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Special Issue "Future Prospects of Machine Learning, Data Mining and IoT in Smart and Sustainable Systems"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 833

Special Issue Editors

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: Internet of Things; vehicle-to-everything communication; smart cities; machine learning, computational intelligence; data science; human factors engineering
Special Issues, Collections and Topics in MDPI journals
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Interests: wireless sensor networks; internet-of-things; mobile and wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid urbanization requires all global cities to be transformed into smart cities to improve our standard of living in terms of government, people, transportation, environmental sustainability and more. The transformation of classic cities to smart cities will rely heavily on modern technologies in the computing paradigm, especially the internet of things (IoT), artificial intelligence (AI), machine learning (ML) and data mining (DM). In the near future, all the federated networks of traditional cities (e.g. transportation, electricity, information, waste management, etc.) will be served by a large number of IoT devices, which, in turn, will generate a large amount of unstructured and heterogeneous data. In this regard, relying solely on the existing infrastructure of the internet to disseminate urban big data is another unprecedented challenge. Hence, applications of ML and DM techniques and IoT technologies are of great significance for urban big data analysis, digitization and visualization of smart and sustainable systems. Advances in unmanned vehicles (UAVs) are also applicable to various smart city applications (e.g. traffic monitoring, personnel safety, rescue operations, etc.) due to advantages such as deployment capabilities, strong line-of-sight connectivity, and degrees of freedom. In addition, advances in data science, information theory, learning theory, edge computing, and computer intelligence can help us design more useful smart and sustainable urban networks.

This Special Issue aims to provide a multidisciplinary, up-to-date reference to current/future challenges and innovative solutions for smart and sustainable systems deployed in the urban cyberspace. To this end, we invite high-quality research papers on ML, DM techniques, IoT applications, and environmental research.

The topics of interest for this Special Issue include, but are not limited to, the following:

  • Urban big data modeling and evaluation;
  • Data-driven urban traffic management methods and applications;
  • Modern applications of ML and DM techniques in urban networks (e.g., classification and regression trees, random forests, association rules, clustering, Gaussian mixture models, artificial neural networks, Bayesian networks, forecasting methods, sequential patterns, support vector machines, etc.);
  • UAV-assisted platform for urban traffic monitoring and rescue control;
  • Semantic knowledge for urban big data analytics;
  • Ontology-based recommendation system in connected healthcare;
  • Reinforcement learning to evaluate car control big data;
  • DM and AI-based cloud for smart city big data architecture;
  • Knowledge graph and edge computing model for IoT applications in smart cities;
  • Big data analysis and IoT applications for smart grid, smart home, connected car, connected health, smart agriculture, smart retail, etc.;
  • Robust, sustainable and resilient smart city IoT infrastructure;
  • Optimized data security, privacy and trust for smart and sustainable urban networks;
  • D2D communication protocols and algorithms for city network control;
  • IoT to reduce traffic accidents, congestion, pollution, etc.;
  • AI, ML and big data analysis-based smart systems to convert municipal waste into valuable resources;
  • Innovative human–computer interaction models for intelligent and sustainable systems;
  • Legal, ethical and social considerations for the transition from classic cities to smart cities.

Dr. Muhammad Shafiq
Dr. Jin-Ghoo Choi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 2200 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.


  • artificial intelligence
  • big data
  • data mining
  • smart grid
  • internet of things
  • smart cities
  • smart home
  • machine learning
  • intelligent transportation systems
  • healthcare monitoring systems
  • computational intelligence
  • UAVs technology
  • data communication and visualization

Published Papers (1 paper)

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Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste
Sustainability 2023, 15(7), 6088; - 31 Mar 2023
Viewed by 555
Municipal solid waste (MSW) management is an essential element of present-day society. The proper storage and disposal of solid waste is critical to public health, safety, and environmental performance. The direct recovery of MSW into useful energy is a critical task. In addition, [...] Read more.
Municipal solid waste (MSW) management is an essential element of present-day society. The proper storage and disposal of solid waste is critical to public health, safety, and environmental performance. The direct recovery of MSW into useful energy is a critical task. In addition, the demand for conventional power supplies is high. As a strategy to solve these two problems, the technology to directly convert municipal solid waste into conventional energy to replace fossil fuels has been obtained. The hydrothermal carbonization (HTC) process is a thermochemical conversion process that utilizes heat to convert wet biomass feedstocks into hydrochar. Hydrochar with premium gasoline properties is used for fuel combustion for strength. The properties of fuel hydrochar, including C char (carbon content), HHV (higher heating value), and yield, are mainly based on the properties of the MSW. This study aimed to predict the properties of fuel hydrochar using a machine learning (ML) model. We employed an ensemble support vector machine (E-SVM) as the classifier, which was combined with the slime mode algorithm (SMA) for optimization and developed based on 281 data points. The model was primarily trained and tested on a fusion of three datasets: sewage sludge, leftovers, and cow dung. The proposed ESVM_SMA model achieved an excellent overall performance with an average R2 of 0.94 and RMSE of 2.62. Full article
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