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Artificial Intelligence (AI) and the Internet of Things (IoT) for Sustainable Applications

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 12342

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Special Issue Editor

Prof. Dr. M. Shamim Hossain

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Guest Editor

Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
Interests: social media; big data; cloud for healthcare; smart health; ambient assisted living, sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and the Internet of Things (IoT) have proved themselves as critical tools in practically every industry. The IoT has enabled sustained digitalization, which is the key to creating the digital future. The IoT, along with AI, can provide bright solutions to challenges faced in the sustainable development of our societies, environment, and natural resources. The fast and multidisciplinary evolution of AI and IoT is promising for their future employment in research on the sustainable development goals. A larger number of industries are being formed with artificial intelligence at the center. AI is predicted to have both immediate and long-term effects on the global economy via its role in sustainable development. Through the incorporation of AI tools, platforms and systems are one step closer to achieving ubiquitous and sustainable IoT applications. Considering the growing demand for sustainable IoT- and AI-based smart applications, this Special Issue invites submissions exploring potential tools, platforms, architectures, systems and solutions, including papers presenting novel architectures and sustainable Internet of Things (IoT) and artificial intelligence (AI) applications in the areas of smart cities, the environment, healthcare, industry, agriculture, military, etc.

AI-based green energy for IoT systems;
AI-enabled IoT in healthcare;
AI-based cloud deployment tools and platforms;
IoT and AI for environmental sustainability;
Intelligent data analytics in emerging systems;
Data monitoring and management for sustainable IoT applications;
Cybersecurity challenges for smart cities ;
Sustainable smart applications;
Machine learning in the biomedical data processing;
Sustainable cloud radio access networks;
AI-based digital twins;
IoT and AI for sustainable health;
Sustainable and cognitive modeling for next-generation smart applications.

You may choose our Joint Special Issue in AI.

Prof. Dr. M. Shamim Hossain
Guest Editor

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

sustainable internet of things
sustainable artificial intelligence
smart applications
intelligent data analytics

Published Papers (6 papers)

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Research

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25 pages, 4684 KiB

Open AccessArticle

A Meta-Heuristic Sustainable Intelligent Internet of Things Framework for Bearing Fault Diagnosis of Electric Motor under Variable Load Conditions

by Swarnali Deb Bristi, Mehtar Jahin Tatha, Md. Firoj Ali, Uzair Aslam Bhatti, Subrata K. Sarker, Mehdi Masud, Yazeed Yasin Ghadi, Abdulmohsen Algarni and Dip K. Saha

Sustainability 2023, 15(24), 16722; https://doi.org/10.3390/su152416722 - 11 Dec 2023

Viewed by 819

Abstract

The study introduces an Intelligent Diagnosis Framework (IDF) optimized using the Grasshopper Optimization Algorithm (GOA), an advanced swarm intelligence method, to enhance the precision of bearing defect diagnosis in electrical machinery. This area is vital for the energy sector and IoT manufacturing, but the evolving designs of electric motors add complexity to fault identification. Machine learning offers potential solutions but faces challenges due to computational intensity and the need for fine-tuning hyperparameters. The optimized framework, named GOA-IDF, is rigorously tested using experimental bearing fault data from the CWRU database, focusing on the 12,000 drive end and fan end datasets. Compared to existing machine learning algorithms, GOA-IDF shows superior diagnostic capabilities, especially in processing high-frequency data that are susceptible to noise interference. This research confirms that GOA-IDF excels in accurately categorizing faults and operates with increased computational efficiency. This advancement is a significant contribution to fault diagnosis in electrical motors. It suggests that integrating intelligent frameworks with meta-heuristic optimization techniques can greatly improve the standards of health monitoring and maintenance in the electrical machinery domain. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and the Internet of Things (IoT) for Sustainable Applications)

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28 pages, 175605 KiB

Open AccessArticle

Deep-Learning-Based Anti-Collision System for Construction Equipment Operators

by Yun-Sung Lee, Do-Keun Kim and Jung-Hoon Kim

Sustainability 2023, 15(23), 16163; https://doi.org/10.3390/su152316163 - 21 Nov 2023

Viewed by 1059

Abstract

Due to the dynamic environment of construction sites, worker collisions and stray accidents caused by heavy equipment are constantly occurring. In this study, a deep learning-based anti-collision system was developed to improve the existing proximity warning systems and to monitor the surroundings in real time. The technology proposed in this paper consists of an AI monitor, an image collection camera, and an alarm device. The AI monitor has a built-in object detection algorithm, automatically detects the operator from the image input from the camera, and notifies the operator of a danger warning. The deep learning-based object detection algorithm was trained with an image data set composed of a total of 42,620 newly constructed in this study. The proposed technology was installed on an excavator, which is the main equipment operated at the construction site, and performance tests were performed, and it showed the potential to effectively prevent collision accidents. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and the Internet of Things (IoT) for Sustainable Applications)

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25 pages, 3533 KiB

Open AccessArticle

Sustainable Energy Production in Smart Cities

by Ramiz Salama and Fadi Al-Turjman

Sustainability 2023, 15(22), 16052; https://doi.org/10.3390/su152216052 - 17 Nov 2023

Cited by 1 | Viewed by 1325

Abstract

Finding a method to provide the installed Internet of Things (IoT) nodes with energy that is both ubiquitous and long-lasting is crucial for ensuring continuous smart city optimization. These and other problems have impeded new research into energy harvesting. After the COVID-19 pandemic and the lockdown that all but ended daily activity in many countries, the ability of human remote connections to enforce social distancing became crucial. Since they lay the groundwork for surviving a lockdown, Internet of Things (IoT) devices are once again widely recognised as crucial elements of smart cities. The recommended solution of energy collection would enable IoT hubs to search for self-sustaining energy from ecologically large sources. The bulk of urban energy sources that could be used were examined in this work, according to descriptions made by researchers in the literature. Given the abundance of free resources in the city covered in this research, we have also suggested that energy sources can be application-specific. This implies that energy needs for various IoT devices or wireless sensor networks (WSNs) for smart city automation should be searched for near those needs. One of the important smart, ecological and energy-harvesting subjects that has evolved as a result of the advancement of intelligent urban computing is intelligent cities and societies. Collecting and exchanging Internet of Things (IoT) gadgets and smart applications that improve people’s quality of life is the main goal of a sustainable smart city. Energy harvesting management, a key element of sustainable urban computing, is hampered by the exponential rise of Internet of Things (IoT) sensors, smart apps, and complicated populations. These challenges include the requirement to lower the associated elements of energy consumption, power conservation, and waste management for the environment. However, the idea of energy-harvesting management for sustainable urban computing is currently expanding at an exponential rate and requires attention due to regulatory and economic constraints. This study investigates a variety of green energy-collecting techniques in relation to edge-based intelligent urban computing’s smart applications for sustainable and smart cities. The four categories of energy-harvesting strategies currently in use are smart grids, smart environmental systems, smart transportation systems, and smart cities. In terms of developed algorithms, evaluation criteria, and evaluation environments, this review’s objective is to discuss the technical features of energy-harvesting management systems for environmentally friendly urban computing. For sustainable smart cities, which specifically contribute to increasing the energy consumption of smart applications and human life in complex and metropolitan areas, it is crucial from a technical perspective to examine existing barriers and unexplored research trajectories in energy harvesting and waste management. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and the Internet of Things (IoT) for Sustainable Applications)

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22 pages, 15655 KiB

Open AccessArticle

Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

by Abdulkabir Abdulraheem and Im Y. Jung

Sustainability 2023, 15(17), 12915; https://doi.org/10.3390/su151712915 - 26 Aug 2023

Viewed by 977

Abstract

In this study, we present a machine-learning-based approach that focuses on the automatic retrieval of engraved expiry dates. We leverage generative adversarial networks by augmenting the dataset to enhance the classifier performance and propose a suitable convolutional neural network (CNN) model for this dataset referred to herein as the CNN for engraved digit (CNN-ED) model. Our evaluation encompasses a diverse range of supervised classifiers, including classic and deep learning models. Our proposed CNN-ED model remarkably achieves an exceptional accuracy, reaching a 99.88% peak with perfect precision for all digits. Our new model outperforms other CNN-based models in accuracy and precision. This work offers valuable insights into engraved digit recognition and provides potential implications for designing more accurate and efficient recognition models in various applications. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and the Internet of Things (IoT) for Sustainable Applications)

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17 pages, 1525 KiB

Open AccessArticle

AI- and IoT-Assisted Sustainable Education Systems during Pandemics, such as COVID-19, for Smart Cities

by M. M. Kamruzzaman, Saad Alanazi, Madallah Alruwaili, Nasser Alshammari, Said Elaiwat, Marwan Abu-Zanona, Nisreen Innab, Bassam Mohammad Elzaghmouri and Bandar Ahmed Alanazi

Sustainability 2023, 15(10), 8354; https://doi.org/10.3390/su15108354 - 21 May 2023

Cited by 14 | Viewed by 3611

Abstract

The integration of AI and the IoT in education has the potential to revolutionize the way we learn. Personalized learning, real-time feedback and support, and immersive learning experiences are some of the benefits that AI and the IoT can bring to the education system. In this regard, this research paper aims to investigate how AI and the IoT can be integrated into sustainable education in order to provide students with personalized and immersive learning experiences during pandemics, such as COVID-19, for smart cities. The study’s key findings report that AI can be employed in sustainable education through personalized learning. AI-powered algorithms can be used to analyze student data and create personalized learning experiences for each student. This includes providing students with tailored content, assessments, and feedback that align with their unique learning style and pace. Additionally, AI can be used to communicate with students in a more natural and human-like way, making the learning experience more engaging and interactive. Another key aspect of the integration of AI and the IoT in education obtained from this research is the ability to provide real-time feedback and support. IoT-enabled devices, such as smart cameras and microphones, can be used to monitor student engagement and provide real-time feedback. AI algorithms can then use these data to adapt the learning experience in real time. IoT-enabled devices, such as tablets and laptops, can be used to collect and process student work, allowing for the automatic grading of assignments and assessments. Additionally, IoT technology can facilitate remote monitoring and grading of student work, which would be particularly useful for students who cannot attend traditional classroom settings. Furthermore, AI and the IoT can also be used to create intelligent personal learning environments (PLEs) that provide students with personalized, adaptive, and engaging learning experiences. IoT-enabled devices, such as smart cameras and microphones, combined with AI-powered algorithms, can provide real-time feedback and support, allowing the PLE to adapt to the student’s needs and preferences. It is concluded that integrating AI and the IoT in sustainable education can revolutionize the way people learn, providing students with personalized, real-time feedback and support and opening up new opportunities for remote and disadvantaged students. However, it will be important to ensure that the use of AI and the IoT in education is ethical and responsible to ensure that all students have equal access to the benefits of these technologies. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and the Internet of Things (IoT) for Sustainable Applications)

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Review

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38 pages, 2340 KiB

Open AccessReview

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

by Mohamed S. Abdalzaher, Moez Krichen, Derya Yiltas-Kaplan, Imed Ben Dhaou and Wilfried Yves Hamilton Adoni

Sustainability 2023, 15(15), 11713; https://doi.org/10.3390/su151511713 - 28 Jul 2023

Cited by 13 | Viewed by 3823

Abstract

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and the Internet of Things (IoT) for Sustainable Applications)

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