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Machine Learning, IoT and Artificial Intelligence for Sustainable Development
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Machine Learning, IoT and Artificial Intelligence for Sustainable Development

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

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 36285

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Mourade Azrour

E-Mail Website
Guest Editor
Department of Computer, Faculty of Sciences and Technologies, Moulay Ismail University of Meknès, Errachidia 52000, Morocco
Interests: water quality modeling; IoT and smart devices; internet of things; water quality
Special Issues, Collections and Topics in MDPI journals
Dr. Azidine Guezzaz

E-Mail
Guest Editor
Technology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, Morocco
Interests: computer sciences; Industry 4.0; network security; computer networking
Dr. Imad Zeroual

E-Mail Website
Guest Editor
Department of Computer, Faculty of Sciences and Technologies, Moulay Ismail University of Meknès, Errachidia 52000, Morocco
Interests: machine learning; AI; data science
Dr. Azeem Irshad

E-Mail Website
Guest Editor
Computer Sciences Department, International Islamic University Islamabad, Islamabad 44000, Pakistan
Interests: IoT; security; authentication
Dr. Jamal Mabrouki

E-Mail Website
Guest Editor
Laboratory of Spectroscopy, Molecular Modeling, Materials, Nanomaterial, Water and Environment, CERNE2D, Faculty of Science, Mohammed V University in Rabat, Rabat BP1014, Morocco
Interests: environment; monitoring systems; water management
Special Issues, Collections and Topics in MDPI journals
Dr. Said Benkirane

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Guest Editor
Technology Higher School Essaouira, Cadi Ayyad University, Essaouira 44000, Morocco
Interests: VANET; smart grid; smart transportation
Dr. Shehzad Ashraf Chaudhry

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
Interests: cryptography; Internet of Things; authentication; authenticated encryption; blockchains; 6G communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the newest technologies, devices and techniques related to IoT, machine learning and artificial intelligence are constantly developing. Therefore, they have a significant impact on the sustainability of our lifestyles. Accordingly, the application domain of these technologies and tools involves agriculture, water management, healthcare, bioinformatics, smart grids, smart cities, security, and so on. In addition, the IoT has brought an innovative perspective that is totally distinct from the usual approaches: the former consists of a device that can communicate with the network and is capable of implementing intelligent solutions. The goal of this Special Issue is to create a common gateway between researchers, allowing them to exchange and share their results related to the application of IoT, artificial intelligence and machine learning in various domains.

Dr. Mourade Azrour
Dr. Azidine Guezzaz
Dr. Imad Zeroual
Dr. Azeem Irshad
Dr. Jamal Mabrouki
Dr. Said Benkirane
Dr. Shehzad Ashraf Chaudhry
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

  • machine learning, IoT and AI for smart systems
  • machine learning, IoT and AI for environment and health
  • machine learning, IoT and AI for air, water and climate sustainability
  • machine learning, IoT and AI for smart energy, renewable energy and green fuel
  • machine learning, IoT and AI for smart cities
  • smart communication and networking technologies
  • big data and smart systems
  • biometrics and smart systems
  • cloud/edge/fog computing technologies for smart systems
  • IoT and industrial IoT technologies for smart system
  • authentication and authorization
  • security of private data
  • blockchain-related applications for intelligent IoT trust, security and privacy
  • big data analytics in IoT systems

Published Papers (16 papers)

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15 pages, 1404 KiB  
Article
Beyond Metrics: Navigating AI through Sustainable Paradigms
by Nir Ofek and Oded Maimon
Sustainability 2023, 15(24), 16789; https://doi.org/10.3390/su152416789 - 13 Dec 2023
Viewed by 825
Abstract
This manuscript presents an innovative approach to the concept of sustainability in the realm of Artificial Intelligence (AI), recognizing that sustainability is a dynamic vision characterized by harmony and balance. We argue that achieving sustainability in AI systems requires moving beyond rigid adherence [...] Read more.
This manuscript presents an innovative approach to the concept of sustainability in the realm of Artificial Intelligence (AI), recognizing that sustainability is a dynamic vision characterized by harmony and balance. We argue that achieving sustainability in AI systems requires moving beyond rigid adherence to protocols and compliance checklists, which tend to simplify sustainability into static criteria. Instead, sustainable AI should reflect the balance and adaptability intrinsic to the broader vision of sustainability. In crafting this vision, we draw upon the principles of complex systems theory, the wisdom of philosophical doctrines, and the insights of ecology, weaving them into a comprehensive paradigm. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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23 pages, 1479 KiB  
Article
Advances in the Optimization of Vehicular Traffic in Smart Cities: Integration of Blockchain and Computer Vision for Sustainable Mobility
by Angel Jaramillo-Alcazar, Jaime Govea and William Villegas-Ch
Sustainability 2023, 15(22), 15736; https://doi.org/10.3390/su152215736 - 08 Nov 2023
Cited by 1 | Viewed by 1358
Abstract
The growing adoption of Artificial Intelligence of Things technologies in smart cities generates significant transformations to address urban challenges and move towards sustainability. This article analyzes the economic, social, and environmental impacts of Artificial Intelligence of Things in urban environments, focusing on a [...] Read more.
The growing adoption of Artificial Intelligence of Things technologies in smart cities generates significant transformations to address urban challenges and move towards sustainability. This article analyzes the economic, social, and environmental impacts of Artificial Intelligence of Things in urban environments, focusing on a case study on optimizing vehicular traffic. The research methodology is based on a comprehensive analysis of academic literature and government sources, followed by the creation of a simulated city model. This framework implemented a vehicle-traffic optimization system integrating artificial intelligence algorithms, computer vision, and blockchain technology. The results obtained in this case study are highly encouraging: artificial intelligence algorithms processed real-time data from security cameras and traffic lights, resulting in a notable 20% reduction in traffic congestion during peak hours. Furthermore, implementing blockchain technology guarantees the security and immutability of traffic data, strengthening trust in the system and promoting sustainability in urban environments. These results highlight the importance of combining advanced technologies to effectively address modern cities’ complex challenges and move towards more sustainable and livable cities. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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20 pages, 5595 KiB  
Article
Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications
by Amer Aljaedi, Saba Siddique, Muhammad Islam Satti, Adel R. Alharbi, Mohammed Alotaibi and Muhammad Usman
Sustainability 2023, 15(22), 15683; https://doi.org/10.3390/su152215683 - 07 Nov 2023
Viewed by 745
Abstract
The Internet of Things (IoT) constitutes a digitally integrated network of intelligent devices equipped with sensors, software, and communication capabilities, facilitating data exchange among a multitude of digital systems via the Internet. Despite its pivotal role in the software development life-cycle (SDLC) for [...] Read more.
The Internet of Things (IoT) constitutes a digitally integrated network of intelligent devices equipped with sensors, software, and communication capabilities, facilitating data exchange among a multitude of digital systems via the Internet. Despite its pivotal role in the software development life-cycle (SDLC) for ensuring software quality in terms of both functional and non-functional aspects, testing within this intricate software–hardware ecosystem has been somewhat overlooked. To address this, various testing techniques are applied for real-time minimization of failure rates in IoT applications. However, the execution of a comprehensive test suite for specific IoT software remains a complex undertaking. This paper proposes a holistic framework aimed at aiding quality assurance engineers in delineating essential testing methods across different testing levels within the IoT. This delineation is crucial for effective quality assurance, ultimately reducing failure rates in real-time scenarios. Furthermore, the paper offers a mapping of these identified tests to each layer within the layered framework of the IoT. This comprehensive approach seeks to enhance the reliability and performance of IoT-based applications. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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18 pages, 3000 KiB  
Article
Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors
by Naif Al Mudawi, Mahwish Pervaiz, Bayan Ibrahimm Alabduallah, Abdulwahab Alazeb, Abdullah Alshahrani, Saud S. Alotaibi and Ahmad Jalal
Sustainability 2023, 15(20), 14780; https://doi.org/10.3390/su152014780 - 12 Oct 2023
Viewed by 1144
Abstract
The COVID-19 pandemic has sped up the acceptance of online education as a substitute for conventional classroom instruction. E-Learning emerged as an instant solution to avoid academic loss for students. As a result, educators and academics are becoming more and more interested in [...] Read more.
The COVID-19 pandemic has sped up the acceptance of online education as a substitute for conventional classroom instruction. E-Learning emerged as an instant solution to avoid academic loss for students. As a result, educators and academics are becoming more and more interested in comprehending how students behave in e-learning settings. Behavior analysis of students in an e-learning environment can provide vision and influential factors that can improve learning outcomes and guide the creation of efficient interventions. The main objective of this work is to provide a system that analyzes the behavior and actions of students during e-learning which can help instructors to identify and track student attention levels so that they can design their content accordingly. This study has presented a fresh method for examining student behavior. Viola–Jones was used to recognize the student using the object’s movement factor, and a region-shrinking technique was used to isolate occluded items. Each object has been checked by a human using a template-matching approach, and for each object that has been confirmed, features are computed at the skeleton and silhouette levels. A genetic algorithm was used to categorize the behavior. Using this system, instructors can spot kids who might be failing or uninterested in learning and offer them specific interventions to enhance their learning environment. The average attained accuracy for the MED and Edu-Net datasets are 90.5% and 85.7%, respectively. These results are more accurate when compared to other methods currently in use. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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20 pages, 6952 KiB  
Article
Enhancing the Automatic Recognition Accuracy of Imprinted Ship Characters by Using Machine Learning
by Abdulkabir Abdulraheem, Jamiu T. Suleiman and Im Y. Jung
Sustainability 2023, 15(19), 14130; https://doi.org/10.3390/su151914130 - 24 Sep 2023
Viewed by 717
Abstract
In this paper, we address the challenge of ensuring safe operations and rescue efforts in emergency situations, for the sake of a sustainable marine environment. Our focus is on character recognition, specifically on deciphering characters present on the surface of aged and corroded [...] Read more.
In this paper, we address the challenge of ensuring safe operations and rescue efforts in emergency situations, for the sake of a sustainable marine environment. Our focus is on character recognition, specifically on deciphering characters present on the surface of aged and corroded ships, where the markings may have faded or become unclear over time, in contrast to vessels with clearly visible letters. Imprinted ship characters encompassing engraved, embroidered, and other variants found on ship components serve as vital markers for ship identification, maintenance, and safety in marine technology. The accurate recognition of these characters is essential for ensuring efficient operations and effective decision making. This study presents a machine-learning-based method that markedly improves the recognition accuracy of imprinted ship numbers and characters. This improvement is achieved by enhancing data classification accuracy through data augmentation. The effectiveness of the proposed method was validated by comparing it to State-of-the-Art classification technologies within the imprinted ship character dataset. We started with the originally sourced dataset and then systematically increased the dataset size, using the most suitable generative adversarial networks for our dataset. We compared the effectiveness of classic and convolutional neural network (CNN)-based classifiers to our classifier, a CNN-based classifier for imprinted ship characters (CNN-ISC). Notably, on the augmented dataset, our CNN-ISC model achieved impressive maximum recognition accuracy of 99.85% and 99.7% on alphabet and digit recognition, respectively. Overall, data augmentation markedly improved the recognition accuracy of ship digits and alphabets, with the proposed classification model outperforming other methods. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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17 pages, 5117 KiB  
Article
Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities
by Mahmoud Ragab, Adil O. Khadidos, Abdulrhman M. Alshareef, Khaled H. Alyoubi, Diaa Hamed and Alaa O. Khadidos
Sustainability 2023, 15(16), 12523; https://doi.org/10.3390/su151612523 - 17 Aug 2023
Viewed by 1189
Abstract
Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT [...] Read more.
Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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11 pages, 647 KiB  
Article
Transformer Architecture-Based Transfer Learning for Politeness Prediction in Conversation
by Shakir Khan, Mohd Fazil, Agbotiname Lucky Imoize, Bayan Ibrahimm Alabduallah, Bader M. Albahlal, Saad Abdullah Alajlan, Abrar Almjally and Tamanna Siddiqui
Sustainability 2023, 15(14), 10828; https://doi.org/10.3390/su151410828 - 10 Jul 2023
Cited by 2 | Viewed by 1346
Abstract
Politeness is an essential part of a conversation. Like verbal communication, politeness in textual conversation and social media posts is also stimulating. Therefore, the automatic detection of politeness is a significant and relevant problem. The existing literature generally employs classical machine learning-based models [...] Read more.
Politeness is an essential part of a conversation. Like verbal communication, politeness in textual conversation and social media posts is also stimulating. Therefore, the automatic detection of politeness is a significant and relevant problem. The existing literature generally employs classical machine learning-based models like naive Bayes and Support Vector-based trained models for politeness prediction. This paper exploits the state-of-the-art (SOTA) transformer architecture and transfer learning for respectability prediction. The proposed model employs the strengths of context-incorporating large language models, a feed-forward neural network, and an attention mechanism for representation learning of natural language requests. The trained representation is further classified using a softmax function into polite, impolite, and neutral classes. We evaluate the presented model employing two SOTA pre-trained large language models on two benchmark datasets. Our model outperformed the two SOTA and six baseline models, including two domain-specific transformer-based models using both the BERT and RoBERTa language models. The ablation investigation shows that the exclusion of the feed-forward layer displays the highest impact on the presented model. The analysis reveals the batch size and optimization algorithms as effective parameters affecting the model performance. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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21 pages, 3941 KiB  
Article
A Novel Machine Learning Approach for Solar Radiation Estimation
by Hasna Hissou, Said Benkirane, Azidine Guezzaz, Mourade Azrour and Abderrahim Beni-Hssane
Sustainability 2023, 15(13), 10609; https://doi.org/10.3390/su151310609 - 05 Jul 2023
Cited by 10 | Viewed by 2527
Abstract
Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth’s climate and weather systems, influencing the [...] Read more.
Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth’s climate and weather systems, influencing the distribution of heat across the planet, shaping global air and ocean currents, and determining weather patterns. Variations in Rs levels have significant implications for climate change and long-term climate trends. Moreover, Rs represents an abundant and renewable energy resource, offering a clean and sustainable alternative to fossil fuels. By harnessing solar energy, we can actively reduce greenhouse gas emissions. However, the utilization of Rs comes with its own challenges that must be addressed. One problem is its variability, which makes it difficult to predict and plan for consistent solar energy generation. Its intermittent nature also poses difficulties in meeting continuous energy demand unless appropriate energy storage or backup systems are in place. Integrating large-scale solar energy systems into existing power grids can present technical challenges. Rs levels are influenced by various factors; understanding these factors is crucial for various applications, such as renewable energy planning, climate modeling, and environmental studies. Overcoming the associated challenges requires advancements in technology and innovative solutions. Measuring and harnessing Rs for various applications can be achieved using various devices; however, the expense and scarcity of measuring equipment pose challenges in accurately assessing and monitoring Rs levels. In order to address this, alternative methods have been developed with which to estimate Rs, including artificial intelligence and machine learning (ML) models, like neural networks, kernel algorithms, tree-based models, and ensemble methods. To demonstrate the impact of feature selection methods on Rs predictions, we propose a Multivariate Time Series (MVTS) model using Recursive Feature Elimination (RFE) with a decision tree (DT), Pearson correlation (Pr), logistic regression (LR), Gradient Boosting Models (GBM), and a random forest (RF). Our article introduces a novel framework that integrates various models and incorporates overlooked factors. This framework offers a more comprehensive understanding of Recursive Feature Elimination and its integrations with different models in multivariate solar radiation forecasting. Our research delves into unexplored aspects and challenges existing theories related to solar radiation forecasting. Our results show reliable predictions based on essential criteria. The feature ranking may vary depending on the model used, with the RF Regressor algorithm selecting features such as maximum temperature, minimum temperature, precipitation, wind speed, and relative humidity for specific months. The DT algorithm may yield a slightly different set of selected features. Despite the variations, all of the models exhibit impressive performance, with the LR model demonstrating outstanding performance with low RMSE (0.003) and the highest R2 score (0.002). The other models also show promising results, with RMSE scores ranging from 0.006 to 0.007 and a consistent R2 score of 0.999. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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20 pages, 2133 KiB  
Article
Elliptic Curve Cryptography-Based Scheme for Secure Signaling and Data Exchanges in Precision Agriculture
by Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Hend Muslim Jasim, Junchao Ma, Mohammed Abdulridha Hussain, Zaid Alaa Hussien and Abdulla J. Y. Aldarwish
Sustainability 2023, 15(13), 10264; https://doi.org/10.3390/su151310264 - 28 Jun 2023
Cited by 6 | Viewed by 1369
Abstract
Precision agriculture encompasses automation and application of a wide range of information technology devices to improve farm output. In this environment, smart devices collect and exchange a massive number of messages with other devices and servers over public channels. Consequently, smart farming is [...] Read more.
Precision agriculture encompasses automation and application of a wide range of information technology devices to improve farm output. In this environment, smart devices collect and exchange a massive number of messages with other devices and servers over public channels. Consequently, smart farming is exposed to diverse attacks, which can have serious consequences since the sensed data are normally processed to help determine the agricultural field status and facilitate decision-making. Although a myriad of security schemes has been presented in the literature to curb these challenges, they either have poor performance or are susceptible to attacks. In this paper, an elliptic curve cryptography-based scheme is presented, which is shown to be formally secure under the Burrows–Abadi–Needham (BAN) logic. In addition, it is semantically demonstrated to offer user privacy, anonymity, unlinkability, untraceability, robust authentication, session key agreement, and key secrecy and does not require the deployment of verifier tables. In addition, it can withstand side-channeling, physical capture, eavesdropping, password guessing, spoofing, forgery, replay, session hijacking, impersonation, de-synchronization, man-in-the-middle, privileged insider, denial of service, stolen smart device, and known session-specific temporary information attacks. In terms of performance, the proposed protocol results in 14.67% and 18% reductions in computation and communication costs, respectively, and a 35.29% improvement in supported security features. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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26 pages, 8482 KiB  
Article
A Framework and IoT-Based Accident Detection System to Securely Report an Accident and the Driver’s Private Information
by Amal Hussain Alkhaiwani and Badr Soliman Alsamani
Sustainability 2023, 15(10), 8314; https://doi.org/10.3390/su15108314 - 19 May 2023
Cited by 3 | Viewed by 4164
Abstract
Road traffic accidents in Saudi Arabia have become a serious issue because many of these accidents lead to deaths, injuries, and financial losses. Human lives are often lost in road accidents due to the delay in accident detection by medical assistance. In fact, [...] Read more.
Road traffic accidents in Saudi Arabia have become a serious issue because many of these accidents lead to deaths, injuries, and financial losses. Human lives are often lost in road accidents due to the delay in accident detection by medical assistance. In fact, the accident’s location and the driver’s personal information are considered critical information that plays a vital role in preserving human life. Additionally, previous studies have found a limitation in the encryption of sensitive data; in fact, a leak of private information is thought to be one of the challenges that restrict the use of IoT devices. To resolve this problem, this research presents an intelligent security framework, and an Internet-of-Things-based system is proposed for immediate accident detection. Thus, this system requires the highest level of security and privacy to maintain the driver’s privacy. Moreover, the design science research methodology was followed to design and evaluate the artifacts. Thus, the study’s research resulted in the ability to design a secure and effective IoT-based system to detect and report a car accident instantly. In addition, the message is encrypted using Elliptic Curve Integrated Encryption and sent through Message Queuing Telemetry Transport over GSM. The study’s overall results show the flexibility with which the proposed artifact can be used for other purposes related to the IoT security framework to send and encrypt critical information. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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16 pages, 1642 KiB  
Article
Provably Secure Dynamic Anonymous Authentication Protocol for Wireless Sensor Networks in Internet of Things
by Zixuan Ding and Qi Xie
Sustainability 2023, 15(7), 5734; https://doi.org/10.3390/su15075734 - 24 Mar 2023
Cited by 5 | Viewed by 1056
Abstract
Wireless sensor networks are a promising application of the Internet of Things in the sustainable development of smart cities, and have been afforded significant attention since first being proposed. Authentication protocols aim to protect the security and confidentiality of legitimate users when accessing [...] Read more.
Wireless sensor networks are a promising application of the Internet of Things in the sustainable development of smart cities, and have been afforded significant attention since first being proposed. Authentication protocols aim to protect the security and confidentiality of legitimate users when accessing and transmitting data. However, existing protocols may suffer from one or more security flaws. Recently, Butt et al. proposed an energy-efficient three-factor authentication protocol for wireless sensor networks. However, their protocol is vulnerable to several attacks, and lacks certain security properties. In this paper, the causes of these design flaws are analyzed. Furthermore, we propose a novel three-factor authentication protocol (password, smart card, and biometric information) for wireless sensor networks in Internet of Things contexts. A dynamic anonymous strategy is designed to prevent privacy disclosure and to resist sensor node capture attacks, tracking attacks, and desynchronization attacks. The Find–Guess model and random oracle model are combined to prove the security of the proposed protocol. A comparative analysis with related schemes shows that the proposed protocol has higher security and is able to maintain a low computational overhead. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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30 pages, 12635 KiB  
Article
A Multi-Criteria Analysis Approach to Identify Flood Risk Asset Damage Hotspots in Western Australia
by Pornpit Wongthongtham, Bilal Abu-Salih, Jeff Huang, Hemixa Patel and Komsun Siripun
Sustainability 2023, 15(7), 5669; https://doi.org/10.3390/su15075669 - 23 Mar 2023
Viewed by 1468
Abstract
Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. [...] Read more.
Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. Although international research projects on water-sensitive urban design and rural drainage design have provided partial solutions to this problem, road networks commonly serve unique combinations of urban-rural residential and undeveloped areas; these areas often have diverse hydrology, geology, and climates. Resultantly, applying a one-size-fits-all solution to asset management is ineffective. This paper focuses on data-driven flood modelling that can be used to mitigate or prevent floodwater-related damage in Western Australia. In particular, a holistic and coherent view of data-driven asset management is presented and multi-criteria analysis (MCA) is used to define the high-risk hotspots for asset damage in Western Australia. These state-wide hotspots are validated using road closure data obtained from the relevant government agency. The proposed approach offers important insights with regard to factors influencing the risk of damage in the stormwater management system. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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20 pages, 7940 KiB  
Article
A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies
by Ch. Anwar Ul Hassan, Faten Khalid Karim, Assad Abbas, Jawaid Iqbal, Hela Elmannai, Saddam Hussain, Syed Sajid Ullah and Muhammad Sufyan Khan
Sustainability 2023, 15(5), 3982; https://doi.org/10.3390/su15053982 - 22 Feb 2023
Cited by 2 | Viewed by 2716
Abstract
Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that [...] Read more.
Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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18 pages, 5658 KiB  
Article
Smart Diagnosis of Adenocarcinoma Using Convolution Neural Networks and Support Vector Machines
by Balasundaram Ananthakrishnan, Ayesha Shaik, Shubhadip Chakrabarti, Vaishnavi Shukla, Dewanshi Paul and Muthu Subash Kavitha
Sustainability 2023, 15(2), 1399; https://doi.org/10.3390/su15021399 - 11 Jan 2023
Viewed by 1406
Abstract
Adenocarcinoma is a type of cancer that develops in the glands present on the lining of the organs in the human body. It is found that histopathological images, obtained as a result of biopsy, are the most definitive way of diagnosing cancer. The [...] Read more.
Adenocarcinoma is a type of cancer that develops in the glands present on the lining of the organs in the human body. It is found that histopathological images, obtained as a result of biopsy, are the most definitive way of diagnosing cancer. The main objective of this work is to use deep learning techniques for the detection and classification of adenocarcinoma using histopathological images of lung and colon tissues with minimal preprocessing. Two approaches have been utilized. The first method entails creating two CNN architectures: CNN with a Softmax classifier (AdenoCanNet) and CNN with an SVM classifier (AdenoCanSVM). The second approach corresponds to training some of the prominent existing architecture such as VGG16, VGG19, LeNet, and ResNet50. The study aims at understanding the performance of various architectures in diagnosing using histopathological images with cases taken separately and taken together, with a full dataset and a subset of the dataset. The LC25000 dataset used consists of 25,000 histopathological images, having both cancerous and normal images from both the lung and colon regions of the human body. The accuracy metric was taken as the defining parameter for determining and comparing the performance of various architectures undertaken during the study. A comparison between the several models used in the study is presented and discussed. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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22 pages, 1576 KiB  
Article
Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques
by Zafar Mahmood, Benmao Cheng, Naveed Anwer Butt, Ghani Ur Rehman, Muhammad Zubair, Afzal Badshah and Muhammad Aslam
Sustainability 2023, 15(2), 1378; https://doi.org/10.3390/su15021378 - 11 Jan 2023
Cited by 6 | Viewed by 1655
Abstract
The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, [...] Read more.
The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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Review

Jump to: Research

28 pages, 3621 KiB  
Review
A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope
by Ahmad Waleed Salehi, Shakir Khan, Gaurav Gupta, Bayan Ibrahimm Alabduallah, Abrar Almjally, Hadeel Alsolai, Tamanna Siddiqui and Adel Mellit
Sustainability 2023, 15(7), 5930; https://doi.org/10.3390/su15075930 - 29 Mar 2023
Cited by 39 | Viewed by 8746
Abstract
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis [...] Read more.
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations. Full article
(This article belongs to the Special Issue Machine Learning, IoT and Artificial Intelligence for Sustainable Development)
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