Advances of Future IoE Wireless Network Technology

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.

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.