electronics11071157 - 06 Apr 2022
Cited by 1 | Viewed by 932
Abstract
The mentality of electricity consumers is one of the most important entities that must be addressed when dealing with issues in the operation of power systems. Consumers are used to being completely passive, but recently these things have changed as significant progress of [...] Read more.
The mentality of electricity consumers is one of the most important entities that must be addressed when dealing with issues in the operation of power systems. Consumers are used to being completely passive, but recently these things have changed as significant progress of Information and Communication Technologies (ICT) and Internet of Things (IoT) has gained momentum. In this paper, we propose a statistical measurement model using a covariance structure, specifically a first-order confirmatory factor analysis (CFA) using SAS CALIS procedure to identify the factors that could contribute to the change of attitude within energy communities. Furthermore, this research identifies latent constructs and indicates which observed variables load on or measure them. For the simulation, two complex data sets of questionnaires created by the Irish Commission for Energy Regulation (CER) were analyzed, demonstrating the influence of some exogenous variables on the items of the questionnaires. The results revealed that there is a relevant relationship between the social–economic and the behavioral factors and the observed variables. Furthermore, the models provided a good fit to the data, as measured by the performance indicators. Full article
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