Special Issue "Data Analytics Challenges in Smart Cities Applications"

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

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 5559

Special Issue Editors

Dr. Mohammad M. Banat
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Guest Editor
Jordan University of Science and Technology, Irbid, Jordan
Interests: wireless communications; signal processing; smart technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Paiva
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Guest Editor
Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
Interests: smart mobility; applied mobile computing; outdoor positioning; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue is based on extended versions of selected papers to be presented at GMC-Technology 2020.

A ‘smart city’ is defined as a complex and interconnected system that applies new technologies to manage all the elements that make it up, from transport to the efficient use of energy resources, including the social and economic aspects of its inhabitants.

In order to manage this correct operation, the research community is developing numerous applications that deal with the enormous amounts of data produced in cities. In this aspect, data analysis plays a fundamental role, in which the application of Big Data, Machine Learning or Deep Learning techniques stands out. Currently, there are several challenges in the field of application of these techniques through applications focused on the management and optimization of the activities that take place in a smart city.

This Special Issue is devoted to promoting the investigation of the latest research in Data Analytics in smart cities and their effective applications, to explore the latest innovations in models, technologies, and tools to assess the impact of the approach, and to facilitate technology transfer of these techniques to our cities.

The topics of interest for this Issue include but are not limited to the following:

  • Applications of AI (TTIA);
  • Blockchain;
  • Case-based reasoning;
  • Data analysis;
  • Machine learning;
  • Deep learning;
  • Big Data;
  • Edge computing;
  • Fog computing;
  • High-performance systems;
  • AI in mobile device development;
  • Intelligent environments;
  • Learning through reinforcement;
  • Mobile and wireless systems;
  • Model-based reasoning;
  • Multiagent systems;
  • Multimedia and distributed animation systems;
  • Networks;
  • Neural networks;
  • Smart cities challenges;
  • Smart home and smart buildings;
  • Open data and big data analytics;
  • Smart health and emergency management;
  • Smart environments;
  • Smart manufacturing and logistics.

Dr. Alfonso González-Briones
Dr. Mohammad M. Banat
Dr. Sara Paiva
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

  • data analytics
  • machine learning
  • deep learning
  • smart cities
  • open data

Published Papers (4 papers)

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Research

Article
Ontologies to Enable Interoperability of Multi-Agent Electricity Markets Simulation and Decision Support
Electronics 2021, 10(11), 1270; https://doi.org/10.3390/electronics10111270 - 26 May 2021
Cited by 1 | Viewed by 834
Abstract
This paper presents the AiD-EM Ontology, which provides a semantic representation of the concepts required to enable the interoperability between multi-agent-based decision support systems, namely AiD-EM, and the market agents that participate in electricity market simulations. Electricity markets’ constant changes, brought about by [...] Read more.
This paper presents the AiD-EM Ontology, which provides a semantic representation of the concepts required to enable the interoperability between multi-agent-based decision support systems, namely AiD-EM, and the market agents that participate in electricity market simulations. Electricity markets’ constant changes, brought about by the increasing necessity for adequate integration of renewable energy sources, make them complex and dynamic environments with very particular characteristics. Several modeling tools directed at the study and decision support in the scope of the restructured wholesale electricity markets have emerged. However, a common limitation is identified: the lack of interoperability between the various systems. This gap makes it impossible to exchange information and knowledge between them, test different market models, enable players from heterogeneous systems to interact in common market environments, and take full advantage of decision support tools. To overcome this gap, this paper presents the AiD-EM Ontology, which includes the necessary concepts related to the AiD-EM multi-agent decision support system, to enable interoperability with easier cooperation and adequate communication between AiD-EM and simulated market agents wishing to take advantage of this decision support tool. Full article
(This article belongs to the Special Issue Data Analytics Challenges in Smart Cities Applications)
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Article
An AI-Powered System for Residential Demand Response
Electronics 2021, 10(6), 693; https://doi.org/10.3390/electronics10060693 - 16 Mar 2021
Cited by 4 | Viewed by 1008
Abstract
Recent studies show that energy consumption of buildings has dramatically increased over the last decade, accounting for more than 35% of global energy use. However, with proper operation, significant energy savings can be achieved. Demand response is envisioned as a key enabler of [...] Read more.
Recent studies show that energy consumption of buildings has dramatically increased over the last decade, accounting for more than 35% of global energy use. However, with proper operation, significant energy savings can be achieved. Demand response is envisioned as a key enabler of this operation enhancement, as it may contribute to the reduction of demand peaks and maximization of renewable energy exploitation while mitigating potential problems with grid stability. In this article, a system based on artificial intelligence that solves the complex multi-objective problem to bring demand response programs to the residential sector is proposed. Through the application of novel machine learning-based algorithms, a unique control loop is developed to help dwellers determine how and when to use their appliances. The feasibility and validity of the proposed system has been demonstrated in a real-world neighbourhood where a notable reduction and shift of electricity demand peaks has been achieved. Concretely, in accordance with extreme changes in the energy prices, the users have demonstrated the ability to shift their demand to periods with lower prices as well as reducing power consumption during periods with higher prices, thus fully translating the demand peak in time. Full article
(This article belongs to the Special Issue Data Analytics Challenges in Smart Cities Applications)
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Article
LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities
Electronics 2021, 10(1), 78; https://doi.org/10.3390/electronics10010078 - 04 Jan 2021
Cited by 5 | Viewed by 1246
Abstract
Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. [...] Read more.
Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies. Full article
(This article belongs to the Special Issue Data Analytics Challenges in Smart Cities Applications)
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Article
SMS: A Secure Healthcare Model for Smart Cities
Electronics 2020, 9(7), 1135; https://doi.org/10.3390/electronics9071135 - 13 Jul 2020
Cited by 11 | Viewed by 1517
Abstract
Technological innovations have enabled the realization of a utopian world where all objects of everyday life, as well as humans, are interconnected to form an “Internet of Things (IoT).” These connected technologies and IoT solutions have led to the emergence of smart cities [...] Read more.
Technological innovations have enabled the realization of a utopian world where all objects of everyday life, as well as humans, are interconnected to form an “Internet of Things (IoT).” These connected technologies and IoT solutions have led to the emergence of smart cities where all components are converted into a connected smart ecosystem. IoT has envisioned several areas of smart cities including the modern healthcare environment like real-time monitoring, patient information management, ambient-assisted living, ambient-intelligence, anomaly detection, and accelerated sensing. IoT has also brought a breakthrough in the medical domain by integrating stake holders, medical components, and hospitals to bring about holistic healthcare management. The healthcare domain is already witnessing promising IoT-based solutions ranging from embedded mobile applications to wearable devices and implantable gadgets. However, with all these exemplary benefits, there is a need to ensure the safety and privacy of the patient’s personal and medical data communicated to and from the connected devices and systems. For a smart city, it is pertinent to have an accessible, effective, and secure healthcare system for its inhabitants. This paper discusses the various elements of technology-enabled healthcare and presents a privacy-preserved and secure “Smart Medical System (SMS)” framework for the smart city ecosystem. For providing real-time analysis and responses, this paper proposes to use the concept of secured Mobile Edge Computing (MEC) for performing critical time-bound computations on the edge itself. In order to protect the medical and personal data of the patients and to make the data tamper-proof, the concept of blockchain has been used. Finally, this paper highlights the ways to capture and store the medical big data generated from IoT devices and sensors. Full article
(This article belongs to the Special Issue Data Analytics Challenges in Smart Cities Applications)
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