The Advances of Smart Services for the Creation of Adaptive Smart Areas in Sustainable Smart Cities

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

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 4372

Special Issue Editors

1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals
Systems and Information Technology Department, Universidad de Caldas, Manizales 170001, Caldas, Colombia
Interests: multiagent systems; machine learning; computational algorithms; artificial intelligence
Systems and Information Technology Department, Universidad de Caldas, Manizales 170001, Caldas, Colombia
Interests: cybersecurity; machine learning; bioinformatics
Prof. Dr. Omar Danilo Castrillón
E-Mail Website
Guest Editor
Department of Industrial Engineering, Universidad Nacional de Colombia Sede Manizales, Manizales 17001, Colombia
Interests: optimization; machine learning; flexible job shop
Prof. Dr. Marcelo López Trujillo
E-Mail Website
Guest Editor
Departamento de Sistemas e Informática, Universidad de Caldas, Manizales 170001, Colombia
Interests: smart cities; knowledge management
Dr. Oscar Cardona-Morales
E-Mail Website
Guest Editor
Electronics and Industrial Automation Department, Universidad Autónoma de Manizales, Manizales 170001, Colombia
Interests: digital image processing; artificial intelligence
BISITE Research Group, University of Salamanca, Edificio Multiusos I + D + I, 37007 Salamanca, Spain
Interests: artificial intelligence; multi agent systems; cloud computing and distributed systems; technology enhanced learning

Special Issue Information

Dear Colleagues,

Thanks to technological advances, recent years have seen cities undergo a profound transformation, with significant improvements in sustainability and in the services offered by local governments and other municipal entities. This transformation has resulted in a high investment in resources and research projects focused on the urban environment. However, most of the so-called smart cities are simply cities that have undertaken several smart projects. Given that, nowadays, vast amounts of data are continually generated, and the challenge of a city that strives to become smart lies in identifying intelligent and adaptive means of combining the generated information, so that valuable knowledge may be extracted. Sensorization has been integral to the collection of data. The knowledge extracted from the data analyzed by IoT and smart city platforms optimizes the governments’ decision making and resource consumption. This Special Issue invites researchers to submit high-quality original studies addressing the topic of Adaptive Smart Areas in Sustainable Smart Cities through the application of edge computing systems, hybrid computing service systems, federated learning, or artificial learning in multi-agent system architectures.

The Special Issue mainly receives papers from SSCt2023 (International Conference on Sustainable Smart Cities and Territories), 21–23 June 2023, Manizales, Colombia. Event link: https://www.mdpi.com/journal/electronics/events/15511.

Prof. Dr. Juan M. Corchado
Prof. Dr. Luis Fernando Castillo Ossa 
Prof. Dr. Gustavo Adolfo Isaza Echeverri 
Prof. Dr. Omar Danilo Castrillón
Prof. Dr. Marcelo López Trujillo
Dr. Oscar Cardona-Morales
Dr. Fernando De la Prieta 
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 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

  • smart cities and smart territories successful cases and challenges
  • smart homes and smart buildings
  • smart infrastructures (network, 5G, grids, lighting, water, and waste)
  • smart urban mobility and intelligent transportation systems
  • smart health and emergency management (epidemy control)
  • smart environments
  • smart travel and smart tourism
  • smart manufacturing and smart logistics
  • new retail and smart commerce
  • smart urban governance
  • eco-urbanism, urban resilience, and climate change mitigation and adaptation
  • energy and climate policy
  • intelligent traffic control
  • crowd behavior capturing and modelling and crowd management
  • human–machine interactions
  • artificial intelligence and machine learning
  • open data and big data analytics
  • data security and safety of blockchain
  • sensor‐driven analytics and services
  • context-aware systems

Published Papers (5 papers)

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Research

17 pages, 5454 KiB  
Article
Revolutionising the Quality of Life: The Role of Real-Time Sensing in Smart Cities
Electronics 2024, 13(3), 550; https://doi.org/10.3390/electronics13030550 - 30 Jan 2024
Viewed by 340
Abstract
To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data [...] Read more.
To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data visualisation tools that can adapt to changing conditions. An architecture was created and implemented that ensures secure and easy connectivity between various sources, such as a network of Internet of Things (IoT) devices, to merge with crowdsensing data and use them efficiently. In addition, we expanded the scope of our study to include the development of mobile and online applications, emphasizing the integration of autonomous and geo-surveillance. The main findings highlight the importance of sensor data in urban knowledge. Their incorporation via Tepresentational State Transfer (REST) Application Programming Interface (APIs) improves data access and informed decision-making, and dynamic data visualisation provides better insights. The geofencing of the application encourages community participation in urban planning and resource allocation, supporting sustainable urban innovation. Full article
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22 pages, 5357 KiB  
Article
Ontological Modeling and Clustering Techniques for Service Allocation on the Edge: A Comprehensive Framework
Electronics 2024, 13(3), 477; https://doi.org/10.3390/electronics13030477 - 23 Jan 2024
Viewed by 310
Abstract
Nowadays, we are in a world of large amounts of heterogeneous devices with varying computational resources, ranging from small devices to large supercomputers, located on the cloud, edge or other abstraction layers in between. At the same time, software tasks need to be [...] Read more.
Nowadays, we are in a world of large amounts of heterogeneous devices with varying computational resources, ranging from small devices to large supercomputers, located on the cloud, edge or other abstraction layers in between. At the same time, software tasks need to be performed. They have specific computational or other types of requirements and must also be executed at a particular physical location. Moreover, both services and devices may change dynamically. In this context, methods are needed to effectively schedule efficient allocations of services to computational resources. In this article, we present a framework to address this problem. Our proposal first uses knowledge graphs for describing software requirements and the availability of resources for services and computing nodes, respectively. To this end, we proposed an ontology that extends our previous work. Then, we proposed a hierarchical filtering approach to decide the best allocation of services to computational nodes. We carried out simulations to evaluate four different clustering strategies. The results showed different performances in terms of the number of allocated services and node overload. Full article
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21 pages, 1275 KiB  
Article
Optimization of Rural Demand-Responsive Transportation through Transfer Point Allocation
Electronics 2023, 12(22), 4684; https://doi.org/10.3390/electronics12224684 - 17 Nov 2023
Viewed by 523
Abstract
Rural mobility has a lack of innovative proposals in contrast with its urban counterpart. This research aims to bring solutions that ease the implementation of reliable and flexible rural transportation. Demand-responsive transportation is chosen to develop a public transportation service providing interurban trips [...] Read more.
Rural mobility has a lack of innovative proposals in contrast with its urban counterpart. This research aims to bring solutions that ease the implementation of reliable and flexible rural transportation. Demand-responsive transportation is chosen to develop a public transportation service providing interurban trips among several rural settlements. Given the characteristics of rural displacement demand, a novel approach is introduced to optimize the service’s economic costs: the dynamic transfer point allocation. The problem is fully formulated and an architecture is introduced describing the workflow of the whole system. Data from an interurban bus transportation service are used to build a case study of a rural area of Valencia, Spain, and develop several examples illustrating the benefits of the proposed approach. The results reveal that the dynamic creation of transfer points can simplify the transportation fleet’s itineraries and boost the amount of served travel requests. Finally, a discussion of the benefits and dangers of flexible features in rural transportation is developed, underscoring the need to achieve a balance between dynamic operation and service quality. Full article
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17 pages, 2741 KiB  
Article
EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification
Electronics 2023, 12(21), 4432; https://doi.org/10.3390/electronics12214432 - 27 Oct 2023
Viewed by 911
Abstract
Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive [...] Read more.
Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios. Full article
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31 pages, 4542 KiB  
Article
Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort
Electronics 2023, 12(19), 4179; https://doi.org/10.3390/electronics12194179 - 09 Oct 2023
Cited by 3 | Viewed by 1304
Abstract
Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy [...] Read more.
Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment. Full article
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