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Advances in Big Data and Internet of Things
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Advances in Big Data and Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 2949

Special Issue Editors

Dr. João Alexandre Lobo Marques

E-Mail Website
Guest Editor
Department of Business Administration, University of Saint Joseph, Macau 999078, China
Interests: bioengineering; digital signal processing; image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Simon James Fong

E-Mail Website
Guest Editor
Faculty of Science and Technology, University of Macau, Macau 999078, China
Interests: E-commerce; data mining; business intelligence; intelligent agent technology; electronic governance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement and widespread penetration of mobile networks, web-based information creation and sharing, and software-defined networking technology have enabled the sensing, prediction, and control of the physical world with information technology. Every business process can be empowered, and therefore, various industries have redesigned their business models and processes along the Internet of Things (IoT) paradigm.

The main purpose of this Special Issue is to provide an international platform for presenting and publishing the latest scientific research outcomes related to the topics of big data and the Internet of Things.

Dr. João Alexandre Lobo Marques
Dr. Simon James Fong
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. Sensors 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 2600 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

  • cloud and grid computing for big data
  • data mining and machine learning
  • artificial intelligence and data sciences
  • cloud computing and service data analysis
  • software architecture and middleware
  • performance evaluation and modeling
  • networking and communication protocols
  • machine-to-machine communications
  • software engineering for IoT and IoE
  • machine learning and deep learning approaches data analytics
  • technological focus for smart environments
  • architecture for secure and interactive IoT
  • social implications and ethics for IoT intelligence
  • systems for IoT and services computing
  • sensor networks and remote diagnosis and development
  • intelligent infrastructure and guidance systems for green vehicles
  • systems and smart cities
  • 6G-enabled IoT

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

Published Papers (2 papers)

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Research

21 pages, 2045 KiB  
Article
A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning
by Keyue Yan, Chi-Fai Lam, Simon Fong, João Alexandre Lobo Marques, Richard Charles Millham and Sabah Mohammed
Sensors 2025, 25(4), 1126; https://doi.org/10.3390/s25041126 - 13 Feb 2025
Viewed by 840
Abstract
Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on prediction and pattern recognition. In contrast, causal [...] Read more.
Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on prediction and pattern recognition. In contrast, causal machine learning goes a step further by revealing causal relationships between different variables. We explore a novel concept called Double Machine Learning that embraces causal machine learning in this research. The core goal is to select independent variables from a gesture identification problem that are causally related to final gesture results. This selection allows us to classify and analyze gestures more efficiently, thereby improving models’ performance and interpretability. Compared to commonly used feature selection methods such as Variance Threshold, Select From Model, Principal Component Analysis, Least Absolute Shrinkage and Selection Operator, Artificial Neural Network, and TabNet, Double Machine Learning methods focus more on causal relationships between variables rather than correlations. Our research shows that variables selected using the Double Machine Learning method perform well under different classification models, with final results significantly better than those of traditional methods. This novel Double Machine Learning-based approach offers researchers a valuable perspective for feature selection and model construction. It enhances the model’s ability to uncover causal relationships within complex data. Variables with causal significance can be more informative than those with only correlative significance, thus improving overall prediction performance and reliability. Full article
(This article belongs to the Special Issue Advances in Big Data and Internet of Things)
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16 pages, 2963 KiB  
Article
An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams
by Ibrahim Mutambik
Sensors 2024, 24(22), 7412; https://doi.org/10.3390/s24227412 - 20 Nov 2024
Cited by 3 | Viewed by 1347
Abstract
The rapid growth of data streams, propelled by the proliferation of sensors and Internet of Things (IoT) devices, presents significant challenges for real-time clustering of high-dimensional data. Traditional clustering algorithms struggle with high dimensionality, memory and time constraints, and adapting to dynamically evolving [...] Read more.
The rapid growth of data streams, propelled by the proliferation of sensors and Internet of Things (IoT) devices, presents significant challenges for real-time clustering of high-dimensional data. Traditional clustering algorithms struggle with high dimensionality, memory and time constraints, and adapting to dynamically evolving data. Existing dimensionality reduction methods often neglect feature ranking, leading to suboptimal clustering performance. To address these issues, we introduce E-Stream, a novel entropy-based clustering algorithm for high-dimensional data streams. E-Stream performs real-time feature ranking based on entropy within a sliding time window to identify the most informative features, which are then utilized with the DenStream algorithm for efficient clustering. We evaluated E-Stream using the NSL-KDD dataset, comparing it against DenStream, CluStream, and MR-Stream. The evaluation metrics included the average F-Measure, Jaccard Index, Fowlkes–Mallows Index, Purity, and Rand Index. The results show that E-Stream outperformed the baseline algorithms in both clustering accuracy and computational efficiency while effectively reducing dimensionality. E-Stream also demonstrated significantly less memory consumption and fewer computational requirements, highlighting its suitability for real-time processing of high-dimensional data streams. Despite its strengths, E-Stream requires manual parameter adjustment and assumes a consistent number of active features, which may limit its adaptability to diverse datasets. Future work will focus on developing a fully autonomous, parameter-free version of the algorithm, incorporating mechanisms to handle missing features and improving the management of evolving clusters to enhance robustness and adaptability in dynamic IoT environments. Full article
(This article belongs to the Special Issue Advances in Big Data and Internet of Things)
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