**About the Editors**

#### **Gwo-Jiun Horng**

Gwo-Jiun Horng received his Ph.D. in Information Engineering from National Chenggong University, Taiwan, in 2013. He is currently a Professor in the Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology. Prior to this, he served as an Assistant Professor in Cheng Shiu University, National Kaohsiung Marine University and Southern Taiwan University of Science and Technology. His research interests include AIoT, IoE, mobile service and computing, and wireless networks. In recent years, he has actively participated in academic activities, published over 100 papers in conferences and SCI, SCIE, and SSCI journals such as *Mobile Networks and Applications*, *Journal of Supercomputing*, *Computers and Electrical Engineering*, *ACM/Springer Mobile Networks and Applications*, and *IEEE Transactions on Intelligent Transportation Systems*. He also serves reviewers and editors in related journals. He has received awards including the Smart Transportation Paper Award and National Innovation Award and was sponsored by the Ministry of Science and Technology, Academia Sinica.

#### **S.T. Aripriharta**

Aripriharta received his Ph.D. from the National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, in 2017. Currently, he is an Associate Professor at the Faculty of Engineering, Department of Electrical Engineering, State University of Malang, Indonesia. His research interests focus on low-power electronics converters for biomedical IoT/wearable devices such as self-powered converters, energy harvesting, etc. He has published more than 100 publications, 10 patents, 20 copyrights and 5 books.

He also is a member of professional academic organizations such as IEEE, IAENG, IMRCS, and Fortei Reg 7.

#### **Yao-Tung Tsou**

Yao-Tung Tsou received a Ph.D. degree from the Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan. He joined the Research Center for Information Technology Innovation (CITI), Academia Sinica, in 2013, as a Research Assistant. He is currently an Associate Professor at the Department of Communications Engineering, Feng Chia University, Taichung, Taiwan. He is also a Consultant at Lin Dan Technology Inc. and Swiss Innovation Valley. His research interests include Emergent Memory (STT-MRAM) Security System Application, Mobile Network Computing and Security, Cyber Security, and Embedded System Development and Application. He received the Magnetism Research Prize from the Taiwan Association for Magnetic Technology and the Best Paper Award in Taiwan Internet Seminar and AII2022 Taiwan Innovation and Invention Application Seminar. Dr. Tsou has published papers in conferences and SCIE journals such as *IEEE Transactions on Services Computing*, *Electronics*, *IEEE Access and Computer Communications*.

#### **Chia-Wei Tsai**

Dr. Chia-Wei Tsai is an Assistant Professor at the Department of Computer Science and Information Engineering at the National Taichung University of Science and Technology. In 2011, he received his Doctoral degree in Computer Science and Information Engineering from National Cheng Kung University, Tainan, Taiwan. His primary research interests are quantum cryptography, quantum information, quantum network, and anomaly detection. He has published 68 SCI journal papers in these fields. Furthermore, in terms of practical experiments, he also possesses 6 years of experience as an engineer in machine learning applications.

### *Editorial* **Advances of Future IoE Wireless Network Technology**

**Gwo-Jiun Horng**

Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Yongkang District, Tainan 710301, Taiwan; grojium@stust.edu.tw

#### **1. Introduction**

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. Wireless networks are at the forefront of IoE and they continue to advance, meeting the growing demands of the digital age. In this article, we summarize ten recent research articles that highlight the advances in IoE wireless network technology.

This collection of articles published in *Electronics* encompasses a wide range of topics related to emerging technologies. The articles include research on vendor-managed inventory mechanism based on the SCADA of the Internet of Things, in-memory computing architecture for a convolutional neural network, face prediction system for missing children in a smart city safety network, anomaly electricity usage behavior in residence using autoencoder, cloud-edge-smart IoT architecture for speeding up the deployment of neural network models, generative adversarial network and diverse feature extraction methods to enhance the classification accuracy of tool-wear status, classifying conditions of speckles and wrinkles on the human face using a deep learning approach, repetition with learning approaches in massive machine-type communications, GDPR personal privacy security mechanism for smart home systems, and the development of an autonomous vehicle training and verification system. These articles demonstrate the rapid advances being made in the field of electronics and highlight the potential for these technologies to impact on our daily lives.

In recent years, advances in technology have led to many exciting developments in the field of electronics. From smart city safety networks to autonomous vehicle training systems, researchers are constantly working on innovative solutions to complex problems. In this summary, we will discuss some of the latest research articles published in the *Electronics* journal.

#### **2. Brief Description of the Published Articles**

First, Kao and Chueh [1] proposed a vendor-managed inventory mechanism based on the SCADA of the Internet of Things Framework. This mechanism allows for vendors to monitor and manage the inventory levels of their customers in real time, enabling an efficient supply chain management.

Second, Huang et al. [2] presented an in-memory computing architecture for a convolutional neural network based on the spin orbit torque MRAM. This architecture improves the performance of the neural network while reducing energy consumption.

A study by Wang et al. [3] focus on the development of a face prediction system for missing children in a smart city safety network. The system uses deep learning techniques to predict the appearance of a missing child's face based on their current age and gender.

Another article by Tsai et al. [4] describe a method for detecting anomaly electricity usage behavior in residences using an autoencoder. By analyzing electricity usage patterns, the system can identify abnormal behavior that may indicate a potential safety or security issue.

**Citation:** Horng, G.-J. Advances of Future IoE Wireless Network Technology. *Electronics* **2023**, *12*, 2164. https://doi.org/10.3390/electronics 12102164

Received: 6 May 2023 Accepted: 8 May 2023 Published: 9 May 2023

**Copyright:** © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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Hsu et al. [5] propose a cloud-edge-smart IoT architecture for speeding up the deployment of neural network models using transfer learning techniques. The proposed architecture allows for the efficient deployment of machine learning models on edge devices, reducing the need for expensive cloud infrastructure.

Chen et al. [6] present an application of generative adversarial networks (GANs) and diverse feature extraction methods to enhance the classification accuracy of tool-wear status. The study demonstrates the effectiveness of GANs in generating high-quality samples for training machine learning models.

An article by Chang and Tsai [7] focuses on the classification of conditions of speckles and wrinkles on the human face, using a deep learning approach. The proposed method achieves a high accuracy in identifying these conditions, which can be useful in cosmetic and medical applications.

Chen et al. [8] discuss the use of repetition with learning approaches in massive machine-type communications. The study proposes a framework for training machine learning models in resource-limited environments, such as those found in Internet of Things (IoT) devices.

Jhuang et al. [9] present a GDPR personal privacy security mechanism for smart home systems. The proposed mechanism provides enhanced security and privacy protections for personal data in smart home environments.

Finally, Wu et al. [10] describe the development of an autonomous vehicle training and verification system for teaching experiments. The system allows for the safe and efficient training of autonomous vehicles in real-world scenarios.

#### **3. Conclusions**

In conclusion, these articles demonstrate the wide range of applications for electronics in modern society. From machine learning and deep learning techniques, to smart city safety networks and autonomous vehicles, researchers are constantly pushing the boundaries of electronics technology possibilities.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


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