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Advances of Future IoE Wireless Network Technology
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Advances of Future IoE Wireless Network Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 32472

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Gwo-Jiun Horng

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Guest Editor
Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Yung Kung, Tainan 710301, Taiwan
Interests: AIoT; IoE; mobile service and computing; wireless networks
Special Issues, Collections and Topics in MDPI journals
Dr. S.T. Aripriharta

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universitas Negeri Malang, Malang 65145, Indonesia
Interests: AI-IoT; low-power IoT protocols; self-powered IoT
Dr. Yao-Tung Tsou

E-Mail Website
Guest Editor
Department of Communications Engineering, Feng Chia University, Taichung 40724, Taiwan
Interests: information security and privacy; artificial intelligence application; IoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chia-Wei Tsai

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 404336, Taiwan
Interests: machine learning; quantum information and computation; quantum cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Everyone will know if the Internet of Everything (IoE) is different from Internet of Things (IoT). In fact, many parts of the world have smart technology. For example: smart home, smart city, smart health; it is all around us. Of course, these technologies also generate a lot of information at any time, so let us analyze and predict the possibilities of the future. The Internet of Things mainly includes devices and how they connect to each other or to people. The connectivity of everything is wider and affects many important components of everyday life.

The aim of this Special Issue is to report on new scenarios, technologies, and applications related to the concept of the Internet of Everything and discuss challenges and risks. IoT is devices that connect to each other, but IoE adds elements to people, and elements of data, and is an extension of IoT. There are four modules, including people, process, data, and things. People to People (P2P), Machine to Machine (M2M), or People to Machine (P2M), the generated data is collected through the Data module to make decisions, and then, putting the resulting command into process, data is transmitted to the machine by wireless networks, selecting the right time to do the right thing.

Topics of this Special Issue include, but are not limited to:

  • Intelligent applications for IoE ecosystems;
  • AIoE for edge and fog computing;
  • AIoE in healthcare applications;
  • Innovative wireless network architectures for the IoE;
  • Privacy issues;
  • Scalability issues;
  • Interaction models for the IoE;
  • IoE application pilots and experimentation;
  • Innovative digital signal processing for the IoE;
  • low-power IoT protocols.

Prof. Dr. Gwo-Jiun Horng
Dr. S.T. Aripriharta
Prof. Dr. Yao-Tung Tsou
Prof. Dr. Chia-Wei Tsai
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. Electronics 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
  • IoE
  • smart city
  • smart health
  • smart home
  • wireless network

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Published Papers (11 papers)

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Editorial

Jump to: Research

2 pages, 158 KiB  
Editorial
Advances of Future IoE Wireless Network Technology
by Gwo-Jiun Horng
Electronics 2023, 12(10), 2164; https://doi.org/10.3390/electronics12102164 - 9 May 2023
Cited by 2 | Viewed by 1168
Abstract
The Internet of Everything (IoE) is a concept that refers to the interconnectivity of various devices, objects, and systems, which can communicate and exchange data to enable intelligent decision making [...] Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)

Research

Jump to: Editorial

18 pages, 5421 KiB  
Article
The Development of an Autonomous Vehicle Training and Verification System for the Purpose of Teaching Experiments
by Chien-Chung Wu, Yu-Cheng Wu and Yu-Kai Liang
Electronics 2023, 12(8), 1874; https://doi.org/10.3390/electronics12081874 - 15 Apr 2023
Cited by 1 | Viewed by 3298
Abstract
To cultivate students’ skills in building autonomous vehicle neural network models and to reduce development costs, a system was developed for on-campus training and verification. The system includes (a) autonomous vehicles, (b) test tracks, (c) a data collection and training system, and (d) [...] Read more.
To cultivate students’ skills in building autonomous vehicle neural network models and to reduce development costs, a system was developed for on-campus training and verification. The system includes (a) autonomous vehicles, (b) test tracks, (c) a data collection and training system, and (d) a test and scoring system. In this system, students can assemble the hardware of the vehicle, configure the software, and choose or modify the neural network model in class. They can then collect the necessary data for the model and train the model. Finally, the system’s test and scoring system can be used to test and verify the performance of the autonomous vehicle. The study found that vehicle turning is better controlled by a motor and steering mechanism, and the camera should be mounted in a high position and at the front of the vehicle to avoid interference with the steering mechanism. Additionally, the study revealed that the training and testing speeds of the autonomous vehicle are dependent on each other, and high-quality results cannot be obtained solely by training a model based on camera images. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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23 pages, 6373 KiB  
Article
GDPR Personal Privacy Security Mechanism for Smart Home System
by Yun-Yun Jhuang, Yu-Hui Yan and Gwo-Jiun Horng
Electronics 2023, 12(4), 831; https://doi.org/10.3390/electronics12040831 - 7 Feb 2023
Cited by 5 | Viewed by 2599
Abstract
In the era of vigorous development of the Internet of Things (IoT), the IoT has been widely used in people’s daily life. Before the user starts using an IoT product, the developer provides a privacy consent form for the user to fill in. [...] Read more.
In the era of vigorous development of the Internet of Things (IoT), the IoT has been widely used in people’s daily life. Before the user starts using an IoT product, the developer provides a privacy consent form for the user to fill in. However, the content of the consent form is usually too long for the user to read, and the user neglects the provisions related to privacy use, which often results in personal information being recorded in the database of the product without the user’s knowledge. To protect users’ informed use, we propose a privacy protection standard of the general data protection regulation (GDPR) law applicable to smart-family-related applications and data security with a consensus mechanism. We also propose a unified device data format agreement. Each product can communicate with each other through a smart housekeeper and can collect personal information between its own products and users based on the personal data protection law. Through practice, we demonstrate the feasibility of this open system. In addition, we also collected 70 questionnaires. If the GDPR specification is placed on smart appliances, about 90% of people can accept smart appliances. If smart appliances can be compatible with different brands’ unified standards, about 97% of people can accept smart appliances. Therefore, we recommend the introduction of GDPR specifications for smart home appliances. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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15 pages, 3109 KiB  
Article
Repetition with Learning Approaches in Massive Machine Type Communications
by Li-Sheng Chen, Chih-Hsiang Ho, Cheng-Chang Chen, Yu-Shan Liang and Sy-Yen Kuo
Electronics 2022, 11(22), 3649; https://doi.org/10.3390/electronics11223649 - 8 Nov 2022
Cited by 1 | Viewed by 1314
Abstract
In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy [...] Read more.
In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy consumption. An insufficient number of repetitions prevents the successful deciphering of the data by the receivers, leading to a high bit error rate. The present study developed adaptive repetition approaches with the k-nearest neighbor (KNN) and support vector machine (SVM) to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in the 5G mMTC scenario. The simulation results showed that the proposed repetition with the learning approach effectively improved the probability of successful transmission, the resource utilization, the average number of repetitions, and the average energy consumption. It is therefore more suitable for the eMTC system in the mMTC scenario than the common lookup table. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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12 pages, 4810 KiB  
Article
Classifying Conditions of Speckle and Wrinkle on the Human Face: A Deep Learning Approach
by Tsai-Rong Chang and Ming-Yen Tsai
Electronics 2022, 11(21), 3623; https://doi.org/10.3390/electronics11213623 - 6 Nov 2022
Cited by 4 | Viewed by 2350
Abstract
Speckles and wrinkles are common skin conditions on the face, with occurrence ranging from mild to severe, affecting an individual in various ways. In this study, we aim to detect these conditions using an intelligent deep learning approach. First, we applied a face [...] Read more.
Speckles and wrinkles are common skin conditions on the face, with occurrence ranging from mild to severe, affecting an individual in various ways. In this study, we aim to detect these conditions using an intelligent deep learning approach. First, we applied a face detection model and identified the face image using face positioning techniques. We then split the face into three polygonal areas (forehead, eyes, and cheeks) based on 81 position points. Skin conditions in the images were firstly judged by skin experts and subjectively classified into different categories, from good to bad. Wrinkles were classified into five categories, and speckles were classified into four categories. Next, data augmentation was performed using the following manipulations: changing the HSV hue, image rotation, and horizontal flipping of the original image, in order to facilitate deep learning using the Resnet models. We tested the training using these models each with a different number of layers: ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. Finally, the K-fold (K = 10) cross-validation process was applied to obtain more rigorous results. Results of the classification are, in general, satisfactory. When compared across models and across skin features, we found that Resnet performance is generally better in terms of average classification accuracy when its architecture has more layers. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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19 pages, 2618 KiB  
Article
Application of Generative Adversarial Network and Diverse Feature Extraction Methods to Enhance Classification Accuracy of Tool-Wear Status
by Bo-Xiang Chen, Yi-Chung Chen, Chee-Hoe Loh, Ying-Chun Chou, Fu-Cheng Wang and Chwen-Tzeng Su
Electronics 2022, 11(15), 2364; https://doi.org/10.3390/electronics11152364 - 28 Jul 2022
Cited by 5 | Viewed by 1771
Abstract
The means of accurately determining tool-wear status has long been important to manufacturers. Tool-wear status classification enables factories to avoid the unnecessary costs incurred by replacing tools too early and to prevent product damage caused by overly worn tools. While researchers have examined [...] Read more.
The means of accurately determining tool-wear status has long been important to manufacturers. Tool-wear status classification enables factories to avoid the unnecessary costs incurred by replacing tools too early and to prevent product damage caused by overly worn tools. While researchers have examined this topic for over a decade, most existing studies have focused on model development but have neglected two fundamental issues in machine learning: data imbalance and feature extraction. In view of this, we propose two improvements: (1) using a generative adversarial network to generate realistic computer numerical control machine vibration data to overcome data imbalance and (2) extracting features in the time domain, the frequency domain, and the time–frequency domain simultaneously for modeling and integrating these in an ensemble model. The experiment results demonstrate how both proposed modifications are reasonable and valid. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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14 pages, 2978 KiB  
Article
A Cloud-Edge-Smart IoT Architecture for Speeding Up the Deployment of Neural Network Models with Transfer Learning Techniques
by Tz-Heng Hsu, Zhi-Hao Wang and Aaron Raymond See
Electronics 2022, 11(14), 2255; https://doi.org/10.3390/electronics11142255 - 19 Jul 2022
Cited by 6 | Viewed by 2873
Abstract
Existing edge computing architectures do not support the updating of neural network models, nor are they optimized for storing, updating, and transmitting different neural network models to a large number of IoT devices. In this paper, a cloud-edge smart IoT architecture for speeding [...] Read more.
Existing edge computing architectures do not support the updating of neural network models, nor are they optimized for storing, updating, and transmitting different neural network models to a large number of IoT devices. In this paper, a cloud-edge smart IoT architecture for speeding up the deployment of neural network models with transfer learning techniques is proposed. A new model deployment and update mechanism based on the share weight characteristic of transfer learning is proposed to address the model deployment issues associated with the significant number of IoT devices. The proposed mechanism compares the feature weight and parameter difference between the old and new models whenever a new model is trained. With the proposed mechanism, the neural network model can be updated on IoT devices with just a small quantity of data sent. Utilizing the proposed collaborative edge computing platform, we demonstrate a significant reduction in network bandwidth transmission and an improved deployment speed of neural network models. Subsequently, the service quality of smart IoT applications can be enhanced. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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24 pages, 10907 KiB  
Article
Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder
by Chia-Wei Tsai, Kuei-Chun Chiang, Hsin-Yuan Hsieh, Chun-Wei Yang, Jason Lin and Yao-Chung Chang
Electronics 2022, 11(9), 1450; https://doi.org/10.3390/electronics11091450 - 30 Apr 2022
Cited by 10 | Viewed by 2948
Abstract
Due to the climate crisis, energy-saving issues and carbon reduction have become the top priority for all countries. Owing to the increasing popularity of advanced metering infrastructure and smart meters, the cost of acquiring data on residential electricity consumption has substantially dropped. This [...] Read more.
Due to the climate crisis, energy-saving issues and carbon reduction have become the top priority for all countries. Owing to the increasing popularity of advanced metering infrastructure and smart meters, the cost of acquiring data on residential electricity consumption has substantially dropped. This change promotes the analysis of residential electricity consumption, which features both small and complicated consumption behaviors, using machine learning to become an important research topic among various energy saving and carbon reduction measures. The main subtopic of this subject is the identification of abnormal electricity consumption behaviors. At present, anomaly detection is typically realized using models based on low-level features directly collected by sensors and electricity meters. However, due to the significant number of dimensions and a large amount of redundant information in these low-level features, the training efficiency of the model is often low. To overcome this, this study adopts an autoencoder, which is a deep learning technology, to extract the high-level electricity consumption information of residential users to improve the anomaly detection performance of the model. Subsequently, this study trains one-class SVM models for anomaly detection by using the high-level features of five actual residential users to verify the benefits of high-level features. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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17 pages, 17863 KiB  
Article
Development of a Face Prediction System for Missing Children in a Smart City Safety Network
by Ding-Chau Wang, Zhi-Jing Tsai, Chao-Chun Chen and Gwo-Jiun Horng
Electronics 2022, 11(9), 1440; https://doi.org/10.3390/electronics11091440 - 29 Apr 2022
Cited by 3 | Viewed by 4596
Abstract
Cases of missing children not being found are rare, but they continue to occur. If the child is not found immediately, the parents may not be able to identify the child’s appearance because they have not seen their child for a long time. [...] Read more.
Cases of missing children not being found are rare, but they continue to occur. If the child is not found immediately, the parents may not be able to identify the child’s appearance because they have not seen their child for a long time. Therefore, our purpose is to predict children’s faces when they grow up and help parents search for missing children. DNA paternity testing is the most accurate way to detect whether two people have a blood relation. However, DNA paternity testing for every unidentified child would be costly. Therefore, we propose the development of the Face Prediction System for Missing Children in a Smart City Safety Network. It can predict the faces of missing children at their current age, and parents can quickly confirm the possibility of blood relations with any unidentified child. The advantage is that it can eliminate incorrect matches and narrow down the search at a low cost. Our system combines StyleGAN2 and FaceNet methods to achieve prediction. StyleGAN2 is used to style mix two face images. FaceNet is used to compare the similarity of two face images. Experiments show that the similarity between predicted and expected results is more than 75%. This means that the system can well predict children’s faces when they grow up. Our system has more natural and higher similarity comparison results than Conditional Adversarial Autoencoder (CAAE), High Resolution Face Age Editing (HRFAE) and Identity-Preserved Conditional Generative Adversarial Networks (IPCGAN). Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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17 pages, 1724 KiB  
Article
In-Memory Computing Architecture for a Convolutional Neural Network Based on Spin Orbit Torque MRAM
by Jun-Ying Huang, Jing-Lin Syu, Yao-Tung Tsou, Sy-Yen Kuo and Ching-Ray Chang
Electronics 2022, 11(8), 1245; https://doi.org/10.3390/electronics11081245 - 14 Apr 2022
Cited by 2 | Viewed by 4169
Abstract
Recently, numerous studies have investigated computing in-memory (CIM) architectures for neural networks to overcome memory bottlenecks. Because of its low delay, high energy efficiency, and low volatility, spin-orbit torque magnetic random access memory (SOT-MRAM) has received substantial attention. However, previous studies used calculation [...] Read more.
Recently, numerous studies have investigated computing in-memory (CIM) architectures for neural networks to overcome memory bottlenecks. Because of its low delay, high energy efficiency, and low volatility, spin-orbit torque magnetic random access memory (SOT-MRAM) has received substantial attention. However, previous studies used calculation circuits to support complex calculations, leading to substantial energy consumption. Therefore, our research proposes a new CIM architecture with small peripheral circuits; this architecture achieved higher performance relative to other CIM architectures when processing convolution neural networks (CNNs). We included a distributed arithmetic (DA) algorithm to improve the efficiency of the CIM calculation method by reducing the excessive read/write times and execution steps of CIM-based CNN calculation circuits. Furthermore, our method also uses SOT-MRAM to increase the calculation speed and reduce power consumption. Compared with CIM-based CNN arithmetic circuits in previous studies, our method can achieve shorter clock periods and reduce read times by up to 43.3% without the need for additional circuits. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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13 pages, 1630 KiB  
Article
A Vendor-Managed Inventory Mechanism Based on SCADA of Internet of Things Framework
by Chang-Yi Kao and Hao-En Chueh
Electronics 2022, 11(6), 881; https://doi.org/10.3390/electronics11060881 - 10 Mar 2022
Cited by 4 | Viewed by 3038
Abstract
In recent years, with the rise of the Internet of Things (IoT) and artificial intelligence (AI), intelligent applications in various fields, such as intelligent manufacturing, have been prioritized. The most important issue in intelligent manufacturing is to maintain a high utilization rate of [...] Read more.
In recent years, with the rise of the Internet of Things (IoT) and artificial intelligence (AI), intelligent applications in various fields, such as intelligent manufacturing, have been prioritized. The most important issue in intelligent manufacturing is to maintain a high utilization rate of production. On the one hand, for maintaining high utilization, the production line must have enough materials at any time; on the other hand, too many materials in stock would greatly increase the operating cost of the factory. Therefore, maintaining sufficient inventory while avoiding excessive inventory is an important key issue in intelligent manufacturing. After the factory receives the order, it would issue the manufacturing order to the production line for manufacturing. The capacities of different production lines are different. If the Supervisory Control And Data Acquisition (SCADA) system based on the IoT framework can be used to monitor the capacity of each production line, in addition to estimating the capacity, the usage of key materials can also be accurately estimated through AI; when the quantity of key materials is below the safety stock, the manufacturer can actively notify the supplier and request for replenishment. This is a Customer-to-Business (C2B) safety stock management model (i.e., the vendor-managed inventory, VMI), which combines AI and IoT. In particular, in the case of consumer electronics, because their life cycles are short and they are vulnerable to market fluctuations, the manufacturer must adjust the production capacity. This study will propose to construct a SCADA system based on the IoT, including the capacity of the production line, materials inventory, and downstream order requirements, and use the Artificial Neural Network (ANN) to accurately predict inventory requirements. In this study, through the factory, a SCADA system based on AI and IoT will be constructed to monitor the factory’s manufacturing capacity and predict the product sales of downstream manufacturers, for the purpose of facilitating the analysis and decision-making of safety stock. In addition to effectively reducing the inventory level, in essence, the purpose of this study is to enhance the competitiveness of the overall production and sales ecosystem, and to achieve the goal of digital transformation of manufacturing with AI and IoT. Full article
(This article belongs to the Special Issue Advances of Future IoE Wireless Network Technology)
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