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Sustainable Application of Internet of Things and Artificial Intelligence

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 18650

Special Issue Editors

Dr. Amrit Mukherjee

E-Mail Website
Guest Editor
Institute of Applied Informatics, Department of Computer Science, University of South Bohemia, České Budějovice, Czech Republic
Interests: artificial intelligence; next-generation IoT systems; wireless sensor networks; cognitive radio; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Ranjay Hazra

E-Mail Website
Guest Editor
Department of Electronics and Instrumentation Engineering, National Institute of Technology (NIT) Silchar, Silchar 788010, Assam, India
Interests: communication systems; signal & image processing; applications of ai in communication
Dr. Rudolf Vohnout

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Science, University of South Bohemia in Ceske Budejovice, Branisovska 1760, CZ-37005 Ceske Budesjovice, Czech Republic
Interests: computer networks; data transmission; transmission systems; network programming; optical communication

Special Issue Information

Dear Colleagues,

Sustainable Application of Internet of Things (IoT) and Artificial Intelligence. The special issue aims to design and implement various research outcomes, architectures and other sustainable Internet of Things (IoT) applications fused with Artificial Intelligence (AI). The demands for communication and computation technologies are increasing every day, which thrusts the use of sustainable AI in various applications. As we know, the smart city, environmental, military, medical, industrial, agriculture, healthcare and home networks, the role of IoT is trusted and improvised to make the application secure, green and sustainable. By involving AI in these parameters, the tools, platforms and systems are one step closer to ubiquitous and sustainable IoT applications. Existing research on sensor networks and intelligent transportation uses green AI-based routing and IoT [1, 2]. As there is a constant demand for sustainable IoT and AI with most smart applications, the special issue encourages potential architectures, tools, platforms, systems and results for submission.

  • AI based cloud deployment tools and platforms
  • AI assisted IoT architectures
  • Green deep IoT Infrastructures
  • Hybrid intelligence for Industrial IoT applications
  • ANN based data monitoring and management for sustainable IoT applications
  • Security and trust for green smart applications
  • Sustinaible FINTECH applications
  • Optimizations and maintaiance in sustainable smart applications
  • AI based fault diaginosis in smart grid
  • Machine learnings in biomedical data processing
  • Sustainable cloud radio access networks
  • AI based digitial twins
  • Sustainable and cognitive modelling for next generation smart applications

Dr. Amrit Mukherjee
Dr. Ranjay Hazra
Dr. Rudolf Vohnout
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

  • sustainable internet of things
  • sustainable artificial intelligence
  • smart application
  • wireless sensor networks
  • security
  • energy-efficiency
  • security

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Published Papers (6 papers)

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Research

27 pages, 8660 KiB  
Article
Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability
by Hatem A. Alharbi, Khulud K. Alharbi and Ch Anwar Ul Hassan
Sustainability 2023, 15(22), 15695; https://doi.org/10.3390/su152215695 - 7 Nov 2023
Cited by 4 | Viewed by 6374
Abstract
In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various [...] Read more.
In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications. Full article
(This article belongs to the Special Issue Sustainable Application of Internet of Things and Artificial Intelligence)
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16 pages, 6341 KiB  
Article
Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT
by Mesfer Al Duhayyim
Sustainability 2023, 15(9), 7321; https://doi.org/10.3390/su15097321 - 28 Apr 2023
Cited by 6 | Viewed by 1787
Abstract
The internet of things (IoT) paradigm roles an important play in enhancing smart city tracking applications and managing city procedures in real time. The most important problem connected to smart city applications has been solid waste management, which can have adverse effects on [...] Read more.
The internet of things (IoT) paradigm roles an important play in enhancing smart city tracking applications and managing city procedures in real time. The most important problem connected to smart city applications has been solid waste management, which can have adverse effects on society’s health and environment. Waste management has developed a challenge faced by not only evolving nations but also established and developed counties. Solid waste management is an important and stimulating problem for environments across the entire world. Therefore, there is the need to develop an effective technique that will remove these problems, or at least decreases them to a minimal level. This study develops a modified cuttlefish swarm optimization with machine learning-based solid waste management (MCSOML-SWM) in smart cities. The MCSOML-SWM technique aims to recognize different categories of solid wastes and enable smart waste management. In the MCSOML-SWM model, a single shot detector (SSD) model allows effectual recognition of objects. Then, a deep convolutional neural network-based MixNet model was exploited to produce feature vectors. Since trial-and-error hyperparameter tuning is a tedious process, the MCSO algorithm was applied for automated hyperparameter tuning. For accurate waste classification, the MCSOML-SWM technique applies support vector machine (SVM) in this study. A comprehensive set of simulations demonstrate the improved classification performance of the MCSOML-SWM model with maximum accuracy of 99.34%. Full article
(This article belongs to the Special Issue Sustainable Application of Internet of Things and Artificial Intelligence)
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15 pages, 3150 KiB  
Article
Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic
by Thavavel Vaiyapuri, Sharath Kumar Jagannathan, Mohammed Altaf Ahmed, K. C. Ramya, Gyanendra Prasad Joshi, Soojeong Lee and Gangseong Lee
Sustainability 2023, 15(8), 6404; https://doi.org/10.3390/su15086404 - 9 Apr 2023
Cited by 4 | Viewed by 2561
Abstract
The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during [...] Read more.
The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during the pandemic. With the rise in COVID-19 cases with strict lockdowns, people expressed their opinions publicly on social networking platforms. This provides a deeper knowledge of human psychology at the time of disastrous events. By applying user-produced content on social networking platforms such as Twitter, the sentiments and views of people are analyzed to assist in introducing awareness campaigns and health intervention policies. The modern evolution of artificial intelligence (AI) and natural language processing (NLP) mechanisms has revealed remarkable performance in sentimental analysis (SA). This study develops a new Marine Predator Optimization with Natural Language Processing for Twitter Sentiment Analysis (MPONLP-TSA) for the COVID-19 Pandemic. The presented MPONLP-TSA model is focused on the recognition of sentiments that exist in the Twitter data during the COVID-19 pandemic. The presented MPONLP-TSA technique undergoes data preprocessing to convert the data into a useful format. Furthermore, the BERT model is used to derive word vectors. To detect and classify sentiments, a bidirectional recurrent neural network (BiRNN) model is utilized. Finally, the MPO algorithm is exploited for optimal hyperparameter tuning process, and it assists in enhancing the overall classification performance. The experimental validation of the MPONLP-TSA approach can be tested by utilizing the COVID-19 tweets dataset from the Kaggle repository. A wide comparable study reported a better outcome of the MPONLP-TSA method over current approaches. Full article
(This article belongs to the Special Issue Sustainable Application of Internet of Things and Artificial Intelligence)
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17 pages, 2391 KiB  
Article
Evaluation of Digital Transformation to Support Carbon Neutralization and Green Sustainable Development Based on the Vision of “Channel Computing Resources from the East to the West”
by Zhaoyang Wu, Xiaoning Wang, James Yong Liao, Hongrong Hou and Xiaokui Zhao
Sustainability 2023, 15(7), 6299; https://doi.org/10.3390/su15076299 - 6 Apr 2023
Cited by 5 | Viewed by 1578
Abstract
The long-term dependence on fossil fuels has led to an increase in carbon dioxide emissions. Global warming poses a huge risk to the sustainable development of the world, and even threatens human survival. The arrival of the carbon neutral era means that urban [...] Read more.
The long-term dependence on fossil fuels has led to an increase in carbon dioxide emissions. Global warming poses a huge risk to the sustainable development of the world, and even threatens human survival. The arrival of the carbon neutral era means that urban development is facing serious restrictions on carbon emissions. Digitization has brought profound changes to the economic and social development model, and would also change the pattern of urban competition. The goal of carbon neutrality is to change the low-carbon development model and structure, supplement it with negative carbon emissions, and comprehensively reduce greenhouse gas emissions. However, achieving the goal of carbon neutrality still faces many challenges and problems. For this reason, this paper analyzed the significance of carbon neutralization and the challenges faced by sustainable development to study the advantages of carbon neutralization under Digital Transformation (abbreviated as DT), and finally proposed the implementation path of carbon neutralization and sustainable development based on the channel of computing resources from the east to the west. The carbon emission effect before DT increased with time, while the carbon emission effect after DT decreased with time, in which the carbon emission effect after DT decreased by 47.9% compared with that before DT. The post-DT industry digitalization degree and the carbon trading system perfection degree were better than those before DT. The post-DT industry digitalization degree was 10.4% higher than that before DT, and the carbon trading system perfection degree was 9.5% higher than that before DT. In a word, DT and channeling computing resources from the east to the west can promote the realization of carbon neutrality and sustainable development. Full article
(This article belongs to the Special Issue Sustainable Application of Internet of Things and Artificial Intelligence)
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13 pages, 1382 KiB  
Article
Evaluation of Economic Security and Environmental Protection Benefits from the Perspective of Sustainable Development and Technological Ecological Environment
by Jingtong Li and Qing Hai
Sustainability 2023, 15(7), 6072; https://doi.org/10.3390/su15076072 - 31 Mar 2023
Cited by 6 | Viewed by 2825
Abstract
Under the concept of sustainable development, problems such as high resource consumption, serious environmental pollution and ecosystem degradation are the main factors restricting the sustainable development of economy. This paper aims to analyze the benefits of economic security and environmental protection from the [...] Read more.
Under the concept of sustainable development, problems such as high resource consumption, serious environmental pollution and ecosystem degradation are the main factors restricting the sustainable development of economy. This paper aims to analyze the benefits of economic security and environmental protection from the perspective of sustainable development and scientific and technological ecological environment. This paper puts forward the construction of the indicator system for the coordinated development of eco-technology innovation and economic environment, and analyzes the experimental results of economic security and environmental benefits on this basis. The experimental results of this paper show that after the implementation of the eco-technology innovation management system (hereinafter referred to as IEIMS for convenience), the material utilization rate and unit cost are essentially stable, and the cost is significantly lower than before. Full article
(This article belongs to the Special Issue Sustainable Application of Internet of Things and Artificial Intelligence)
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13 pages, 1486 KiB  
Article
Infiltration Approach of Green Environmental Protection Education in the View of Sustainable Development
by Yawen Su and Hongxia Zhao
Sustainability 2023, 15(6), 5287; https://doi.org/10.3390/su15065287 - 16 Mar 2023
Viewed by 1756
Abstract
In today’s world, global ecological environment problems are increasingly serious, and human beings have gradually realized the importance of protecting the ecological environment. With the continuous deepening of the concept of sustainable development, the concept of green education has also aroused great attention [...] Read more.
In today’s world, global ecological environment problems are increasingly serious, and human beings have gradually realized the importance of protecting the ecological environment. With the continuous deepening of the concept of sustainable development, the concept of green education has also aroused great attention in the education sector; moreover, its concept has gradually been applied to education and teaching. However, there are many problems in the application of green education. For example, the current green education system is not sound, the importance of green education in schools is not obvious, and there is no effective green teacher training program. In order to improve the current application of green education, this paper proposes to integrate the concept of sustainable development into the teaching of green environmental protection education. Through the construction of a sound green education system and green education classrooms, green ecological ideas would thus be infiltrated into many aspects of education and teaching. Due to the uneven distribution of educational resources in many regions, the quality of education in these regions is also uneven. In order to improve this situation, this paper also constructs an educational resource distribution model. Furthermore, this paper also analyzes the educational resource allocation model by using a differential evolution algorithm. From the experimental results, under the algorithm in this paper, the average student–teacher ratio of each county was 4.93, and the average number of books per student was 38.92. The average running time of teacher resources and book resources allocated by the model was found to be 1.47 s and 1.39 s, respectively. Under the traditional algorithm, the average student–teacher ratio in each county was 5.93, and the average number of books per student in each county was 31.8. The average running time of teacher resources and book resources allocated by the model was 2.36 s and 2.58 s, respectively. It can be seen from the above data that the algorithm in this paper can effectively optimize the allocation rationality of the educational resource allocation model and shorten the running time of resource allocation. Full article
(This article belongs to the Special Issue Sustainable Application of Internet of Things and Artificial Intelligence)
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