Special Issue "Machine Learning, Data Mining, and IoT Applications in Smart and Sustainable Networks"

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

Deadline for manuscript submissions: 31 August 2022.

Special Issue Editors

Dr. Amjad Ali
E-Mail Website
Guest Editor
Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan
Interests: smart cities; cloud computing; machine learning; information security; device-to-device communication; haptic communications & tactile internet
Dr. Farman Ali
E-Mail Website
Guest Editor
Department of Software, Sejong University, Seoul 05006, Korea
Interests: data science; data mining, big data, sentiment analysis, social network analysis, medical informatics, machine learning, recommendation system, natural language processing
Prof. Dr. Jin-Ghoo Choi
E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Interests: wireless sensor networks; internet-of-things; mobile and wireless networks
Dr. Muhammad Shafiq
E-Mail Website
Chief Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Interests: Internet of Things; vehicle-to-everything communication; smart cities; machine learning, computational intelligence; data science; human factors engineering

Special Issue Information

Dear Colleagues,

Driven by rapid urbanization, we need all global cities to be transformed as Smart Cities in order to improve our living standards on so many dimensions like government, people, transportation, environmental sustainability, and much more. The transformation of classical Cities to Smart Cities will greatly depend on modern technologies in computing paradigms especially Internet-of-Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Data Mining (DM).  In near future, all the conglomerate networks of a conventional city (e.g., transportation, electricity, information, etc.) will be served by a wide range of IoT devices, which will generate a huge volume of unstructured and heterogeneous data in return. The lack of useful knowledge in such a Big Data is a huge hassle when it comes to decision-making and planning operations of Smart Cities while dealing with urgent challenges like energy/environmental sustainability, urban traffic management, information security, and so on. In this regard, the sole reliance over existing infrastructure of the Internet for communication of urban Big Data is another unprecedented challenge. Therefore, applications of ML and DM techniques (e.g., classification and regression trees, random forests, association rules, clustering, Gaussian mixture models, artificial neural networks, Bayesian networks, prediction methods, sequential patterns, support vector machines, etc.), AI, and IoT enabling technologies are of much interest in urban Big Data analytics, digitization, and visualization for smart and sustainable systems. Furthermore, the developments in data science, information theory, learning theory, edge computing, and computational intelligence could be helpful in adding intelligence to urban networks. The advents in Unmanned-Automated Vehicles (UAVs) are also significant in various applications (e.g., traffic surveillance, people safety, rescue operations, etc.) of Smart Cities due to a number of virtues like facility of deployment, strong line-of-sight links, and degrees of freedom. 

The aim of this special issue is to present a multidisciplinary state-of-the-art reference regarding theoretical and real-world challenges, and innovative solutions by inviting high-quality research papers spanning across ML and DM techniques, IoT applications (e.g., Smart Home, Smart Grid, Industrial IoT, Connected Car, Connected Healthcare, Smart Farming, Smart Retail, etc.), and environmental studies for sustainable networks deployed in the urban cyber domain.  

The topics of interest for this special issue include, but are not limited to:

  • Modeling and evaluation of urban Big Data
  • Data-driven methods and applications for urban traffic management
  • UAVs assisted platforms for urban traffic surveillance and rescue control
  • Semantic knowledge for urban Big Data analytics
  • Ontology-based recommendation system in Connected Healthcare
  • Reinforcement learning for assessment and evaluation of vehicle-actuated Big Data
  • DM and AI-based cloud for Big Data architectures in Smart Cities
  • Knowledge graph and edge computing models for IoT applications in Smart Cities
  • Big Data analytics and IoT applications for Smart Grid, Smart Home, Connected Car, Connected Health, Smart Farming, Smart Retail, etc.
  • High-performance sustainable and resilient infrastructure for IoT in Smart Cities
  • Optimized data security, privacy, and trust for smart and sustainable urban networks
  • Device-to-Device communication protocols and algorithms for urban networks
  • IoT for mitigating traffic accidents, congestion, environmental pollution, etc.
  • AI, ML and Big Data analytics-based systems for turning urban waste into value
  • Innovative Human-Computer Interaction models for smart and sustainable systems
  • Future perspectives for smart and sustainable networks in Smart Cities
  • Legal, ethical, and social considerations in the transformation of classical cities to Smart Cities 

Dr. Muhammad Shafiq
Dr. Amjad Ali
Dr. Farman Ali
Prof. Dr. Jin-Ghoo Choi
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 papers will be 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 1900 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
  • Data Mining
  • Artificial Intelligence
  • Intelligent Transportation System
  • Healthcare Monitoring System
  • Computational Intelligence
  • Big Data
  • Smart Cities
  • Smart Home
  • Smart Grid
  • Internet-of-Things
  • UAVs Technology
  • Data Communication and Visualization

Published Papers (6 papers)

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Research

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Article
Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network
Sustainability 2021, 13(17), 9775; https://doi.org/10.3390/su13179775 (registering DOI) - 31 Aug 2021
Abstract
Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic [...] Read more.
Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%. Full article
Article
Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment
Sustainability 2021, 13(16), 8900; https://doi.org/10.3390/su13168900 - 09 Aug 2021
Viewed by 346
Abstract
The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery [...] Read more.
The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines. Full article
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Article
An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy
Sustainability 2021, 13(11), 6199; https://doi.org/10.3390/su13116199 - 31 May 2021
Viewed by 606
Abstract
Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market [...] Read more.
Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units. Full article
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Article
A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data
Sustainability 2021, 13(9), 4888; https://doi.org/10.3390/su13094888 - 27 Apr 2021
Viewed by 488
Abstract
The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, [...] Read more.
The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics. Full article
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Article
A New Efficient Architecture for Adaptive Bit-Rate Video Streaming
Sustainability 2021, 13(8), 4541; https://doi.org/10.3390/su13084541 - 19 Apr 2021
Viewed by 584
Abstract
The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with [...] Read more.
The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with flawless or smooth video streaming in less time with high availability. Failure to provide satisfactory QoE results in the churning of the viewers. QoE depends on various factors, such as those related to the network infrastructure that significantly affects perceived quality. Furthermore, the video delivery’s impact also plays an essential role in the overall QoE that can be made efficient by delivering content through specialized content delivery architectures called Content Delivery Networks (CDNs). This article proposes a design that enables effective and efficient streaming, distribution, and caching multimedia content. Moreover, experiments are carried out for the factors impacting QoE, and their behavior is evaluated. The statistical data is taken from real architecture and analysis. Likewise, we have compared the response time and throughput with the varying segment size in adaptive bitrate video streaming. Moreover, resource usage is also analyzed by incorporating the effect of CPU consumption and energy consumption over segment size, which will be counted as effective efforts for sustainable development of multimedia systems. The proposed architecture is validated and indulged as a core component for video streaming based on the use case of a Mobile IPTV solution for 4G/LTE Users. Full article
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Review

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Review
A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification
Sustainability 2021, 13(17), 9597; https://doi.org/10.3390/su13179597 (registering DOI) - 26 Aug 2021
Viewed by 200
Abstract
Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote [...] Read more.
Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works. Full article
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