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Sustainable Smart Cities and Societies Using Emerging Technologies

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 77981

Special Issue Editors

Department of Information and Analytical Security Systems, Southern Federal University, 344006 Rostov-on-Don, Russia
Interests: artificial intelliegence; network reliability; network management; sustainable development
Special Issues, Collections and Topics in MDPI journals
Director of the Institute of Computer Technology and Information Security, Southern Federal University, 344006 Rostov-on-Don, Russia
Interests: control theory; mechatronics; robotics systems; synergy; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals
Department of Information and Analytical Security Systems, Southern Federal University, 344006 Rostov-on-Don, Russia
Interests: theory of sociosemantic knowledge networks; graph mining and social network analysis; theory of fuzzy graphs and hypergaphs

Special Issue Information

Dear Colleagues,

Sustainable Smart Cities and Artificial Intelligence offer an intensive evaluation of how sustainable Smart City establishments are made at different scales through automated means of thinking, for instance, geospatial information, data examination, data portrayal, clever related things, and quick natural frameworks handiness. Progress in electronic thinking brings us closer to making a consistently reproducable model of human-made and trademark structures, from urban regions and transportation establishments to utility frameworks. This continuous living model empowers us to  manage and improve these working structures, making them dynamically observant. Smart Cities and Artificial Intelligence providea a multidisciplinary procedure, using speculative and applied pieces of information, for the evaluation of Smart City applications.

City dwellers are naturally turning towards explicit advances to convey issues related to society, science, and emerging technology. The idea of joining of sensors and Big Data through the Internet of Things (IoT) in the context of Sustainable Smart Cities bolsters their applications. This surge of data brings new possibilities in the intelligent design and management of smart cities towards a safe, comfortable, and inclusive environment to live and work.

While Big Data prepared through Artificial Intelligence (AI) can enhance aspects of urban life, sensibility and liveability estimations ought not to be ignored. As shown by the data dispersed by the United Nations (UN), the global population will reach up to 9.7 billion before the end of 2050. It is presumed that, for all intents and purposes, 70% of those people will be located in urban regions, with various urban zones home to over 10m tenants. As this number increases, we will encounter challenges with respect to resource management, ensuring that people have enough and that living conditions are maintained. Other challenges will be found in sanitation, traffic mitigation, and infrastructure.

Regardless, large parts of these issues can be addressed by the use of AI-enabled IoT. Using mechanical progress to support the experience for inhabitants of urban regions can increase their comfort and security. Therein lies the benefit of Smart Cities. A sustainable Smart City is a city that uses information to improve the quality of urban essentials such as sanitation and transport, thus reducing waste and excessive expense.

Dr. Ashutosh Sharma
Prof. Dr. Gennady E. Veselov
Dr. Alexey Tselykh
Prof. Dr. Byung-Gyu Kim
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

  • GIS mapping
  • smart classrooms
  • smart agriculture
  • smart industries
  • smart parking
  • smart healthcare
  • smart drainage system
  • smart waste management
  • sustainable urban development
  • virtual reality
  • trust management
  • artificial intelligence
  • security
  • privacy

Published Papers (22 papers)

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Research

22 pages, 3894 KiB  
Article
Driver-Centric Urban Route Planning: Smart Search for Parking
by Jasmin Ćelić, Bia Mandžuka, Vinko Tomas and Frane Tadić
Sustainability 2024, 16(2), 856; https://doi.org/10.3390/su16020856 - 19 Jan 2024
Viewed by 565
Abstract
With urbanization, there is a growing need for mobility. Challenges for urban drivers include finding available parking spaces. Searching for a parking spot can be a frustrating experience, often time consuming and costly. Also, the increasing number of vehicles on the roads leads [...] Read more.
With urbanization, there is a growing need for mobility. Challenges for urban drivers include finding available parking spaces. Searching for a parking spot can be a frustrating experience, often time consuming and costly. Also, the increasing number of vehicles on the roads leads to an additional strain on traffic flow, while the search for parking spaces lowers the level of service. In inner cities, vehicles circulate in search of an available parking space, leading to an increase in travel time, fuel consumption, pollutant emissions, and a decrease in traffic safety. The search for a free parking space generates a significant increase in traffic in urban areas. To solve the parking search problem, it is necessary to develop certain strategies and measures that minimize circling in search of a parking space. The implementation of intelligent transportation systems stands out. By applying intelligent transport systems, drivers are provided with information about free parking spaces, which reduces the circulation of vehicles in search of free parking. Although initially ITS systems mainly provided services for closed parking lots and garages, with the further development of the system, the service was extended to street parking lots or open-type parking lots. These measures not only solve traffic challenges but also promote sustainability in urban areas. This article analyzes the effect of a cooperative approach of guiding vehicles to available parking spaces compared to a standard model of searching for an available parking space. Within the framework of the advanced model for searching for available space, four parking demand scenarios were defined and simulated. Based on the created traffic simulation, a comparative analysis was made between the classic and cooperative approach, while the primary differences are manifested in the load of the traffic flow A simulation model was developed using the road network from the urban center of Zagreb. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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19 pages, 553 KiB  
Article
Exploring the Success Factors of Smart City Adoption via Structural Equation Modeling
by Tayseer Alkdour, Mohammed Amin Almaiah, Rima Shishakly, Abdalwali Lutfi and Mahmoud Alrawad
Sustainability 2023, 15(22), 15915; https://doi.org/10.3390/su152215915 - 14 Nov 2023
Viewed by 709
Abstract
This study investigated the roles of security and technological factors in the adoption of smart cities, with the aim of developing a deeper understanding of the key aspects of the successful adoption of smart cities in Jordanian traditional cities. This study developed a [...] Read more.
This study investigated the roles of security and technological factors in the adoption of smart cities, with the aim of developing a deeper understanding of the key aspects of the successful adoption of smart cities in Jordanian traditional cities. This study developed a conceptual model to investigate the importance of security and technological factors in the adoption of smart cities. The proposed model was tested using the structural equation modeling method after collecting data from ICT experts. The findings of the study revealed that perceived security, perceived trust, and service quality play pivotal roles in enhancing the adoption of smart city services. Moreover, the results indicated that information security and information privacy positively impact intentions toward adopting smart city services. These research findings provide valuable insights into the critical factors that can drive the adoption of smart city services. Policymakers and academics could utilize this knowledge to devise and implement new strategies aimed at increasing the adoption of smart city services. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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25 pages, 3667 KiB  
Article
A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities
by Amit Sharma, Ashutosh Sharma, Polina Nikashina, Vadim Gavrilenko, Alexey Tselykh, Alexander Bozhenyuk, Mehedi Masud and Hossam Meshref
Sustainability 2023, 15(15), 11893; https://doi.org/10.3390/su151511893 - 02 Aug 2023
Cited by 6 | Viewed by 2445
Abstract
Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments [...] Read more.
Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments is what significantly contributes to the increase of traffic congestion. This interconnectedness poses a significant challenge to increasing prediction accuracy. To address this, we propose a novel approach based on Deep Graph Neural Networks (DGNNs), which represent the connectedness of road sections as a graph using Graph Neural Networks (GNNs). In this study, we implement the proposed approach, called STGGAN, for real-time traffic-speed estimation using two different actual traffic datasets: PeMSD4 and PeMSD8. The experimental results validate the prediction accuracy values of 96.67% and 98.75% for the PeMSD4 and PeMSD8 datasets, respectively. The computation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) also shows a progressive decline in these error values with increasing iteration count, demonstrating the success of the suggested technique. To confirm the feasibility, reliability, and applicability of the suggested STGGAN technique, we also perform a comparison analysis, including several statistical, analytical, and machine-learning- and deep-learning-based approaches. Our work contributes significantly to the field of traffic-speed estimation by considering the structure and characteristics of road networks through the implementation of DGNNs. The proposed technique trains a neural network to accurately predict traffic flow using data from the entire road network. Additionally, we extend DGNNs by incorporating Gated Graph Attention Network (GGAN) blocks, enabling the modification of the input and output to sequential graphs. The prediction accuracy of the proposed model based on DGNNs is thoroughly evaluated through extensive tests on real-world datasets, providing a comprehensive comparison with existing state-of-the-art models for traffic-flow forecasting. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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23 pages, 3286 KiB  
Article
Assessing Coastal Land-Use and Land-Cover Change Dynamics Using Geospatial Techniques
by Anindita Nath, Bappaditya Koley, Tanupriya Choudhury, Subhajit Saraswati, Bidhan Chandra Ray, Jung-Sup Um and Ashutosh Sharma
Sustainability 2023, 15(9), 7398; https://doi.org/10.3390/su15097398 - 29 Apr 2023
Cited by 6 | Viewed by 2146
Abstract
Geospatial techniques can be used to assess the dynamic conditions of coastal land use and land cover in order to make informed decisions about future management strategies for sustainable development through a combination of remote sensing data with field observations of shoreline characteristics [...] Read more.
Geospatial techniques can be used to assess the dynamic conditions of coastal land use and land cover in order to make informed decisions about future management strategies for sustainable development through a combination of remote sensing data with field observations of shoreline characteristics along coastlines worldwide. Geospatial techniques offer an invaluable method for analyzing complex coastal systems at multiple scales. The coastal land use and land cover from the Subarnarekha (Orissa) to the Rasulpur estuaries (West Bengal) along the Bay of Bengal are dynamically modified by a complex interaction between land and sea. This is due to various dominating factors of physical and anthropogenic activities, which cause changes in the landscape. The main objective of this study was to identify the periodical transformation and changes in land-use/land-cover (LULC) features by the USGS-LULC classification method using a maximum-likelihood classifier (MLC) algorithm and satellite images for the period 1975–2018. The entire study area was divided into three ‘littoral zones’ (LZs). This will help in understanding how LULC has changed over time, as well as providing insight into human activities impacting on coastal environments. This study focused on five features selected for LULC classification, namely, built-up, vegetation, soil, sand and shallow-water areas. The purpose of this study was to investigate human encroachment near shore areas as well as the transformation of soil and sand into built-up areas over a 43-year period from 1975 to 2018 using geospatial techniques. To estimate the changes in the areas, a geodatabase was prepared for each LULC feature. Finally, statistical analysis was performed on all available datasets, which allowed the researchers to identify trends in land-cover change from 1975–2018 within each category, such as increasing deforestation and urbanization rates due to increased population growth. The results of the study show the expansion of shallow-water areas, which is one of the major factors influencing coastal erosion. Maximum shallow-water-level enhancement was observed in LZ I and LZ II. In LZ I, shallow water increased from 1 km2 to 4.55 km2. In LZ II, the initial 1.7 km2 shallow-water area increased to 13.56 km2, meaning an increase of 11.86 km2 in shallow-water zones. A positive change was noticed in vegetation area, which increased from 2.82% (4.13 km2) to 15.46% (22.07 km2). Accuracy assessment was applied for all classified images, and more than 85% accuracy was considered for the overall accuracy assessment. Finally, Kappa coefficient statistics were adopted to complete the accuracy analysis, and 80% or more than 80% accuracy was obtained for all classified images. This information can also help inform policy makers about potential environmental impacts associated with certain activities, such as coastal development and agricultural expansion, so that appropriate steps can be taken towards mitigating these impacts before it is too late. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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14 pages, 3109 KiB  
Article
Energy Analysis-Based Cyber Attack Detection by IoT with Artificial Intelligence in a Sustainable Smart City
by D. Prabakar, M. Sundarrajan, R. Manikandan, N. Z. Jhanjhi, Mehedi Masud and Abdulmajeed Alqhatani
Sustainability 2023, 15(7), 6031; https://doi.org/10.3390/su15076031 - 30 Mar 2023
Cited by 1 | Viewed by 1728
Abstract
Cybersecurity continues to be a major issue for all industries engaged in digital activity given the cyclical surge in security incidents. Since more Internet of Things (IoT) devices are being used in homes, offices, transportation, healthcare, and other venues, malicious attacks are happening [...] Read more.
Cybersecurity continues to be a major issue for all industries engaged in digital activity given the cyclical surge in security incidents. Since more Internet of Things (IoT) devices are being used in homes, offices, transportation, healthcare, and other venues, malicious attacks are happening more frequently. Since distance between IoT as well as fog devices is closer than distance between IoT devices as well as the cloud, attacks can be quickly detected by integrating fog computing into IoT. Due to the vast amount of data produced by IoT devices, ML is commonly employed for attack detection. This research proposes novel technique in cybersecurity-based network traffic analysis and malicious attack detection using IoT artificial intelligence techniques for a sustainable smart city. A traffic analysis has been carried out using a kernel quadratic vector discriminant machine which enhances the data transmission by reducing network traffic. This enhances energy efficiency with reduced traffic. Then, the malicious attack detection is carried out using adversarial Bayesian belief networks. The experimental analysis has been carried out in terms of throughput, data traffic analysis, end-end delay, packet delivery ratio, energy efficiency, and QoS. The proposed technique attained a throughput of 98%, data traffic analysis of 74%, end-end delay of 45%, packet delivery ratio of 92%, energy efficiency of 92%, and QoS of 79%. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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19 pages, 4693 KiB  
Article
Secure and Fast Emergency Road Healthcare Service Based on Blockchain Technology for Smart Cities
by Amel Ksibi, Halima Mhamdi, Manel Ayadi, Latifah Almuqren, Mohammed S. Alqahtani, Mohd Dilshad Ansari, Ashutosh Sharma and Sakli Hedi
Sustainability 2023, 15(7), 5748; https://doi.org/10.3390/su15075748 - 25 Mar 2023
Cited by 7 | Viewed by 1661
Abstract
Road accidents occur everywhere in the world and the numbers of people dead or injured increase from time to time. People hope that emergency vehicles and medical staff will arrive as soon as possible at the scene of the accident. The development of [...] Read more.
Road accidents occur everywhere in the world and the numbers of people dead or injured increase from time to time. People hope that emergency vehicles and medical staff will arrive as soon as possible at the scene of the accident. The development of recent technologies such as the Internet of Things (IoT) allows us to find solutions to ensure rapid movement by road in emergencies. Integrating the healthcare sector and smart vehicles, IoT ensures this objective. This integration gives rise to two paradigms: the Internet of Vehicles (IoV) and the Internet of Medical Things (IoMT), where smart devices collect medical data from patients and transmit them to medical staff in real time. These data are extremely sensitive and must be managed securely. This paper proposes a system design that brings together the three concepts of Blockchain technology (BC), IoMT and IoV to address the problem mentioned above. The designed system is composed of three main parts: a list of hospitals, patient electronic medical record (EMR) and a network of connected ambulances. It allows the road user in the case of an accident to report their position to the nearby health services and ambulances. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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14 pages, 2280 KiB  
Article
Emerging Technologies for the Production of In Vitro Raised Quality Rich Swertia chirayita by Using LED Lights
by Rolika Gupta and Hemant Sood
Sustainability 2023, 15(2), 1714; https://doi.org/10.3390/su15021714 - 16 Jan 2023
Cited by 3 | Viewed by 1469
Abstract
The major bioactive compounds in S. chirayita are amarogentin (most bitter compound) and mangiferin, which contribute to its medicinal value due to its antidiabetic, anticancer, antimicrobial and antimalarial properties. In this study, we developed a light emitting diode (LED)–based culture setup as an [...] Read more.
The major bioactive compounds in S. chirayita are amarogentin (most bitter compound) and mangiferin, which contribute to its medicinal value due to its antidiabetic, anticancer, antimicrobial and antimalarial properties. In this study, we developed a light emitting diode (LED)–based culture setup as an alternative to the existing white fluorescent lamps (WFL) used as a light source in the tissue culture conditions of the plants. The in-vitro raised plants of S. chirayita cultivated under LED lights showed a higher accumulation of shoot biomass and secondary metabolites as compared with plants growing under WFL. In the LED lights experiment, red LED accounted forthe maximum biomass accumulation (3.56 ± 0.04 g L−1), and blue LED accounted for the accumulated maximum content of amarogentin (8.025 ± 0.04 µg mg−1 DW), total phenolics (22.33 ± 1.05 mg GA g−1 DW), total flavonoids (29 ± 1.03 mg QE g−1 DW) and DPPH radical scavenging activity (50.40 ± 0.16%) in comparison with other light conditions. From the findings, we propose LED lightning as a more sustainable, eco-friendly and reliable source for the enormous production of quality rich secondary metabolites in shoot cultures of S. chirayita than the traditionally used fluorescent lights. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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17 pages, 2433 KiB  
Article
Crop Type Prediction: A Statistical and Machine Learning Approach
by Bikram Pratim Bhuyan, Ravi Tomar, T. P. Singh and Amar Ramdane Cherif
Sustainability 2023, 15(1), 481; https://doi.org/10.3390/su15010481 - 28 Dec 2022
Cited by 9 | Viewed by 2961
Abstract
Farmers’ ability to accurately anticipate crop type is critical to global food production and sustainable smart cities since timely decisions on imports and exports, based on precise forecasts, are crucial to the country’s food security. In India, agriculture and allied sectors constitute the [...] Read more.
Farmers’ ability to accurately anticipate crop type is critical to global food production and sustainable smart cities since timely decisions on imports and exports, based on precise forecasts, are crucial to the country’s food security. In India, agriculture and allied sectors constitute the country’s primary source of revenue. Seventy percent of the country’s rural residents are small or marginal agriculture producers. Cereal crops such as rice, wheat, and other pulses make up the bulk of India’s food supply. Regarding cultivation, climate and soil conditions play a vital role. Information is of utmost need in predicting which crop is best suited given the soil and climate. This paper provides a statistical look at the features and indicates the best crop type on the given features in an Indian smart city context. Machine learning algorithms like k-NN, SVM, RF, and GB trees are examined for crop-type prediction. Building an accurate crop forecast system required high accuracy, and the GB tree technique provided that. It outperforms all the classification algorithms with an accuracy of 99.11% and an F1-score of 99.20%. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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36 pages, 568 KiB  
Article
A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)
by Bikram Pratim Bhuyan, Ravi Tomar and Amar Ramdane Cherif
Sustainability 2022, 14(22), 15249; https://doi.org/10.3390/su142215249 - 17 Nov 2022
Cited by 9 | Viewed by 3771
Abstract
Urban agriculture is the practice of growing food inside the city limits. Due to the exponential amount of data generated by information and technology-based farm management systems, we need proper methods to represent the data. The branch of artificial intelligence known as “knowledge [...] Read more.
Urban agriculture is the practice of growing food inside the city limits. Due to the exponential amount of data generated by information and technology-based farm management systems, we need proper methods to represent the data. The branch of artificial intelligence known as “knowledge representation and reasoning” is devoted to the representation of information about the environment in a way where a computer system can utilise it to accomplish difficult problems. This research is an extensive survey of the knowledge representation techniques used in smart agriculture, and specifically in the urban agricultural domain. Relevant articles on the knowledge base are extracted from the retrieved set to study the fulfillment of the criteria of the system. Various interesting findings were observed after the review. Spatial–temporal characteristics were rarely approached. A generalised representation technique to include all domains in agriculture is another issue. Finally, proper validation technique is found to be missing in such an ontology. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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19 pages, 655 KiB  
Article
Predicting Student Attrition in Higher Education through the Determinants of Learning Progress: A Structural Equation Modelling Approach
by Pavlos Nikolaidis, Maizatul Ismail, Liyana Shuib, Shakir Khan and Gaurav Dhiman
Sustainability 2022, 14(20), 13584; https://doi.org/10.3390/su142013584 - 20 Oct 2022
Cited by 5 | Viewed by 3582
Abstract
Higher education policies are designed to facilitate students’ learning progression and academic success. Following Tinto’s integration theory and Bean’s attrition model, this study proposes a research model to investigate whether students prone to attrition can be pre-emptively identified through self-evaluating academic factors contributing [...] Read more.
Higher education policies are designed to facilitate students’ learning progression and academic success. Following Tinto’s integration theory and Bean’s attrition model, this study proposes a research model to investigate whether students prone to attrition can be pre-emptively identified through self-evaluating academic factors contributing to their learning progress. Theoretically, the learning progress is identified with student success, represented by factors amenable to intervention including the interaction with peers and instructors, teaching effectiveness, exam scores, absenteeism, students’ effort, and academic course-related variables. An exploratory and confirmatory factor analysis of 530 undergraduate students revealed that the indicators of learning progress in such students were channeled into two constructs. The results indicated that the teacher effectiveness and learning materials contributed most to the learning progress. Structural equation modelling revealed that the learning progress variables have a significant impact on students’ attrition status. A multi-group analysis confirmed the academic semesters to be a moderator in the mediating effects of the students’ grade point average (GPA). This model functions as a framework to design a student-oriented learning system promoting students’ learning experience and academic success. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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65 pages, 22666 KiB  
Article
Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling
by Eman Alqahtani, Nourah Janbi, Sanaa Sharaf and Rashid Mehmood
Sustainability 2022, 14(20), 13534; https://doi.org/10.3390/su142013534 - 19 Oct 2022
Cited by 14 | Viewed by 4562
Abstract
Homes are the building block of cities and societies and therefore smart homes are critical to establishing smart living and are expected to play a key role in enabling smart, sustainable cities and societies. The current literature on smart homes has mainly focused [...] Read more.
Homes are the building block of cities and societies and therefore smart homes are critical to establishing smart living and are expected to play a key role in enabling smart, sustainable cities and societies. The current literature on smart homes has mainly focused on developing smart functions for homes such as security and ambiance management. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, and the roles of smart homes that will serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public, using over a hundred thousand research papers and nearly a million tweets. We developed a methodology using deep learning, natural language processing (NLP), and big data analytics methods (BERT and other machine learning methods) and applied it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100 s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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18 pages, 294 KiB  
Article
Stress, Anxiety, and Depression in Pre-Clinical Medical Students: Prevalence and Association with Sleep Disorders
by Fahad Abdulaziz Alrashed, Abdulrahman M. Alsubiheen, Hessah Alshammari, Sarah Ismail Mazi, Sara Abou Al-Saud, Samha Alayoubi, Shaji John Kachanathu, Ali Albarrati, Mishal M. Aldaihan, Tauseef Ahmad, Kamran Sattar, Shakir Khan and Gaurav Dhiman
Sustainability 2022, 14(18), 11320; https://doi.org/10.3390/su141811320 - 09 Sep 2022
Cited by 12 | Viewed by 4127
Abstract
Our aim was to assess sleep quality in different subgroups of preclinical medical students, and then to identify specific lifestyle factors, academic and social factors as well as Corona virus related factors that were associated with poor sleeping quality and poor psychological health. [...] Read more.
Our aim was to assess sleep quality in different subgroups of preclinical medical students, and then to identify specific lifestyle factors, academic and social factors as well as Corona virus related factors that were associated with poor sleeping quality and poor psychological health. Study participants were all medical students at King Saud University of Medical Sciences in the first and second years (648 students), and the study was conducted from December 2021 to January 2022. We administered the survey on paper as well as online. We used three types of questionnaires in this study. The first was a self-administered questionnaire, the second was a validated Insomnia Severity Index (ISI) for finding sleeping problems, and the third was a validated DASS 10 for determining Depression, Anxiety, and Stress. A total of 361 pre-clinical medical students consisted of 146 (40.4%) males and 215 (59.5%) females. The majority of the students, 246 (68.1%), were in their second year. Furthermore, in the current study, students who had poor academic performance (15.8%), satisfactory academic performance (21.3%), or good academic performance (30.7%) had significant sleeping problems found (χ2 = 19.4; p = 0.001), among them poor academic performance students 21.6%, satisfactory academic performance students (29.3%), and good academic performance students (29.3%) had moderate to severe levelled sleeping problems. Similarly, poor, satisfactory, and good academic performers experienced the highest levels of anxiety (poor = 21.5%; satisfactory = 22.1%; and good = 22.8%); stress (poor = 22.4%; satisfactory = 25.2%; and good = 22.4%); and depression (poor = 40.5%; satisfactory = 40.5%; and good = 11.9%). The majority of students (64.8%) reported that during the pandemic crisis their anxiety levels were high. Additionally, students reported significantly high sleeping issues (χ2 = 10.6; p = 0.001) and also serious psychological issues (Anxiety = 34.9 (0.000); Stress = 32.5 (0.000); and Depression = 5.42 (0.01)). There was a high prevalence of sleep issues, anxiety, stress, and depression among the pre-clinical medical students, with significantly higher sleeping disorders, anxiety, stress, and depression levels among those medical students who struggle with their academic performances, poor lifestyle factor, and poor Social and COVID management. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
32 pages, 14607 KiB  
Article
An Autonomous Framework for Real-Time Wrong-Way Driving Vehicle Detection from Closed-Circuit Televisions
by Pintusorn Suttiponpisarn, Chalermpol Charnsripinyo, Sasiporn Usanavasin and Hiro Nakahara
Sustainability 2022, 14(16), 10232; https://doi.org/10.3390/su141610232 - 17 Aug 2022
Viewed by 1511
Abstract
Around 1.3 million people worldwide die each year because of road traffic crashes. There are many reasons which cause accidents, and driving in the wrong direction is one of them. In our research, we developed an autonomous framework called WrongWay-LVDC that detects wrong-way [...] Read more.
Around 1.3 million people worldwide die each year because of road traffic crashes. There are many reasons which cause accidents, and driving in the wrong direction is one of them. In our research, we developed an autonomous framework called WrongWay-LVDC that detects wrong-way driving vehicles from closed-circuit television (CCTV) videos. The proposed WrongWay-LVDC provides several helpful features such as lane detection, correct direction validation, detecting wrong-way driving vehicles, and image capturing features. In this work, we proposed three main contributions: first, the improved algorithm for road lane boundary detection on CCTV (called improved RLB-CCTV) using the image processing technique. Second is the Distance-Based Direction Detection (DBDD) algorithm that uses the deep learning method, where the system validates and detects wrong-driving vehicles. Lastly, the Inside Boundary Image (IBI) capturing feature algorithm captures the most precise shot of the wrong-way-of-driving vehicles. As a result, the framework can run continuously and output the reports for vehicles’ driving behaviors in each area. The accuracy of our framework is 95.23%, as we tested with several CCTV videos. Moreover, the framework can be implemented on edge devices with real-time speed for functional implementation and detection in various areas. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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21 pages, 2224 KiB  
Article
Smart Waste Management and Classification Systems Using Cutting Edge Approach
by Sehrish Munawar Cheema, Abdul Hannan and Ivan Miguel Pires
Sustainability 2022, 14(16), 10226; https://doi.org/10.3390/su141610226 - 17 Aug 2022
Cited by 19 | Viewed by 17247
Abstract
With a rapid increase in population, many problems arise in relation to waste dumps. These emits hazardous gases, which have negative effects on human health. The main issue is the domestic solid waste collection, management, and classification. According to studies, in America, nearly [...] Read more.
With a rapid increase in population, many problems arise in relation to waste dumps. These emits hazardous gases, which have negative effects on human health. The main issue is the domestic solid waste collection, management, and classification. According to studies, in America, nearly 75% of waste can be recycled, but there is a lack of a proper real-time waste-segregating mechanism, due to which only 30% of waste is being recycled at present. To maintain a clean and green environment, we need a smart waste management and classification system. To tackle the above-highlighted issue, we propose a real-time smart waste management and classification mechanism using a cutting-edge approach (SWMACM-CA). It uses the Internet of Things (IoT), deep learning (DL), and cutting-edge techniques to classify and segregate waste items in a dump area. Moreover, we propose a waste grid segmentation mechanism, which maps the pile at the waste yard into grid-like segments. A camera captures the waste yard image and sends it to an edge node to create a waste grid. The grid cell image segments act as a test image for trained deep learning, which can make a particular waste item prediction. The deep-learning algorithm used for this specific project is Visual Geometry Group with 16 layers (VGG16). The model is trained on a cloud server deployed at the edge node to minimize overall latency. By adopting hybrid and decentralized computing models, we can reduce the delay factor and efficiently use computational resources. The overall accuracy of the trained algorithm is over 90%, which is quite effective. Therefore, our proposed (SWMACM-CA) system provides more accurate results than existing state-of-the-art solutions, which is the core objective of this work. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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18 pages, 12084 KiB  
Article
Establishing an Intelligent Emotion Analysis System for Long-Term Care Application Based on LabVIEW
by Kai-Chao Yao, Wei-Tzer Huang, Teng-Yu Chen, Cheng-Chun Wu and Wei-Sho Ho
Sustainability 2022, 14(14), 8932; https://doi.org/10.3390/su14148932 - 21 Jul 2022
Cited by 3 | Viewed by 1901
Abstract
In this study, the authors implemented an intelligent long-term care system based on deep learning techniques, using an AI model that can be integrated with the Lab’s Virtual Instrumentation Engineering Workbench (LabVIEW) application for sentiment analysis. The input data collected is a database [...] Read more.
In this study, the authors implemented an intelligent long-term care system based on deep learning techniques, using an AI model that can be integrated with the Lab’s Virtual Instrumentation Engineering Workbench (LabVIEW) application for sentiment analysis. The input data collected is a database of numerous facial features and environmental variables that have been processed and analyzed; the output decisions are the corresponding controls for sentiment analysis and prediction. Convolutional neural network (CNN) is used to deal with the complex process of deep learning. After the convolutional layer simplifies the processing of the image matrix, the results are computed by the fully connected layer. Furthermore, the Multilayer Perceptron (MLP) model embedded in LabVIEW is constructed for numerical transformation, analysis, and predictive control; it predicts the corresponding control of emotional and environmental variables. Moreover, LabVIEW is used to design sensor components, data displays, and control interfaces. Remote sensing and control is achieved by using LabVIEW’s built-in web publishing tools. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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14 pages, 2399 KiB  
Article
A Fog-Cluster Based Load-Balancing Technique
by Prabhdeep Singh, Rajbir Kaur, Junaid Rashid, Sapna Juneja, Gaurav Dhiman, Jungeun Kim and Mariya Ouaissa
Sustainability 2022, 14(13), 7961; https://doi.org/10.3390/su14137961 - 29 Jun 2022
Cited by 17 | Viewed by 2499
Abstract
The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first [...] Read more.
The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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16 pages, 4335 KiB  
Article
Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture
by Kanwalpreet Kour, Deepali Gupta, Kamali Gupta, Sapna Juneja, Manjit Kaur, Amal H. Alharbi and Heung-No Lee
Sustainability 2022, 14(9), 5607; https://doi.org/10.3390/su14095607 - 06 May 2022
Cited by 15 | Viewed by 2279
Abstract
Saffron, also known as “the golden spice”, is one of the most expensive crops in the world. The expensiveness of saffron comes from its rarity, the tedious harvesting process, and its nutritional and medicinal value. Different countries of the world are making great [...] Read more.
Saffron, also known as “the golden spice”, is one of the most expensive crops in the world. The expensiveness of saffron comes from its rarity, the tedious harvesting process, and its nutritional and medicinal value. Different countries of the world are making great economic growth due to saffron export. In India, it is cultivated mostly in regions of Kashmir owing to its climate and soil composition. The economic value generated by saffron export can be increased manyfold by studying the agronomical factors of saffron and developing a model for artificial cultivation of saffron in any season and anywhere by monitoring and controlling the conditions of its growth. This paper presents a detailed study of all the agronomical variables of saffron that have a direct or indirect impact on its growth. It was found that, out of all the agronomical variables, the important ones having an impact on growth include corm size, temperature, water availability, and minerals. It was also observed that the use of IoT for the sustainable cultivation of saffron in smart cities has been discussed only by very few research papers. An IoT-based framework has also been proposed, which can be used for controlling and monitoring all the important growth parameters of saffron for its cultivation. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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16 pages, 1765 KiB  
Article
Data Intelligence in Public Transportation: Sustainable and Equitable Solutions to Urban Modals in Strategic Digital City Subproject
by Luis André Wernecke Fumagalli, Denis Alcides Rezende and Thiago André Guimarães
Sustainability 2022, 14(8), 4683; https://doi.org/10.3390/su14084683 - 14 Apr 2022
Cited by 3 | Viewed by 2508
Abstract
Transport infrastructure investments must be linked to the public transport demand strategically. User behavior and decision-making process bring several possible alternative transportation options due to a series of factors that define it. Municipalities must manage these factors to promote equal and sustainable transport [...] Read more.
Transport infrastructure investments must be linked to the public transport demand strategically. User behavior and decision-making process bring several possible alternative transportation options due to a series of factors that define it. Municipalities must manage these factors to promote equal and sustainable transport solutions through urban infrastructure, public transport competitiveness, and attractiveness, and fossil fuels use and pricing policy. The research objective is to determine these factors to monitor and use them for citizens’ benefit by means of analytical tools and methods to gain a superior knowledge of reality with a focus on improving investments and services in an agile and efficient manner. Methodologically, the number of passengers of main Curitiba’s (Brazil) bus rapid transit (BRT) lines is operated in two linear regression models combined with the number of private vehicles, public transport fare, and fuel price for the period between January/2010 and December/2019. Research analysis indicates direct causal relationships between the studied factors and that the necessary data for decision-making is available in the government information systems. In conclusion, urban management and strategic digital city project can be more balanced and assertive in transport infrastructure investments and citizen services provision. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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15 pages, 4278 KiB  
Article
6+: A Novel Approach for Building Extraction from a Medium Resolution Multi-Spectral Satellite
by Mayank Dixit, Kuldeep Chaurasia, Vipul Kumar Mishra, Dilbag Singh and Heung-No Lee
Sustainability 2022, 14(3), 1615; https://doi.org/10.3390/su14031615 - 29 Jan 2022
Cited by 4 | Viewed by 2352
Abstract
For smart, sustainable cities and urban planning, building extraction through satellite images becomes a crucial activity. It is challenging in the medium spatial resolution. This work proposes a novel methodology named ‘6+’ for improving building extraction in 10 m medium spatial resolution multispectral [...] Read more.
For smart, sustainable cities and urban planning, building extraction through satellite images becomes a crucial activity. It is challenging in the medium spatial resolution. This work proposes a novel methodology named ‘6+’ for improving building extraction in 10 m medium spatial resolution multispectral satellite images. Data resources used are Sentinel-2A satellite images and OpenStreetMap (OSM). The proposed methodology merges the available high-resolution bands, super-resolved Short-Wave InfraRed (SWIR) bands, and an Enhanced Normalized Difference Impervious Surface Index (ENDISI) built-up index-based image to produce enhanced multispectral satellite images that contain additional information on impervious surfaces for improving building extraction results. The proposed methodology produces a novel building extraction dataset named ‘6+’. Another dataset named ‘6 band’ is also prepared for comparison by merging super-resolved bands 11 and 12 along with all the highest spatial resolution bands. The building ground truths are prepared using OSM shapefiles. The models specific for extracting buildings, i.e., BRRNet, JointNet, SegUnet, Dilated-ResUnet, and other Unet based encoder-decoder models with a backbone of various state-of-art image segmentation algorithms, are applied on both datasets. The comparative analyses of all models applied to the ‘6+’ dataset achieve a better performance in terms of F1-Score and Intersection over Union (IoU) than the ‘6 band’ dataset. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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13 pages, 2495 KiB  
Article
A Novel Machine-Learning-Based Hybrid CNN Model for Tumor Identification in Medical Image Processing
by Gaurav Dhiman, Sapna Juneja, Wattana Viriyasitavat, Hamidreza Mohafez, Maryam Hadizadeh, Mohammad Aminul Islam, Ibrahim El Bayoumy and Kamal Gulati
Sustainability 2022, 14(3), 1447; https://doi.org/10.3390/su14031447 - 27 Jan 2022
Cited by 40 | Viewed by 4053
Abstract
The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event [...] Read more.
The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event extraction has become a research hotspot in academia. Many academic conferences publish it as an evaluation task and provide a series of high-quality annotation data. This article aims at the characteristics of discrete attributes of tumor-related medical events and proposes a medical event. The standard extraction method realizes the combined extraction of the primary tumor site and primary tumor size characteristics, as well as the extraction of tumor metastasis sites. In addition, given the problems of the small number and types of annotation texts for tumor-related medical events, a key-based approach is proposed. A pseudo-data-generation algorithm that randomly replaces information in the whole domain improves the transfer learning ability of the standard extraction method for different types of tumor-related medical event extractions. The proposed method won third place in the clinical medical event extraction and evaluation task of the CCKS2020 electronic medical record. A large number of experiments on the CCKS2020 dataset verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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19 pages, 3478 KiB  
Article
Smart-Hydroponic-Based Framework for Saffron Cultivation: A Precision Smart Agriculture Perspective
by Kanwalpreet Kour, Deepali Gupta, Kamali Gupta, Gaurav Dhiman, Sapna Juneja, Wattana Viriyasitavat, Hamidreza Mohafez and Mohammad Aminul Islam
Sustainability 2022, 14(3), 1120; https://doi.org/10.3390/su14031120 - 19 Jan 2022
Cited by 24 | Viewed by 8551
Abstract
Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite [...] Read more.
Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite the use of different artificial methods for cultivation, hydroponic approaches using the IoT prove to give the best results. The presented study consists of potential artificial approaches used for cultivation and the selection of hydroponics as the best approach out of these based on different parameters. This paper also provides a comparative analysis of six present hydroponic approaches. The research work on different factors of saffron, such as the parameters responsible for growth, reasons for the decline in growth, and different agronomical variables, has been shown graphically. A smart hydroponic system for saffron cultivation has been proposed using the NFT (nutrient film technique) and renewable sources of energy. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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12 pages, 2787 KiB  
Article
Geological Resource Planning and Environmental Impact Assessments Based on GIS
by Yun Xie, Binggeng Xie, Ziwei Wang, Rajeev Kumar Gupta, Mohammed Baz, Mohammed A. AlZain and Mehedi Masud
Sustainability 2022, 14(2), 906; https://doi.org/10.3390/su14020906 - 13 Jan 2022
Cited by 7 | Viewed by 2212
Abstract
The purpose is to study the geological resource planning and environmental impact assessments based on the geographic information system (GIS). In this study, the land resources of Yinan County in southeastern Shandong Province are taken as the research object. Based on a GIS, [...] Read more.
The purpose is to study the geological resource planning and environmental impact assessments based on the geographic information system (GIS). In this study, the land resources of Yinan County in southeastern Shandong Province are taken as the research object. Based on a GIS, the current situation of land resource development is analyzed, land resource planning is carried out, and environmental impact mitigation measures are evaluated and analyzed through the environmental impact. The results obtained depict the distribution of cultivated land; the development area is 1617.31 hm2, of which 577.32 hm2 is cultivated land, 30.43 hm2 is garden land, 399.66 hm2 is forest land, 40.87 hm2 is urban and rural construction land, 10.11 hm2 is traffic water conservancy and other construction land, and 558.92 hm2 is natural reserve land. In the layout of the construction land, the development area is 841.94 hm2, of which 175.44 hm2 is cultivated land, 47.88 hm2 is garden land, 100.54 hm2 is forest land, 0.1 hm2 is other agricultural land, 90.45 hm2 is urban and rural construction land, 3.66 hm2 is traffic water conservancy and other construction land, 11.33 hm2 is water area, and 412.54 hm2 is natural reserve land. The impact of the implementation of planning on most indicators is positive and beneficial, while the impact of negative indicators is relatively small. It is revealed that the implementation of the plan has little impact on most of the ecological environment indicators. Construction and cultivated land development further improve the level of urbanization. In the process of planning implementation, corresponding measures should be taken to slow down or eliminate the negative development of the ecological environment. Full article
(This article belongs to the Special Issue Sustainable Smart Cities and Societies Using Emerging Technologies)
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