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Sustainability
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Technologies for Sustainability in Smart Cities
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Technologies for Sustainability in Smart Cities

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 9611

Special Issue Editors

Dr. Manuel Dominguez-Morales

E-Mail Website
Guest Editor
Computer Architecture and Technology Department, University of Seville
Interests: Neuromorphic Engineering;E-Health;Robotics;Computer Vision;Computer Architecture
Special Issues, Collections and Topics in MDPI journals
Prof. Ángel Jiménez-Fernández

E-Mail Website
Guest Editor
Computer Architecture and Technology department (ATC), University of Seville, 41012, Seville (Spain)
Interests: Embedded systems, IoT devices, embedded intelligence, neuromorphic engineering
Dr. Juan P. Domínguez-Morales

E-Mail Website
Guest Editor
Computer Architecture and Technology department (ATC), University of Seville, 41012 Seville, Spain
Interests: neuromorphic engineering; machine learning; deep learning; embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The lack of natural resources, overpopulation in urban areas and the pollution generated in them are worrying aspects that we are dealing with today. The population must be aware of contributing to avoid the disastrous consequences that these events can cause on the planet.

Usually, many of these problems are attributed to the technological expansion and its impact on society. But, as a matter of fact, technological evolution can help alleviate these effects if used properly.

Therefore, this special edition of the journal Sustainability is focused on the use of innovative technologies that, applied in cities, allow to automate and reduce the power consumption of daily activities, monitor traffic, monitor the quality of several environmental parameters and/or be able to analyze this data in order to offer recommendations to improve the parameters indicated before.

To this end, new technologies in the field of machine learning and IoT, among others, could represent new opportunities to improve sustainability.

Dr. Manuel Dominguez-Morales
Prof. Ángel Jiménez-Fernández
Dr. Juan P. Domínguez-Morales
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

  • Environmental wireless sensors networks
  • Big data analysis and machine learning for environmental data
  • IoT technologies
  • Low-power embedded monitoring systems
  • Traffic monitoring
  • Air and water quality assessment
  • Technologies for energy saving in cities

Published Papers (3 papers)

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Research

14 pages, 2260 KiB  
Article
A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations
by Amaro García-Suárez, José-Luis Guisado-Lizar, Fernando Diaz-del-Rio and Francisco Jiménez-Morales
Sustainability 2021, 13(10), 5421; https://0-doi-org.brum.beds.ac.uk/10.3390/su13105421 - 12 May 2021
Cited by 2 | Viewed by 2007
Abstract
We present a hybrid model combining cellular automata (CA) and agent-based modeling (ABM) to analyze the deployment of electric vehicle charging stations through microscopic traffic simulations. This model is implemented in a simulation tool called SIMTRAVEL, which allows combining electric vehicles (EVs) and [...] Read more.
We present a hybrid model combining cellular automata (CA) and agent-based modeling (ABM) to analyze the deployment of electric vehicle charging stations through microscopic traffic simulations. This model is implemented in a simulation tool called SIMTRAVEL, which allows combining electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) that navigate in a city composed of streets, avenues, intersections, roundabouts, and including charging stations (CSs). Each EV is modeled as an agent that incorporates complex behaviors, such as decisions about the route to destination or CS, when to drive to a CS, or which CS to choose. We studied three different CS arrangements for a synthetic city: a single large central CS, four medium sized distributed CSs or multiple small distributed CSs, with diverse amounts of traffic and proportions of EVs. The simulator output is found to be robust and meaningful and allows one to extract a first useful conclusion: traffic conditions that create bottlenecks around the CSs play a crucial role, leading to a deadlock in the city when the traffic density is above a certain critical level. Our results show that the best disposition is a distributed network, but it is fundamental to introduce smart routing measures to balance the distribution of EVs among CSs. Full article
(This article belongs to the Special Issue Technologies for Sustainability in Smart Cities)
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15 pages, 3869 KiB  
Article
Identifying Unwanted Conditions through Chaotic Area Determination in the Context of Indonesia’s Economic Resilience at the City Level
by Yuyun Hidayat, Titi Purwandari, Subiyanto and Sukono
Sustainability 2021, 13(9), 5183; https://0-doi-org.brum.beds.ac.uk/10.3390/su13095183 - 06 May 2021
Cited by 3 | Viewed by 1441
Abstract
The purpose of this research is to determine the unwanted condition as a strategic criterion in measuring the economic resilience of a city. A new approach in determining economic resilience was developed to overcome the weaknesses of the index method commonly used internationally. [...] Read more.
The purpose of this research is to determine the unwanted condition as a strategic criterion in measuring the economic resilience of a city. A new approach in determining economic resilience was developed to overcome the weaknesses of the index method commonly used internationally. Based on the output of this research, the development priority program for each city becomes distinctive depending on the status of the city’s economic resilience. Quality improvement programs are used for cities that do not have resilience and retention programs for cities that already have economic resilience. Five piecewise linear regression parameters are applied to identify a statistical model between Income per capita and Pc as a concern variable and modifier variable, and a Z. Model is tested massively involving all 514 cities in Indonesia from 2015 to 2019, covering the components of the modifier variable: local revenue (PAD), poverty, unemployment and concern variable; GRDP and population. The value of the Fraction of variance unexplained (FVU) of the model is 40%. This value is obtained using the Rosenbrock Pattern Search estimation method with a maximum number of iterations of 200 and a convergence criterion of 0.0001. The FVU area is a condition of uncertainty and unpredictability, so that people will avoid this area. This condition is chaotic and declared as an unwanted condition. The chaotic area is located in the value of UZ less than IDR 5,097,592 and Pc < Pc (UZ) = 27,816,310.68, and thus the coordinates of the chaotic boundary area is (5,097,592: 27,816,310.68). FVU as a chaotic area is used as the basis for stating whether or not a city falls into unwanted conditions. A city is claimed not to be economically resilient if the modifier variable Z is in a chaotic boundary. Full article
(This article belongs to the Special Issue Technologies for Sustainability in Smart Cities)
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25 pages, 4141 KiB  
Article
Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era
by Ana De Las Heras, Amalia Luque-Sendra and Francisco Zamora-Polo
Sustainability 2020, 12(22), 9320; https://0-doi-org.brum.beds.ac.uk/10.3390/su12229320 - 10 Nov 2020
Cited by 32 | Viewed by 5303
Abstract
The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these [...] Read more.
The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented. Full article
(This article belongs to the Special Issue Technologies for Sustainability in Smart Cities)
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