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Sustainability
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IoT and Computational Intelligence Applications in Digital and Sustainable Transitions
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IoT and Computational Intelligence Applications in Digital and Sustainable Transitions

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

Deadline for manuscript submissions: closed (25 July 2023) | Viewed by 2945

Special Issue Editors

Prof. Dr. Arun Kumar Sangaiah

E-Mail Website
Guest Editor
International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
Interests: Internet of Things; computational intelligence; sustainable computing
Special Issues, Collections and Topics in MDPI journals
Dr. Chitra Venugopal

E-Mail Website
Guest Editor
Department of Electrical Engineering and Renewable Energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA
Interests: Internet of Things; artificial intelligence
Dr. Adam Slowik

E-Mail Website
Guest Editor
Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland
Interests: artificial intelligence; soft computing

Special Issue Information

Dear Colleagues,

Emerging digital technologies has completely changed the way businesses and individuals perform daily life and business activities, potentially resulting in the digital transition. The concept of digital transition refers to the conversion of analogue processes to digital processes that allow digital tools to model processes and activities, thereby improving performance and productivity. Furthermore, the digital transition has increased the ability to develop and implement sustainable solutions. Sustainability can be defined as development that meets the needs of the present without compromising future generations' ability to meet their own needs. The concept incorporates multiple aspects of contemporary society, such as ecological, social, and economic concerns. It also seeks to strike a balance between environmental protection and economic growth.

The digital transition in environmental sustainability entails the use of technologies such as AI, big data analytics, IoT, and mobile technologies to develop and implement sustainability solutions in areas such as sustainable urban development, sustainable production, and pollution control. Emerging digital technologies in economic sustainability can drive transformation into a more sustainable circular economy, the digital sharing economy, and the establishment of sustainable manufacturing and infrastructure design.

The digital transition processes have shown an enormous capacity to develop and implement sustainable solutions, allowing for the resolution of a variety of issues such as poverty, high rates of species extinction, and a lack of equal opportunity. There is little emphasis on demonstrating the link between digital transition and sustainability. As a result, a lack of research hinders researchers from optimising digital tools to provide long-term solutions to the world's unsustainabilty issues. This Special Issue aims to cover all the latest developments and progress made in the field of IoT and computational intelligence, covering a variety of topics within sustainable computing and digital transitions, for the effective management of the digital transition that can achieve sustainability goals.

Prof. Dr. Arun Kumar Sangaiah
Dr. Chitra Venugopal
Dr. Adam Slowik
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

  • digital transition
  • computational intelligence
  • artificial intelligence
  • IoT
  • sustainability

Published Papers (2 papers)

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Research

18 pages, 1809 KiB  
Article
Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System
by Bhulakshmi Bonthu and Subaji Mohan
Sustainability 2023, 15(14), 10943; https://doi.org/10.3390/su151410943 - 12 Jul 2023
Viewed by 771
Abstract
Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received [...] Read more.
Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy. Full article
(This article belongs to the Special Issue IoT and Computational Intelligence Applications in Digital and Sustainable Transitions)
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25 pages, 1041 KiB  
Article
Application of Advanced Hybrid Models to Identify the Sustainable Financial Management Clients of Long-Term Care Insurance Policy
by You-Shyang Chen, Chien-Ku Lin, Jerome Chih-Lung Chou, Su-Fen Chen and Min-Hui Ting
Sustainability 2022, 14(19), 12485; https://doi.org/10.3390/su141912485 - 30 Sep 2022
Cited by 1 | Viewed by 1332
Abstract
The rapid growth of the aging population and the rate of disabled people with physical and mental disorders is increasing the demand for long-term care. The decline in family care could lead to social and economic collapse. In order to reduce the burden [...] Read more.
The rapid growth of the aging population and the rate of disabled people with physical and mental disorders is increasing the demand for long-term care. The decline in family care could lead to social and economic collapse. In order to reduce the burden of long-term care, long-term care insurance has become one of the most competitive products in the life insurance industry. In the previous literature review, few scholars engaged in the research on this topic with data mining technology, which was motivated to trigger the formation of this study and hoped to increase the different aspects of academic research. The purpose of this study is to develop the long-term insurance business from the original list of insurance clients, to predict whether the sustainable financial management clients will buy the long-term care insurance policies, and to establish a feasible prediction model to assist life insurance companies. This study aims to establish the classified prediction models of Models I~X, to dismantle the data with the percentage split and 10-fold cross validation, plus the application of two kinds of technology as feature selection and data discretization, for the data mining of twenty-three kinds of algorithms in seven different categories (Bayes, Function, Lazy, Meta, Misc, Rule, and Decision Tree) through the data collected from the insurance company database, and to select 20 conditional attributes and 1 decisional attribute (whether to buy the long-term insurance policy or not). The decision attribute is binary classification method for empirical data analysis. The empirical results show that: (1) the marital status, total number of policies purchased, and total amount of policies (including long-term care insurance) are found to be the three important factors affecting the decision attribute; (2) the most stable models are the advanced hybrid Models V and X; and (3) the best classifier is Decision Tree J48 algorithm for the study data used. Full article
(This article belongs to the Special Issue IoT and Computational Intelligence Applications in Digital and Sustainable Transitions)
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