applsci-logo

Journal Browser

Journal Browser

Edge-Enabled Big Data Intelligence for 6G and IoT Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1366

Special Issue Editors


E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University of Technology (Taipei Tech), Taipei 10608, Taiwan
Interests: big data management and processing; uncertain data management; data science; data management over edge computing; spatial data processing; data streams; ad hoc and sensor networks; location-based services
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: UAV networks; edge intelligence; IoT data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The pervasive integration of the Internet of Things (IoT) is the cornerstone in the development of smart cities, healthcare advancements, and the evolution of Industry 4.0. This interconnected landscape generates an unprecedented volume of data, compelling the need for sophisticated big data processing and analysis technologies. These advancements not only streamline operations but also enhance overall efficiency and intelligence. The insights derived from data processing present unparalleled opportunities for a spectrum of emerging applications. However, it is widely recognized that effective big data processing and analysis demand substantial computing and storage resources, a demand that cloud computing readily fulfills, diverging from traditional IT methods.

Concurrently, the exigencies of burgeoning 6G applications have accentuated the necessity for swift field data analysis, particularly in achieving low-latency responses for tasks like autonomous driving and intelligent control within manufacturing and transportation sectors. The potential to fulfill these requirements lies within the realm of cloud/fog/edge computing, offering a compelling avenue for big data intelligence.

This Special Issue endeavors to explore diverse facets of big data processing and analysis interconnected with cloud/fog/edge computing for IoT applications. It encompasses a broad array of subjects, and potential topics include, but are not restricted to, the following:

  • Cloud computing, fog computing, and edge computing in 6G and IoT;
  • Novel theories, concepts, and paradigms of the convergence of AI, IoT, and Edge–Cloud;
  • Artificial Intelligence, machine learning, and data science in/for Edge–Cloud–IoT;
  • Distributed computing architectures, algorithms, and models in 6G and IoT;
  • IoT data analytics models, algorithms, and applications;
  • Edge-enabled big data intelligence in blockchain IoT;
  • Explainable AI for IoT data processing;
  • Big data intelligence for IoT security (authentication, access control, security models), privacy preservation, and data protection;
  • Information integrity and fusion in IoT;
  • Learned indices for efficient data processing in IoT;
  • Big data intelligence for IoT communications and networking;
  • Multi-objective decision making/optimization in 6G and IoT applications;
  • Application and case studies (healthcare, Industry 4.0, energy, smart city, finance, etc.).

Prof. Dr. Chuan-Ming Liu
Dr. Chuan-Chi Lai
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. Applied Sciences 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

  • 6G
  • IoT
  • cloud computing
  • fog computing
  • edge computing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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

Research

19 pages, 2093 KiB  
Article
Hypertuning-Based Ensemble Machine Learning Approach for Real-Time Water Quality Monitoring and Prediction
by Md. Shamim Bin Shahid, Habibur Rahman Rifat, Md Ashraf Uddin, Md Manowarul Islam, Md. Zulfiker Mahmud, Md Kowsar Hossain Sakib and Arun Roy
Appl. Sci. 2024, 14(19), 8622; https://doi.org/10.3390/app14198622 - 24 Sep 2024
Viewed by 879
Abstract
In the present day, the health of the populace is significantly jeopardized by the presence of contaminated water, and the majority of the population is unaware of the distinction between safe and unsafe water consumption. Agricultural, industrial, and other human-induced activities are causing [...] Read more.
In the present day, the health of the populace is significantly jeopardized by the presence of contaminated water, and the majority of the population is unaware of the distinction between safe and unsafe water consumption. Agricultural, industrial, and other human-induced activities are causing a significant decline in the availability of drinking water. Consequently, the issue of ensuring the safety of ingesting water is becoming increasingly prevalent. People should be aware of the purity of the water and the locations where it can be used in order to resolve this situation. There are numerous IoT-based system architectures that are capable of monitoring water parameters; however, the majority of these architectures do not allow for real-time water quality prediction or visualization. In order to achieve this, we suggest a wireless framework that is based on the Internet of Things (IoT). The sensors are able to capture water parameters and transmit the data to the cloud, where a machine learning (ML) model operates to classify the water quality. After that, Grafana enables us to effortlessly visualize the real-time data and predictions from any location. We employed a multi-class dataset from China for the model’s construction. GridSearchCV was implemented to identify the optimal parameters for model optimization. The proposed model is a combination of the Random Forest (RF), Extreme Gradient Boosting (XGB), and Histogram Gradient Boosting (HGB) models. The accuracy of the model for the China dataset was 99.80%. To assess the robustness of the proposed model, we acquired a new dataset from the Bangladesh Water Development Board (BWDB) and used it to test the proposed model. The model’s accuracy for this dataset was 99.72%. In summary, the proposed wireless IoT framework enables individuals to effortlessly monitor the purity of water and view its parameters from any location. Full article
(This article belongs to the Special Issue Edge-Enabled Big Data Intelligence for 6G and IoT Applications)
Show Figures

Figure 1

Back to TopTop