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Electronics
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Wireless Sensor Networks Applications in Internet of Things
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Wireless Sensor Networks Applications in Internet of Things

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 8467

Special Issue Editors

Prof. Dr. Wen-Tsai Sung

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Guest Editor
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung City 411030, Taiwan
Interests: AIoT; wireless sensor network; SOC and microcontroller design; RFID; complex curve and curved surface design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kuo-Torng Lan

E-Mail Website
Guest Editor
Department of Information Technology, Takming University of Science and Technology, Taipei City, Taiwan
Interests: wireless sensor network; research on application of XML/SOAP cross-platform network information exchange technology; AIoT; blockchain; fuzzy logic
Prof. Dr. Pei-Chi Chen

E-Mail Website
Guest Editor
Department of industrial education, National Taiwan Normal University, Taipei City, Taiwan
Interests: wireless sensor network; AIoT; blockchain; big data analysis; machine learning

Special Issue Information

Dear Colleagues,

Recent research has focused on the need to integrate artificial intelligence, machine learning, blockchain, network security, data analysis, big data, the Internet of Things, and other fast-developing fields to predict methods and technologies to provide real-time solutions to certain problems. Smart systems are not only able to increase productivity and efficiency, but they can also deal with unpredictable and imprecise issues such as variability, downtime, and human factors. Blockchain provides a decentralized, distributed, reliable technique for processing and authenticating transactions. Soft computing methods use a combination of heuristics, approximate models, and random and non-deterministic algorithm behaviors to solve the inaccuracy and inaccuracy that often exist in complex manufacturing systems through certainty and partial truth.

This Special Issue of Electronics will focus on the application of soft computing methods in intelligent automation systems, as well as attempts to enhance protection and privacy with complete communication and delivery as blockchain and AI technologies are allowed beyond 5G (B5G). The research emphasis is on incorporating and unifying technologies to produce new and smart networking services and applications as both blockchain and AI advance further.

Prof. Dr. Wen-Tsai Sung
Prof. Dr. Kuo-Torng Lan
Prof. Dr. Pei-Chi Chen
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

  • artificial intelligence
  • big data
  • blockchain
  • cloud computing
  • wireless sensor network
  • deep learning
  • fuzzy logics
  • internet of things
  • machine learning
  • 5G

Published Papers (2 papers)

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Research

20 pages, 8395 KiB  
Article
Controller Design of Tracking WMR System Based on Deep Reinforcement Learning
by Chin-Tan Lee and Wen-Tsai Sung
Electronics 2022, 11(6), 928; https://doi.org/10.3390/electronics11060928 - 16 Mar 2022
Cited by 6 | Viewed by 2411
Abstract
Traditional PID controllers are widely used in industrial applications due to their simple computational architecture. However, the gain parameters of this simple computing architecture are fixed, and in response to environmental changes, the PID parameters must be continuously adjusted until the system is [...] Read more.
Traditional PID controllers are widely used in industrial applications due to their simple computational architecture. However, the gain parameters of this simple computing architecture are fixed, and in response to environmental changes, the PID parameters must be continuously adjusted until the system is optimized. This research proposes to use the most important deep reinforcement learning (DRL) algorithm in deep learning as the basis and to modulate the gain parameters of the PID controller with fuzzy control. The research has the ability and advantages of reinforcement learning and fuzzy control and constructs a tracking unmanned wheel system. The mobile robotic platform uses a normalization system during computation to reduce the effects of reading errors caused by the wheeled mobile robot (WMR) of environment and sensor processes. The DRL-Fuzzy-PID controller architecture proposed in this paper utilizes degree operation to avoid the data error of negative input in the absolute value judgment, thereby reducing the amount of calculation. In addition to improving the accuracy of fuzzy control, it also uses reinforcement learning to quickly respond and minimize steady-state error to achieve accurate calculation performance. The experimental results of this study show that in complex trajectory sites, the tracking stability of the system using DRL-fuzzy PID is improved by 15.2% compared with conventional PID control, the maximum overshoot is reduced by 35.6%, and the tracking time ratio is shortened by 6.78%. If reinforcement learning is added, the convergence time of the WMR system will be about 0.5 s, and the accuracy rate will reach 95%. This study combines the computation of deep reinforcement learning to enhance the experimentally superior performance of the WMR system. In the future, intelligent unmanned vehicles with automatic tracking functions can be developed, and the combination of IoT and cloud computing can enhance the innovation of this research. Full article
(This article belongs to the Special Issue Wireless Sensor Networks Applications in Internet of Things)
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29 pages, 13532 KiB  
Article
Employing a Wireless Sensing Network for AIoT Based on a 5G Approach
by Sung-Jung Hsiao
Electronics 2022, 11(5), 827; https://doi.org/10.3390/electronics11050827 - 7 Mar 2022
Cited by 3 | Viewed by 5168
Abstract
In this paper, wireless sensing networks are considered in the context of the 5G communication architecture in order to ensure the transmission efficiency of the sensed data transmitted by the system. The various types of environmental sensors are very diverse. Wireless sensing networks [...] Read more.
In this paper, wireless sensing networks are considered in the context of the 5G communication architecture in order to ensure the transmission efficiency of the sensed data transmitted by the system. The various types of environmental sensors are very diverse. Wireless sensing networks may include many different types of sensing devices or data related to images or pattern transmission, among others, and are often limited by the problem of insufficient network bandwidth. By using 5G communications for data transmission, the problem of limited network bandwidth can be solved. In addition to the use of 5G transmission, when the NB-IoT method is used within the 5G network environment, it is much more efficient than that under the original LTE network conditions. Therefore, using 5G to transmit data provides the advantages of high transmission efficiency and data integrity. In this paper, in addition to analyzing the development of 5G technology, the proposed approach uses MATLAB software to simulate the generation of 5G signals under various parameter settings representing a range of conditions. Finally, our approach discusses the use of a 5G communication module, including driver installation and data transmission testing. At present, the 5G network architecture is still under construction worldwide. According to the transmission speed test data obtained in this study, the transmission efficiency of 5G is better than that of precursor generations. Full article
(This article belongs to the Special Issue Wireless Sensor Networks Applications in Internet of Things)
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