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Recent Developments in Wireless Network Technology
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Recent Developments in Wireless Network Technology

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 11821

Special Issue Editors

Prof. Dr. Yung-Fa Huang

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: wireless communications; wireless sensor networks; fuzzy systems; 5G mobile networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Patrick Hung

E-Mail Website
Guest Editor
Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
Interests: social robots; human–robot interactions; smart toy; robotic computing; services computing; security and privacy
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of Aizu, Fukushima 965-8580, Japan
Interests: edge/cloud computing; machine learning systems; mobile computing; distributed systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

This Special Issue is organized to discuss the state-of-the-art and emerging research, applications, and problems in the recent developments of Wireless Sensor Networks (WSNs) and Internet of Things (IoTs) fields. Currently, wireless network technology is evolving very rapidly. The growing capacity to transmit information and inter-network traffic is forcing manufacturers to produce more powerful devices that use new methods of data transmission and classification. This connected smart device is used in Vehicular Ad hoc NETworks (VANET) for autonomous driving to intelligently control traffic, smart city power and water, and home automation in smart homes. The addition of Massive Machine-Type Communication (mMTC) as a category of Fifth Generation (5G) mobile communication has increased the popularity of IoTs. With the proliferation of IoTs and mMTC services, every wireless device will be connected to one or more wireless-access networks served by multiple APs or base stations (BSs). Additionally, the emerging technologies and issues in the areas of ubiquitous services, wireless and multimedia applications and networking issues for 5G/Beyond 5G are all within the scope of this Special Issue. This Special Issue will focus on the recent prospective technologies, models, systems and applications in WSNs, IoTs, 5G and advanced networking areas. The aim is to collect the most recent developments in Artificial Intelligence of Things (AIoT) research for Wireless Sensor Networking.

Potential topics include, but are not limited to:

  • Resource allocation and scheduling in wireless sensor networks;
  • Optimization algorithms for wireless sensor networks;
  • Network routing in wireless sensor networks;
  • Resource allocation for an IoT/edge interplay;
  • Energy efficiency of wireless sensors networks and IoT;
  • Scalability issues in wireless sensor networks/the IoT;
  • Security issues in wireless sensor networks;
  • Data analytics, traffic analysis, and classification in the IoT and wireless sensor networks;
  • QoS management in wireless sensor networks;
  • Energy sustained development in the IoT;
  • Performance analysis and modeling in wireless sensor networks;
  • Applications of wireless sensor networks;
  • Performance evaluation in autonomous driving with IoT technologies;
  • Connectivity of smart devices in VANET;
  • Implementation of platform based on 5G and AIoT;
  • Architecture design of IoT systems;
  • Applications of medical health of IoT technologies;
  • Biomedical monitoring of IoT technologies.

Prof. Dr. Yung-Fa Huang
Prof. Dr. Patrick Hung
Dr. Peng Li
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

  • wireless sensor networks (WSNs)
  • internet of things (IoTs)
  • fifth generation (5G) mobile communication
  • artificial intelligence of things (AIoT)
  • massive machine type communication (mMTC)
  • vehicular ad hoc networks (VANET)

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Published Papers (4 papers)

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Research

23 pages, 9304 KiB  
Article
Development of a Deep Learning-Based Epiglottis Obstruction Ratio Calculation System
by Hsing-Hao Su and Chuan-Pin Lu
Sensors 2023, 23(18), 7669; https://doi.org/10.3390/s23187669 - 5 Sep 2023
Cited by 2 | Viewed by 1469
Abstract
Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to [...] Read more.
Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to correspond to changes in respiratory airflow. Current artificial intelligence image technologies can effectively address this issue. To enhance the accuracy of epiglottis obstruction assessment and replace obstruction degrees with obstruction ratios, this study developed a computer vision system with a deep learning-based method for calculating epiglottis obstruction ratios. The system employs a convolutional neural network, the YOLOv4 model, for epiglottis cartilage localization, a color quantization method to transform pixels into regions, and a region puzzle algorithm to calculate the range of a patient’s epiglottis airway. This information is then utilized to compute the obstruction ratio of the patient’s epiglottis site. Additionally, this system integrates web-based and PC-based programming technologies to realize its functionalities. Through experimental validation, this system was found to autonomously calculate obstruction ratios with a precision of 0.1% (ranging from 0% to 100%). It presents epiglottis obstruction levels as continuous data, providing crucial diagnostic insight for surgeons to assess the severity of epiglottis obstruction in patients. Full article
(This article belongs to the Special Issue Recent Developments in Wireless Network Technology)
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18 pages, 2757 KiB  
Article
A CNN Sound Classification Mechanism Using Data Augmentation
by Hung-Chi Chu, Young-Lin Zhang and Hao-Chu Chiang
Sensors 2023, 23(15), 6972; https://doi.org/10.3390/s23156972 - 5 Aug 2023
Cited by 8 | Viewed by 4838
Abstract
Sound classification has been widely used in many fields. Unlike traditional signal-processing methods, using deep learning technology for sound classification is one of the most feasible and effective methods. However, limited by the quality of the training dataset, such as cost and resource [...] Read more.
Sound classification has been widely used in many fields. Unlike traditional signal-processing methods, using deep learning technology for sound classification is one of the most feasible and effective methods. However, limited by the quality of the training dataset, such as cost and resource constraints, data imbalance, and data annotation issues, the classification performance is affected. Therefore, we propose a sound classification mechanism based on convolutional neural networks and use the sound feature extraction method of Mel-Frequency Cepstral Coefficients (MFCCs) to convert sound signals into spectrograms. Spectrograms are suitable as input for CNN models. To provide the function of data augmentation, we can increase the number of spectrograms by setting the number of triangular bandpass filters. The experimental results show that there are 50 semantic categories in the ESC-50 dataset, the types are complex, and the amount of data is insufficient, resulting in a classification accuracy of only 63%. When using the proposed data augmentation method (K = 5), the accuracy is effectively increased to 97%. Furthermore, in the UrbanSound8K dataset, the amount of data is sufficient, so the classification accuracy can reach 90%, and the classification accuracy can be slightly increased to 92% via data augmentation. However, when only 50% of the training dataset is used, along with data augmentation, the establishment of the training model can be accelerated, and the classification accuracy can reach 91%. Full article
(This article belongs to the Special Issue Recent Developments in Wireless Network Technology)
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18 pages, 946 KiB  
Article
Efficient Authentication Scheme for 5G-Enabled Vehicular Networks Using Fog Computing
by Zeyad Ghaleb Al-Mekhlafi, Mahmood A. Al-Shareeda, Selvakumar Manickam, Badiea Abdulkarem Mohammed, Abdulrahman Alreshidi, Meshari Alazmi, Jalawi Sulaiman Alshudukhi, Mohammad Alsaffar and Taha H. Rassem
Sensors 2023, 23(7), 3543; https://doi.org/10.3390/s23073543 - 28 Mar 2023
Cited by 29 | Viewed by 2196
Abstract
Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems [...] Read more.
Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are 0.0031, 2.0185 to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase. Full article
(This article belongs to the Special Issue Recent Developments in Wireless Network Technology)
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16 pages, 3093 KiB  
Article
Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch
by Tan-Hsu Tan, Jyun-Yu Shih, Shing-Hong Liu, Mohammad Alkhaleefah, Yang-Lang Chang and Munkhjargal Gochoo
Sensors 2023, 23(6), 3354; https://doi.org/10.3390/s23063354 - 22 Mar 2023
Cited by 5 | Viewed by 2495
Abstract
Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition [...] Read more.
Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people’s activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F1-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes. Full article
(This article belongs to the Special Issue Recent Developments in Wireless Network Technology)
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