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Electronics
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AI-Aided Sustainable IoT System: Theories, Techniques, and Applications
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AI-Aided Sustainable IoT System: Theories, Techniques, and Applications

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 2338

Special Issue Editors

Dr. Yuchao Chang

E-Mail Website
Guest Editor
1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. CIX Technology (Shanghai) Co., Ltd., Shanghai 201203, China
Interests: artificial intelligence and machine learning for wireless, green Internet of Things system; modeling and algorithm design
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Zhong

E-Mail Website
Guest Editor
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: heterogeneous and femtocell-overlaid cellular networks; wireless ad hoc networks; stochastic geometry; point process theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wen Chen

E-Mail Website
Guest Editor
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: multiple access; coded cooperation; green heterogeneous networks

Special Issue Information

Dear Colleagues,

The global mobile data traffic market is projected to grow from 84 million terabytes per month in 2022 to 603.5 million by 2030. The sustainable Internet of Things (IoT) system has emerged as a proactive response to the mounting energy consumption concerns arising from the rapid proliferation of IoT devices and technologies. In propelling the development of the sustainable IoT system, Artificial Intelligence (AI)-based techniques play important roles. State-of-the-art AI-based technologies in signal processing, wireless communications, embedded systems, and smart computing could be helpful in adding intelligence to the sustainable IoT system. This Special Issue is dedicated to exploring the latest developments of AI-based technologies in the sustainable IoT system with a specific focus on showcasing innovative solutions that augment their capabilities and applications.

The topics of interest for this Special Issue include but are not limited to:

  • Intelligent information theory;
  • Intelligent signal processing;
  • Wireless artificial intelligence;
  • Green intelligent communication and computing;
  • Deep neural networks;
  • Intelligent image processing;
  • Statistical signal modeling;
  • Integrated circuits simulations;
  • Big data analysis;
  • Machine learning applications;
  • Artificial intelligence applications;
  • Internet of Things.

Dr. Yuchao Chang
Dr. Yi Zhong
Prof. Dr. Wen 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
  • IoT
  • big data
  • signal processing

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Published Papers (3 papers)

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Research

17 pages, 2713 KiB  
Article
Gasoline Engine Misfire Fault Diagnosis Method Based on Improved YOLOv8
by Zhichen Li, Zhao Qin, Weiping Luo and Xiujun Ling
Electronics 2024, 13(14), 2688; https://doi.org/10.3390/electronics13142688 - 9 Jul 2024
Viewed by 536
Abstract
In order to realize the online diagnosis and prediction of gasoline engine fire faults, this paper proposes an improved misfire fault detection algorithm model based on YOLOv8 for sound signals of gasoline engines. The improvement involves substituting a C2f module in the YOLOv8 [...] Read more.
In order to realize the online diagnosis and prediction of gasoline engine fire faults, this paper proposes an improved misfire fault detection algorithm model based on YOLOv8 for sound signals of gasoline engines. The improvement involves substituting a C2f module in the YOLOv8 backbone network by a BiFormer attention module and another C2f module substituted by a CBAM module that combines channel and spatial attention mechanisms which enhance the neural network’s capacity to extract the complex features. The normal and misfire sound signals of a gasoline engine are processed by wavelet transformation and converted to time–frequency images for the training, verification, and testing of convolutional neural network. The experimental results show that the precision of the improved YOLOv8 algorithm model is 99.71% for gasoline engine fire fault tests, which is 2 percentage points higher than for the YOLOv8 network model. The diagnosis time of each sound is less than 100 ms, making it suitable for developing IoT devices for gasoline engine misfire fault diagnosis and driverless vehicles. Full article
(This article belongs to the Special Issue AI-Aided Sustainable IoT System: Theories, Techniques, and Applications)
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21 pages, 2996 KiB  
Article
Location Privacy Protection in Edge Computing: Co-Design of Differential Privacy and Offloading Mode
by Guowei Zhang, Shengjian Zhang, Zhiyi Man, Chenlin Cui and Wenli Hu
Electronics 2024, 13(13), 2668; https://doi.org/10.3390/electronics13132668 - 7 Jul 2024
Viewed by 588
Abstract
Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can [...] Read more.
Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can introduce a critical vulnerability, as adversaries may infer a user’s location through an analysis of their offloading mode, thereby threatening the user’s location privacy. To counteract this vulnerability, this study introduces differential privacy as a protective mechanism to obscure the user’s offloading mode, thereby safeguarding their location information. This research specifically addresses the issue of location privacy leakage stemming from the correlation between a user’s location and their task offloading ratio. The proposed strategy is based on differential privacy. It aims to increase the efficiency of offloading services and the benefits of task offloading. At the same time, it ensures privacy protection. An innovative optimization technique for task offloading that maintains location privacy is presented. Utilizing this technique, users can make informed offloading decisions, dynamically adjusting the level of obfuscation in response to the state of the wireless channel and their privacy requirements. This study substantiates the feasibility and effectiveness of the proposed mechanism through rigorous theoretical analysis and extensive empirical testing. The numerical results demonstrate that the proposed strategy can achieve a balance between offloading privacy and processing overhead. Full article
(This article belongs to the Special Issue AI-Aided Sustainable IoT System: Theories, Techniques, and Applications)
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46 pages, 12068 KiB  
Article
Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm
by Song Huang, Huiyu Xiang, Chongjie Leng, Tongyang Dai and Guanghui He
Electronics 2024, 13(11), 2083; https://doi.org/10.3390/electronics13112083 - 27 May 2024
Viewed by 607
Abstract
In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around [...] Read more.
In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance. Full article
(This article belongs to the Special Issue AI-Aided Sustainable IoT System: Theories, Techniques, and Applications)
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