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Data Engineering in the Internet of Things—Second Edition
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Data Engineering in the Internet of Things—Second Edition

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

Deadline for manuscript submissions: 15 March 2025 | Viewed by 5850

Special Issue Editors

Prof. Dr. Ray-I Chang

E-Mail Website
Guest Editor
Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: multimedia networking; data mining; machine learning; Internet of Things; computer security
Special Issues, Collections and Topics in MDPI journals
Dr. Chia-Hui Wang

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan City 333, Taiwan
Interests: networking multimedia; Internet of Things; blockchain technology
Special Issues, Collections and Topics in MDPI journals
Dr. Yu-Hsin Hung

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
Interests: Internet of Thing; mobile application design; artificial intelligence; web technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have seen explosive and exciting advances in the field of the Internet of Things (IoT), where success has been enjoyed in a variety of applications such as digital health, smart city, environmental monitoring, and predictive maintenance. Real-world applications require sensor data to be timely, reliable, and suitable for decision making. The bounding condition in the IoT system is not going to be the deployment of sensors but rather data engineering with management and analysis of the data deriving from those sensors. With the proliferation of the different forms of data in IoT applications, the need for data engineering techniques can result in the in-depth processing, analysis, indexing, learning, mining, searching, management, and retrieval of data.

This Special Issue aims to highlight data engineering techniques that are applied in the design, development, and assessment of IoT systems to prepare, transform, publish, or otherwise make available data for different IoT applications. We are receptive to a range of papers suitable for some aspects of IoT data engineering. For sharing and exchanging research and results to problems encountered in today's IoT data engineering practitioners and researchers, we especially encourage submissions that make efforts to encompass the following:

  1. The most recent research results in IoT data engineering;
  2. The most recent practice problems that arise in IoT data engineering;
  3. The exchange of experiences in IoT data engineering technologies;
  4. The new issues and directions for future research and development in IoT data engineering.

Prof. Dr. Ray-I Chang
Dr. Chia-Hui Wang
Dr. Yu-Hsin Hung
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

  • IoT
  • data engineering
  • data warehouse and database
  • privacy and security
  • data processing
  • data analysis
  • data mining
  • data searching
  • data management
  • data retrieval

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Related Special Issue

Published Papers (4 papers)

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Research

15 pages, 27638 KiB  
Article
Query-Based Multiview Detection for Multiple Visual Sensor Networks
by Hung-Min Hsu, Xinyu Yuan, Yun-Yen Chuang, Wei Sun and Ray-I Chang
Sensors 2024, 24(15), 4773; https://doi.org/10.3390/s24154773 - 23 Jul 2024
Viewed by 670
Abstract
In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use multiple camera perspectives to address occlusion challenges with multiview aggregation being a crucial component. In these applications, data from various interconnected cameras are combined to create a [...] Read more.
In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use multiple camera perspectives to address occlusion challenges with multiview aggregation being a crucial component. In these applications, data from various interconnected cameras are combined to create a detailed ground plane feature. This feature is formed by projecting convolutional feature maps from multiple viewpoints and fusing them using uniform weighting. However, simply aggregating data from all cameras is not ideal due to different levels of occlusion depending on object positions and camera angles. To overcome this, we introduce QMVDet, a new query-based learning multiview detector, which incorporates an innovative camera-aware attention mechanism for aggregating multiview information. This mechanism selects the most reliable information from various camera views, thus minimizing the confusion caused by occlusions. Our method simultaneously utilizes both 2D and 3D data while maintaining 2D–3D multiview consistency to guide the multiview detection network’s training. The proposed approach achieves state-of-the-art accuracy on two leading multiview detection benchmarks, highlighting its effectiveness for IoT-based multiview detection scenarios. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things—Second Edition)
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14 pages, 1000 KiB  
Article
A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis
by Ivan Izonin, Roman Muzyka, Roman Tkachenko, Ivanna Dronyuk, Kyrylo Yemets and Stergios-Aristoteles Mitoulis
Sensors 2024, 24(15), 4762; https://doi.org/10.3390/s24154762 - 23 Jul 2024
Viewed by 2610
Abstract
We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the [...] Read more.
We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things—Second Edition)
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16 pages, 8185 KiB  
Article
Long Short-Term Memory Networks’ Application on Typhoon Wave Prediction for the Western Coast of Taiwan
by Wei-Ting Chao and Ting-Jung Kuo
Sensors 2024, 24(13), 4305; https://doi.org/10.3390/s24134305 - 2 Jul 2024
Viewed by 830
Abstract
Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of [...] Read more.
Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things—Second Edition)
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12 pages, 5013 KiB  
Article
Dual-Message QR Codes
by Kuo-Chien Chou and Ran-Zan Wang
Sensors 2024, 24(10), 3055; https://doi.org/10.3390/s24103055 - 11 May 2024
Viewed by 1241
Abstract
A novel dual-message QR code is proposed for carrying two individual messages that can be read by standard QR code readers: one from a close range and the other from a large distance. By exploring the module value determining the rule of typical [...] Read more.
A novel dual-message QR code is proposed for carrying two individual messages that can be read by standard QR code readers: one from a close range and the other from a large distance. By exploring the module value determining the rule of typical QR code readers, we designed two-state module blocks that can be recognized as different module values through changing the distance from which the QR code is scanned, and applied them to construct the proposed dual-message QR code. Experiments were conducted to test the readability of the two messages within a dual-message QR code, with the results demonstrating the high feasibility of the proposed method. The dual-message QR code can be applied for designing creative applications. For example, an interactive wedding card that can access the growing film of the groom and that of the bride interchangeably, which bring the viewers a higher-quality experience. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things—Second Edition)
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