Special Issue "Transforming Future Cities: Smart City"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Assoc. Prof. Dr. Dhananjay Singh
Website
Guest Editor
Department of Electronics Engineering, Hankuk (Korea) University of Foreign Studies (HUFS), Seoul 02450, South Korea
Interests: IoT for smart communities; connected vehicle; 5G and future internet; cloud computing
Special Issues and Collections in MDPI journals
Dr. Antonio J. Jara
Website
Guest Editor
Department Information Technology, University of Applied Sciences Western Switzerland (HESSO), 2800 Delémont, Switzerland
Interests: smart city; future internet/5G; IoT; cognitive computing; ubiquitous technologies
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Smart city-related research is evolving rapidly and users are shifting from local servers to community data centers. Therefore, cities are desperately in need of solutions that can improve the lifestyle of people living in both urban and rural areas by incorporating electronic solutions and technologies from all fields. However, the rapid and progressive role of smart city-enabled platforms with self-driven capabilities and miniaturized devices has entirely changed the landscape of cities. At the same time, numerous challenges have been posed to cities and communities by hackers, cyber-attackers, adversaries, and third un-trusted parties.

In this Special Issue, we would like to invite experts to submit their electronic solutions for cities and valid arguments for rectifying the technologies, and smart city services. This Special Issue will gather papers exploring innovative electronics for transforming future cities and make them publicly available to smart communities all over the world. This issue will consider publishing groundbreaking and high-quality electronics research in consumer electronics and computational solutions applied to challenging real-world problems in cities.

Assoc. Prof. Dr. Dhananjay Singh
Dr. Antonio J. Jara
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 papers will be 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 monthly 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 1500 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

  • State-of-the-art smart city devices
  • Digital circuit of smart cities
  • Augmented cities
  • Future electronics cities
  • Electronics for sustainable cities
  • Signal processing for smart energy
  • Clean water and clean air
  • Waste management technologies
  • Fiber optics sensors for smart building monitoring
  • Biosensors for smart healthcare
  • Microelectronics chips for smart transportation
  • Smart government and operations
  • Smart government operation system
  • Smart surveillance system
  • Global city business
  • Smart data management devices
  • IoT devices for smart city services
  • Smart city architecture and middleware
  • Electronics solutions for cities

Published Papers (7 papers)

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Research

Open AccessArticle
PRIPRO—Privacy Profiles: User Profiling Management for Smart Environments
Electronics 2020, 9(9), 1519; https://doi.org/10.3390/electronics9091519 - 17 Sep 2020
Cited by 1
Abstract
Smart environments are pervasive computing systems that provide higher comfort levels on daily routines throughout interactions among smart sensors and embedded computers. The lack of privacy within these interactions can lead to the exposure of sensitive data. We present PRIPRO (PRIvacy PROfiles), a [...] Read more.
Smart environments are pervasive computing systems that provide higher comfort levels on daily routines throughout interactions among smart sensors and embedded computers. The lack of privacy within these interactions can lead to the exposure of sensitive data. We present PRIPRO (PRIvacy PROfiles), a management tool that includes an Android application that acts on the user’s smartphone by allowing or blocking resources according to the context, in order to address this issue. Back-end web server processes and imposes a protocol according to the conditions that the user selected beforehand. The experimental results show that the proposed solution successfully communicates with the Android Device Administration framework, and the device appropriately reacts to the expected set of permissions imposed according to the user’s profile with low response time and resource usage. Full article
(This article belongs to the Special Issue Transforming Future Cities: Smart City)
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Open AccessArticle
IoT Technology Applications-Based Smart Cities: Research Analysis
Electronics 2020, 9(8), 1246; https://doi.org/10.3390/electronics9081246 - 02 Aug 2020
Cited by 2
Abstract
The development of technologies enables the application of the Internet of Things (IoT) in urban environments, creating smart cities. Hence, the optimal management of data generated in the interconnection of electronic sensors in real time improves the quality of life. The objective of [...] Read more.
The development of technologies enables the application of the Internet of Things (IoT) in urban environments, creating smart cities. Hence, the optimal management of data generated in the interconnection of electronic sensors in real time improves the quality of life. The objective of this study is to analyze global research on smart cities based on IoT technology applications. For this, bibliometric techniques were applied to 1232 documents on this topic, corresponding to the period 2011–2019, to obtain findings on scientific activity and the main thematic areas. Scientific production has increased annually, so that the last triennium has accumulated 83.23% of the publications. The most outstanding thematic areas were Computer Science and Engineering. Seven lines have been identified in the development of research on smart cities based on IoT applications. In addition, the study has detected seven new future research directions. The growing trend at the global level of scientific production shows the interest in developing aspects of smart cities based on IoT applications. This study contributes to the academic, scientific, and institutional discussion to improve decision making based on the available information. Full article
(This article belongs to the Special Issue Transforming Future Cities: Smart City)
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Open AccessArticle
Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data
Electronics 2020, 9(6), 1028; https://doi.org/10.3390/electronics9061028 - 22 Jun 2020
Cited by 1
Abstract
Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green [...] Read more.
Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture. Full article
(This article belongs to the Special Issue Transforming Future Cities: Smart City)
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Open AccessArticle
Cascade of One Class Classifiers for Water Level Anomaly Detection
Electronics 2020, 9(6), 1012; https://doi.org/10.3390/electronics9061012 - 17 Jun 2020
Abstract
Intelligent anomaly detection is a promising area to discover anomalies as manual processing by human are generally labor-intensive and time-consuming. An effective approach to deal with is essentially to build a classifier system that can reflect the condition of the infrastructure when it [...] Read more.
Intelligent anomaly detection is a promising area to discover anomalies as manual processing by human are generally labor-intensive and time-consuming. An effective approach to deal with is essentially to build a classifier system that can reflect the condition of the infrastructure when it tends to behave abnormally, and therefore the appropriate course of action can be taken immediately. In order to achieve aforementioned objective, we proposed to build a dual-staged cascade one class SVM (OCSVM) for water level monitor systems. In the first stage of the cascade model, our OCSVM learns directly on single observation at a time, 1-g to detect point anomaly. Whereas in the second stage, OCSVM learns from the constructed n-gram feature vectors based on the historical data to discover any collective anomaly where the pattern from the n-gram failed to conform to the expected normal pattern. The experimental result showed that our proposed dual-staged OCSVM is able to detect anomaly and collective anomalies effectively. Our model performance has attained remarkable result of about 99% in terms of F1-score. We also compared the performance of our OCSVM algorithm with other algorithms. Full article
(This article belongs to the Special Issue Transforming Future Cities: Smart City)
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Open AccessArticle
Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai
Electronics 2020, 9(5), 837; https://doi.org/10.3390/electronics9050837 - 19 May 2020
Cited by 2
Abstract
In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures [...] Read more.
In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience. Full article
(This article belongs to the Special Issue Transforming Future Cities: Smart City)
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Open AccessEditor’s ChoiceArticle
Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm
Electronics 2020, 9(3), 406; https://doi.org/10.3390/electronics9030406 - 28 Feb 2020
Cited by 7
Abstract
Due to the exponential increase in the human population of this bio-sphere, energy resources are becoming scarce. Because of the traditional methods, most of the generated energy is wasted every year in the distribution network and demand side. Therefore, researchers all over the [...] Read more.
Due to the exponential increase in the human population of this bio-sphere, energy resources are becoming scarce. Because of the traditional methods, most of the generated energy is wasted every year in the distribution network and demand side. Therefore, researchers all over the world have taken a keen interest in this issue and finally introduced the concept of the smart grid. Smart grid is an ultimate solution to all of the energy related problems of today’s modern world. In this paper, we have proposed a meta-heuristic optimization technique called the dragonfly algorithm (DA). The proposed algorithm is to a real-world problem of single and multiple smart homes. In our system model, two classes of appliances are considered; Shiftable appliances and Non-shiftable appliances. Shiftable appliances play a significant role in demand side load management because they can be scheduled according to real time pricing (RTP) signal from utility, while non-shiftable appliances are not much important in load management, as these appliances are fixed and cannot be scheduled according to RTP. On behalf of our simulation results, it can be concluded that our proposed algorithm DA has achieved minimum electricity cost with a tolerable waiting time. There is a trade-off between electricity cost and waiting time because, with a decrease in electricity cost, waiting time increases and vice versa. This trade-off is also obtained by our proposed algorithm DA. The stability of the grid is also maintained by our proposed algorithm DA because stability of the grid depends on peak-to-average ratio (PAR), while PAR is reduced by DA in comparison with an unscheduled case. Full article
(This article belongs to the Special Issue Transforming Future Cities: Smart City)
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Open AccessArticle
Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries
Electronics 2020, 9(1), 105; https://doi.org/10.3390/electronics9010105 - 06 Jan 2020
Cited by 6
Abstract
Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, [...] Read more.
Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS. Full article
(This article belongs to the Special Issue Transforming Future Cities: Smart City)
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