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Special Issue "Data Engineering in the 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: 20 May 2023 | Viewed by 7140

Special Issue Editors

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
Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan City 333, Taiwan
Interests: multimedia communications; multimedia security; embedded systems
Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin 640301,Taiwan
Interests: Internet of Thing; mobile application design; artificial intelligence; web technology

Special Issue Information

Dear Colleagues,

Recent years have seen explosive and exciting advances in the field of the Internet of Things (IoT) that have enjoyed tremendous success in varieties 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 the data engineering with management and analysis of the data coming off those sensors. With the proliferation of the different forms of data in IoT applications, the need for data engineering techniques can result in-depth processing, analysis, indexing, learning, mining, searching, management, and retrieval of data.

This Special Issue will 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 to some aspect 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

(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
Prof. Dr. Chia-Hui Wang
Prof. 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 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

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

Published Papers (8 papers)

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Research

Article
LAD: Layer-Wise Adaptive Distillation for BERT Model Compression
Sensors 2023, 23(3), 1483; https://doi.org/10.3390/s23031483 - 28 Jan 2023
Viewed by 645
Abstract
Recent advances with large-scale pre-trained language models (e.g., BERT) have brought significant potential to natural language processing. However, the large model size hinders their use in IoT and edge devices. Several studies have utilized task-specific knowledge distillation to compress the pre-trained language models. [...] Read more.
Recent advances with large-scale pre-trained language models (e.g., BERT) have brought significant potential to natural language processing. However, the large model size hinders their use in IoT and edge devices. Several studies have utilized task-specific knowledge distillation to compress the pre-trained language models. However, to reduce the number of layers in a large model, a sound strategy for distilling knowledge to a student model with fewer layers than the teacher model is lacking. In this work, we present Layer-wise Adaptive Distillation (LAD), a task-specific distillation framework that can be used to reduce the model size of BERT. We design an iterative aggregation mechanism with multiple gate blocks in LAD to adaptively distill layer-wise internal knowledge from the teacher model to the student model. The proposed method enables an effective knowledge transfer process for a student model, without skipping any teacher layers. The experimental results show that both the six-layer and four-layer LAD student models outperform previous task-specific distillation approaches during GLUE tasks. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Article
Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers
Sensors 2023, 23(1), 430; https://doi.org/10.3390/s23010430 - 30 Dec 2022
Viewed by 566
Abstract
In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by [...] Read more.
In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Article
Developing an Improved Ensemble Learning Approach for Predictive Maintenance in the Textile Manufacturing Process
Sensors 2022, 22(23), 9065; https://doi.org/10.3390/s22239065 - 22 Nov 2022
Viewed by 555
Abstract
With the rapid development of digital transformation, paper forms are digitalized as electronic forms (e-Forms). Existing data can be applied in predictive maintenance (PdM) for the enabling of intelligentization and automation manufacturing. This study aims to enhance the utilization of collected e-Form data [...] Read more.
With the rapid development of digital transformation, paper forms are digitalized as electronic forms (e-Forms). Existing data can be applied in predictive maintenance (PdM) for the enabling of intelligentization and automation manufacturing. This study aims to enhance the utilization of collected e-Form data though machine learning approaches and cloud computing to predict and provide maintenance actions. The ensemble learning approach (ELA) requires less computation time and has a simple hardware requirement; it is suitable for processing e-form data with specific attributes. This study proposed an improved ELA to predict the defective class of product data from a manufacturing site’s work order form. This study proposed the resource dispatching approach to arrange data with the corresponding emailing resource for automatic notification. This study’s novelty is the integration of cloud computing and an improved ELA for PdM to assist the textile product manufacturing process. The data analytics results show that the improved ensemble learning algorithm has over 98% accuracy and precision for defective product prediction. The validation results of the dispatching approach show that data can be correctly transmitted in a timely manner to the corresponding resource, along with a notification being sent to users. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Article
Scale-Mark-Based Gauge Reading for Gauge Sensors in Real Environments with Light and Perspective Distortions
Sensors 2022, 22(19), 7490; https://doi.org/10.3390/s22197490 - 02 Oct 2022
Viewed by 691
Abstract
Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own [...] Read more.
Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own specific methods for gauge reading. This paper presents a systematized solution called SGR (Scale-mark-based Gauge Reading) to automatically read gauge values from different types of gauges. Since most gauges have scale marks (circular or in an arc), our SGR algorithm utilizes PCA (principal components analysis) to find the primary eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the gauge center to ascertain the scale marks. Then, the endpoint of the gauge pointer is found to calculate the corresponding angles to the gauge’s center. Using OCR (optical character recognition), the corresponding dial values can be extracted to match with their scale marks. Finally, the gauge reading value is obtained by using the linear interpolation of these angles. Our experiments use four videos in real environments with light and perspective distortions. The gauges in the video are first detected by YOLOv4 and the detected regions are clipped as the input images. The obtained results show that SGR can automatically and successfully read gauge values. The average error of SGR is nearly 0.1% for the normal environment. When the environment becomes abnormal with respect to light and perspective distortions, the average error of SGR is still less than 0.5%. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Article
Cost-Effective Fitting Model for Indoor Positioning Systems Based on Bluetooth Low Energy
Sensors 2022, 22(16), 6007; https://doi.org/10.3390/s22166007 - 11 Aug 2022
Cited by 1 | Viewed by 677
Abstract
Bluetooth Low Energy (BLE) is a positioning technology that is commonly used in indoor positioning systems (IPS) such as shopping malls or underground parking lots, because of its low power consumption and the low cost of Bluetooth devices. It also maintains high positioning [...] Read more.
Bluetooth Low Energy (BLE) is a positioning technology that is commonly used in indoor positioning systems (IPS) such as shopping malls or underground parking lots, because of its low power consumption and the low cost of Bluetooth devices. It also maintains high positioning accuracy. Since the cost of BLE itself is low, it has now been used in larger environments such as parking lots or shopping malls for a long time. However, it is necessary to configure a large number of devices in the environment to obtain accurate positioning results. The most accurate method of using signal strength for positioning is the signal pattern-matching method. The positioning result is compared through a database with the overheads of time and labor costs, since the amount of data will be proportional to the size of the environment for BLE-IPS. A planar model that conforms to the signal strength in the environment was generated, wherein the database comparison method is replaced by an equation solution, to improve various costs but diminish the positioning accuracy. In this paper, we propose to further replace the planar model with a cost-effective fitting model to both save costs and improve positioning accuracy. The experimental results demonstrate that this model can effectively reduce the average positioning error in distance by 31%. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Article
Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis
Sensors 2022, 22(13), 4872; https://doi.org/10.3390/s22134872 - 28 Jun 2022
Viewed by 578
Abstract
The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. [...] Read more.
The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. In this paper, a high-capacity and multilayer reversible information hiding technique for digital images was presented. First, the integer Haar wavelet transform scheme converted the cover image from the spatial into the frequency domain that was used. Furthermore, we applied dynamic threshold analysis, the parameters of the predicted model, the location map, and the multilayer embedding method to improve the quality of the stego image and restore the cover image. In comparison with current algorithms, the proposed algorithm often had better embedding capacity versus image quality performance. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Article
Edge Computing of Online Bounded-Error Query for Energy-Efficient IoT Sensors
Sensors 2022, 22(13), 4799; https://doi.org/10.3390/s22134799 - 24 Jun 2022
Cited by 1 | Viewed by 799
Abstract
Since the power of transmitting one-bit data is higher than that of computing one thousand lines of code in IoT (Internet of Things) applications, it is very important to reduce communication costs to save battery power and prolong system lifetime. In IoT sensors, [...] Read more.
Since the power of transmitting one-bit data is higher than that of computing one thousand lines of code in IoT (Internet of Things) applications, it is very important to reduce communication costs to save battery power and prolong system lifetime. In IoT sensors, the transformation of physical phenomena to data is usually with distortion (bounded-error tolerance). It introduces bounded-error data in IoT applications according to their required QoS2 (quality-of-sensor service) or QoD (quality-of-decision making). In our previous work, we proposed a bounded-error data compression scheme called BESDC (Bounded-Error-pruned Sensor Data Compression) to reduce the point-to-point communication cost of WSNs (wireless sensor networks). Based on BESDC, this paper proposes an online bounded-error query (OBEQ) scheme with edge computing to handle the entire online query process. We propose a query filter scheme to reduce the query commands, which will inform WSN to return unnecessary queried data. It not only satisfies the QoS2/QoD requirements, but also reduces the communication cost to request sensing data. Our experiments use real data of WSN to demonstrate the query performance. Results show that an OBEQ with a query filter can reduce up to 88% of the communication cost when compared with the traditional online query process. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Article
Ensemble Machine Learning Model for Accurate Air Pollution Detection Using Commercial Gas Sensors
Sensors 2022, 22(12), 4393; https://doi.org/10.3390/s22124393 - 10 Jun 2022
Cited by 5 | Viewed by 1165
Abstract
This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advantage and become key components of IoT devices in atmospheric condition monitoring. However, their native coarse [...] Read more.
This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advantage and become key components of IoT devices in atmospheric condition monitoring. However, their native coarse resolution and poor selectivity limit their performance. Thus, we adopted recurrent neural network (RNN) models to extract the time-series concentration data characteristics and improve the detection accuracy. Firstly, four types of RNN models, LSTM and GRU, Bi-LSTM, and Bi-GRU, were optimized to define the best-performance single weak models for CO, O3, and NO2 gases, respectively. Next, ensemble models which integrate multiple single weak models with a dynamic model were defined and trained. The testing results show that the ensemble models perform better than the single weak models. Further, a retraining procedure was proposed to make the ensemble model more flexible to adapt to environmental conditions. The significantly improved determination coefficients show that the retraining helps the ensemble models maintain long-term stable sensing performance in an atmospheric environment. The result can serve as an essential reference for the applications of IoT devices with commercial gas sensors in environment condition monitoring. Full article
(This article belongs to the Special Issue Data Engineering in the Internet of Things)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Smart-watch Sensor with Deep Learning to Predict the Purchase Intention of Online Shopper
Authors: Ray-I Chang
Affiliation: Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei, Taiwan

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