sustainability-logo

Journal Browser

Journal Browser

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: closed (31 December 2023) | Viewed by 3325

Special Issue Editors


E-Mail Website
Guest Editor
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

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of 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 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

  • 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 (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 5632 KiB  
Article
Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence
by Abdullah Addas, Muhammad Tahir and Najma Ismat
Sustainability 2024, 16(1), 355; https://doi.org/10.3390/su16010355 - 30 Dec 2023
Cited by 2 | Viewed by 1355
Abstract
Water sustainability will be scarce in the coming decades because of global warming, an alarming situation for irrigation systems. The key requirement for crop production is water, and it also needs to fulfill the requirements of the ever-increasing population around the globe. The [...] Read more.
Water sustainability will be scarce in the coming decades because of global warming, an alarming situation for irrigation systems. The key requirement for crop production is water, and it also needs to fulfill the requirements of the ever-increasing population around the globe. The changing climate significantly impacts agriculture production due to the extreme weather conditions that prevail in various regions. Since urbanization is increasing worldwide, smart cities must find innovative ways to grow food sustainably within built environments. This paper explores how precision agriculture powered by artificial intelligence (AI) can transform crop farms (CF) to enhance food security, nutrition, and environmental sustainability. We developed a robotic CF prototype that uses deep reinforcement learning to optimize seeding, watering, and crop maintenance in response to real-time sensor data. The system was tested in a simulated CF setting and benchmarked. The results revealed a 26% increase in crop yield, a 41% reduction in water utilization, and a 33% decrease in chemical use. We employed AI-enabled precision farming to improve agriculture’s efficiency, sustainability, and productivity within smart cities. The widespread adoption of such technologies makes food supplies resilient, reduces land, and minimizes agriculture’s environmental footprint. This study also qualitatively assessed the broader implications of AI-enabled precision farming. Interviews with farmers and stakeholders were conducted, which revealed the benefits of the proposed approach. The multidimensional impacts of precision crop farming beyond measurable outcomes emphasize its potential to foster social cohesion and well-being in urban communities. Full article
Show Figures

Figure 1

14 pages, 3076 KiB  
Article
Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste
by Parthasarathy Velusamy, Jagadeesan Srinivasan, Nithyaselvakumari Subramanian, Rakesh Kumar Mahendran, Muhammad Qaiser Saleem, Maqbool Ahmad, Muhammad Shafiq and Jin-Ghoo Choi
Sustainability 2023, 15(7), 6088; https://doi.org/10.3390/su15076088 - 31 Mar 2023
Cited by 3 | Viewed by 1291
Abstract
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
Show Figures

Figure 1

Back to TopTop