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The Role and Impact of the Internet of Things (IoT) in Sustainable Smart Cities Volume II

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

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 5711

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Prof. Dr. Fadi Al-Turjman

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Artificial Intelligence Engineering Department, Research Center for AI and IoT, AI & Robotics Institute, Near East University, North Cyprus via Mersin 10, Nicosia 99138, Turkey
Interests: IoT; AI; cloud computing; blockchain; AIoT
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Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is becoming a key enabling technology, with multiple innovations supporting the smart city paradigm. For instance, providing multi-hop collaboration among the IoT sensor networks helps to reach possible services from Cloud computing facilities, and Machine Learning (ML) techniques are employed to adapt existing configurations. Moreover, emerging 5th and 6th generation (5G/6G) technologies can revolutionize ubiquitous computing with numerous sustainable applications built around various “smart” sensors enabled with cognition and ML techniques. ML showed an outstanding performance in a complicated task that requires human-like intelligence and intuition to perform. It is capable of detecting hidden structures in the data and using that to make smart decisions in smart-cities’ critical missions. To successfully accomplish this vision, cognitive IoT solutions are needed to reshape the existing smart applications towards further sustainable services in smart-city paradigms. This Special Issue brings together a broad multidisciplinary community studying cognitive architectures across science and engineering. It aims to integrate ideas, theories, models and techniques from across different disciplines on cognitive architectures. Potential topics include, but are not limited to:

IoT communication protocols for sustainable smart cities;
Cognitive resource management in IoT;
ML and intelligent IoT-based localization;
Intelligent blockchain for sustainable cities;
AI algorithms in the IoT era;
Enablers for intelligent/secured IoT;
Use cases enabled by intelligent IoT in Smart cities;
IoT and ML in smart cities;
ML and mobile assisted public safety and emergency IoT;
Intelligent 5G/6G communication for sustainable cities;
Design and evaluation of IoT test beds, prototypes and platform.

Prof. Dr. Fadi Al-Turjman
Guest Editor

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Keywords

IoT
sustainable smart cities
deep learning
machine learning
smart apps

Published Papers (4 papers)

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Research

18 pages, 3777 KiB

Open AccessArticle

Political Optimization Algorithm with a Hybrid Deep Learning Assisted Malicious URL Detection Model

by Mohammed Aljebreen, Fatma S. Alrayes, Sumayh S. Aljameel and Muhammad Kashif Saeed

Sustainability 2023, 15(24), 16811; https://doi.org/10.3390/su152416811 - 13 Dec 2023

Cited by 1 | Viewed by 810

Abstract

With the enhancement of the Internet of Things (IoT), smart cities have developed the idea of conventional urbanization. IoT networks permit distributed smart devices to collect and process data in smart city structures utilizing an open channel, the Internet. Accordingly, challenges like security, centralization, privacy (i.e., execution data poisoning and inference attacks), scalability, transparency, and verifiability restrict faster variations of smart cities. Detecting malicious URLs in an IoT environment is crucial to protect devices and the network from potential security threats. Malicious URL detection is an essential element of cybersecurity. It is established that malicious URL attacks mean large risks in smart cities, comprising financial damages, losses of personal identifications, online banking, losing data, and loss of user confidentiality in online businesses, namely e-commerce and employment of social media. Therefore, this paper concentrates on the proposal of a Political Optimization Algorithm by a Hybrid Deep Learning Assisted Malicious URL Detection and Classification for Cybersecurity (POAHDL-MDC) technique. The presented POAHDL-MDC technique identifies whether malicious URLs occur. To accomplish this, the POAHDL-MDC technique performs pre-processing to transform the data to a compatible format, and a Fast Text word embedding process is involved. For malicious URL recognition, a Hybrid Deep Learning (HDL) model integrates the features of stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM). Finally, POA is exploited for optimum hyperparameter tuning of the HDL technique. The simulation values of the POAHDL-MDC approach are tested on a Malicious URL database, and the outcome exhibits an improvement of the POAHDL-MDC technique with a maximal accuracy of 99.31%. Full article

(This article belongs to the Special Issue The Role and Impact of the Internet of Things (IoT) in Sustainable Smart Cities Volume II)

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21 pages, 1062 KiB

Open AccessArticle

Validation of Instruments for the Improvement of Interprofessional Education through Educational Management: An Internet of Things (IoT)-Based Machine Learning Approach

by Mustafa Mohamed, Fahriye Altinay, Zehra Altinay, Gokmen Dagli, Mehmet Altinay and Mutlu Soykurt

Sustainability 2023, 15(24), 16577; https://doi.org/10.3390/su152416577 - 6 Dec 2023

Viewed by 1008

Abstract

Educational management is the combination of human and material resources that supervises, plans, and responsibly executes an educational system with outcomes and consequences. However, when seeking improvements in interprofessional education and collaborative practice through the management of health professions, educational modules face significant obstacles and challenges. The primary goal of this study was to analyse data collected from discussion sessions and feedback from respondents concerning interprofessional education (IPE) management modules. Thus, this study used an explanatory and descriptive design to obtain responses from the selected group via a self-administered questionnaire and semi-structured interviews, and the results were limited to averages, i.e., frequency distributions and summary statistics. The results of this study reflect the positive responses from both subgroups and strongly support the further implementation of IPE in various aspects and continuing to improve and develop it. Four different artificial intelligence (AI) techniques were used to model interprofessional education improvement through educational management, using 20 questions from the questionnaire as the variables (19 input variables and 1 output variable). The modelling performance of the nonlinear and linear models could reliably predict the output in both the calibration and validation phases when considering the four performance metrics. These models were shown to be reliable tools for evaluating and modelling interprofessional education through educational management. Gaussian process regression (GPR) outperformed all the models in both the training and validation stages. Full article

(This article belongs to the Special Issue The Role and Impact of the Internet of Things (IoT) in Sustainable Smart Cities Volume II)

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24 pages, 1310 KiB

Open AccessArticle

How to Regain Green Consumer Trust after Greenwashing: Experimental Evidence from China

by Dandan Wang and Thomas Walker

Sustainability 2023, 15(19), 14436; https://doi.org/10.3390/su151914436 - 2 Oct 2023

Cited by 1 | Viewed by 2376

Abstract

Greenwashing leads to consumer skepticism of all green products as well as doubts about company claims regarding sustainability. However, the understanding of how to regain green consumer trust after greenwashing is rather limited. The authors fill this gap by exploring the psychological process of green consumers following intervention strategies designed to reduce greenwashing. We collect and interpret quantitative data from two psychological experiments, the first experiment identified two types of intervention strategies that serve to counter the negative impact of greenwashing and based on our findings from the first studies, we proposed and tested the moderating effect of two factors—implicit beliefs of consumers and companies who implement intervention strategies after greenwashing. The results indicate that distrust regulation (quantifying a product’s green attributes) and trustworthiness demonstration (visualizing environmental behaviors) are effective intervention strategies that can enable consumers to re-evaluate the cost-benefit of green products, and which may serve as critical psychological factors for green consumers and contribute to the degree of trust. Validation and comparative study of the derived results show that distrust regulation, followed by trustworthiness demonstration, has the best effect on increasing green trust after intervention. If the sequence is reversed, the effect of the intervention strategy is worse than if only one strategy had been applied. The implicit beliefs of green consumers play a moderating role between intervention strategies and reconsideration of the cost-benefit of green products. The behavior of genuinely green companies and the incremental beliefs of consumers can promote the intervention effect after greenwashing. Alternatively, the behavior of greenwashing companies can easily counter these effects. These findings contribute to knowledge about which psychological factors can promote or hinder the effectiveness of an intervention. Full article

(This article belongs to the Special Issue The Role and Impact of the Internet of Things (IoT) in Sustainable Smart Cities Volume II)

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15 pages, 1169 KiB

Open AccessArticle

Green Energy Consumption Path Selection and Optimization Algorithms in the Era of Low Carbon and Environmental Protection Digital Trade

by Jiayi Yuan, Ziqing Gao and Yijun Xiang

Sustainability 2023, 15(15), 12080; https://doi.org/10.3390/su151512080 - 7 Aug 2023

Cited by 1 | Viewed by 898

Abstract

In order to better study the chosen path of the consumption model of public green energy and more accurately predict consumers’ green energy consumer behavior, we take new energy vehicles as an example to explore the driving mechanism and internal mechanism of the public green energy consumption model from the perspective of motivation. We propose an ensemble learning model based on a stacking strategy. The model uses XGBoost, random forest and gradient lifting decision trees as primary learners to transform features, and uses logistic regression as a meta-learner to predict users’ consumer behavior. The experimental results show that this feature engineering method can significantly improve the accuracy rate in multiple model algorithms, and the prediction effect of the ensemble learning model is better than that of a single model, with the accuracy rate of 82.81%. In conclusion, the ensemble learning model based on a stacking strategy can effectively predict the public’s consumer behavior. This provides a theoretical basis and policy recommendations for promoting green energy products represented by new energy vehicles, thereby improving the practical path for proposing green energy consumption. Full article

(This article belongs to the Special Issue The Role and Impact of the Internet of Things (IoT) in Sustainable Smart Cities Volume II)

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The Role and Impact of the Internet of Things (IoT) in Sustainable Smart Cities Volume II

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