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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 3162

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 (4 papers)

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Research

17 pages, 21513 KiB  
Article
Differential Privacy-Based Location Privacy Protection for Edge Computing Networks
by Guowei Zhang, Jiayuan Du, Xiaowei Yuan and Kewei Zhang
Electronics 2024, 13(17), 3510; https://doi.org/10.3390/electronics13173510 - 4 Sep 2024
Viewed by 381
Abstract
Mobile Edge Computing (MEC) has been widely applied in various Internet of Things (IoT) scenarios due to its advantages of low latency and low energy consumption. However, the offloading of tasks generated by terminal devices to edge servers inevitably raises privacy leakage concerns. [...] Read more.
Mobile Edge Computing (MEC) has been widely applied in various Internet of Things (IoT) scenarios due to its advantages of low latency and low energy consumption. However, the offloading of tasks generated by terminal devices to edge servers inevitably raises privacy leakage concerns. Given the limited resources in MEC networks, this paper proposes a task scheduling strategy, named DQN-DP, to minimize location privacy leakage under the constraint of offloading costs. The strategy is based on a differential privacy location obfuscation probability density function. Theoretical analysis demonstrates that the probability density function employed in this study is valid and satisfies ϵ-differential privacy in terms of security. Numerical results indicate that, compared to existing baseline approaches, the proposed DQN-DP algorithm effectively balances privacy leakage and offloading cost. Specifically, DQN-DP reduces privacy leakage by approximately 20% relative to baseline approaches. Full article
(This article belongs to the Special Issue AI-Aided Sustainable IoT System: Theories, Techniques, and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Edge offloading model with location protection.</p> Full article ">Figure 2
<p>The framework of the DQN-based optimization process.</p> Full article ">Figure 3
<p>Relationship between task offloading cost, offloading decisions, and distance between users and MEC servers.</p> Full article ">Figure 4
<p>The relationship between task execution ratio and cost at a distance of 150.</p> Full article ">Figure 5
<p>The relationship between the confusion interval and the distance between users and MEC servers.</p> Full article ">Figure 6
<p>Comparison of task offloading costs using different algorithms.</p> Full article ">Figure 7
<p>Comparison of task offloading costs under different path loss constants.</p> Full article ">Figure 8
<p>Comparison of PL levels using different algorithms.</p> Full article ">Figure 9
<p>Impact of different task loss ratios on task computation costs.</p> Full article ">
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 662
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Engine sound signal acquisition.</p> Full article ">Figure 2
<p>Engine sound signal. (<b>a</b>) normal; (<b>b</b>) one-cylinder misfire; (<b>c</b>) two-cylinder misfire.</p> Full article ">Figure 3
<p>Wavelet transformation time–frequency image. (<b>a</b>) normal; (<b>b</b>) one-cylinder misfire; (<b>c</b>) two-cylinder misfire.</p> Full article ">Figure 4
<p>Structure of BiFormer.</p> Full article ">Figure 5
<p>The overview of CBAM (Note: Pictures are from the Ref. [<a href="#B34-electronics-13-02688" class="html-bibr">34</a>]). (<b>A</b>) the structure of channel attention; (<b>B</b>) the structure of spatial attention; (<b>C</b>) the structure of CBAM attention.</p> Full article ">Figure 5 Cont.
<p>The overview of CBAM (Note: Pictures are from the Ref. [<a href="#B34-electronics-13-02688" class="html-bibr">34</a>]). (<b>A</b>) the structure of channel attention; (<b>B</b>) the structure of spatial attention; (<b>C</b>) the structure of CBAM attention.</p> Full article ">Figure 6
<p>Structural comparison of YOLOv8 and YOLOv8-CBBF. (<b>A</b>) Structure of YOLOv8; (<b>B</b>) Structure of YOLOv8-CBBF.</p> Full article ">Figure 7
<p>YOLOv8-CBBF training process. (<b>A</b>) Train Loss; (<b>B</b>) Validation Loss; (<b>C</b>) Train accuracy.</p> Full article ">
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
Cited by 1 | Viewed by 725
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Task offloading framework with location protection in edge computing.</p> Full article ">Figure 2
<p>Relationship between distance and offloading ratio/distance and cost.</p> Full article ">Figure 3
<p>Relationship between true offloading ratio and ratio range.</p> Full article ">Figure 4
<p>Relationship between privacy leakage impact factor and confusion interval.</p> Full article ">Figure 5
<p>The relationship between privacy leakage impact factor, privacy leakage and cost.</p> Full article ">Figure 6
<p>Relationship between distance and privacy leakage.</p> Full article ">Figure 7
<p>Relationship between distance and average cost.</p> Full article ">
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 736
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Abstract structure of slant type solar greenhouse.</p> Full article ">Figure 2
<p>Schematic structure of sectional control system for a large greenhouse.</p> Full article ">Figure 3
<p>Structure diagram of SNPID feedforward compensation decoupling system.</p> Full article ">Figure 4
<p>System structure diagram after decoupling.</p> Full article ">Figure 5
<p>Undecoupled temperature and humidity setpoints vs. simulated test curves.</p> Full article ">Figure 6
<p>Temperature and humidity curves of a single neuron PID decoupling control dynamic experiment.</p> Full article ">Figure 7
<p>Temperature and humidity curves of single neuron PID decoupling control perturbation test.</p> Full article ">Figure 8
<p>Schematic diagram of PID controller.</p> Full article ">Figure 9
<p>Block diagram of the fuzzy PID control system.</p> Full article ">Figure 10
<p>Greenhouse simulation model.</p> Full article ">Figure 11
<p>SNPID controller structure.</p> Full article ">Figure 12
<p>Workflow of SNPID controller.</p> Full article ">Figure 13
<p>SNPID Simulation Module.</p> Full article ">Figure 14
<p>Temperature and humidity control algorithm workflow.</p> Full article ">Figure 15
<p>SNPID self-control mode workflow.</p> Full article ">Figure 16
<p>Bootloader workflow design.</p> Full article ">Figure 17
<p>Keil MDK Engineering Code Structure.</p> Full article ">Figure 18
<p>Keil MDK CORE Component Enabling Method.</p> Full article ">Figure 19
<p>Keil MDK header file inclusion method.</p> Full article ">Figure 20
<p>RT Thread Welcome Interface.</p> Full article ">Figure 21
<p>Hardware Architecture of Control System.</p> Full article ">Figure 22
<p>Main control board design scheme.</p> Full article ">Figure 23
<p>Control System Program Workflow.</p> Full article ">Figure 24
<p>Initialization process of various functional modules on the main control board.</p> Full article ">Figure 25
<p>Workflow of Temperature and Humidity Automatic Control Module.</p> Full article ">Figure 26
<p>Comparison test simulation structure.</p> Full article ">Figure 27
<p>Simulation results of the comparison test of the step response.</p> Full article ">Figure 28
<p>Greenhouse Simulation Model Structure.</p> Full article ">Figure 29
<p>Controlled greenhouse simulation model.</p> Full article ">Figure 30
<p>Solar radiation simulation curves.</p> Full article ">Figure 31
<p>Variation curves of controlled temperature and vent opening of greenhouse. (<b>a</b>) Controlled Temperature Curves of Greenhouse Under SNPID And cPID Control and (<b>b</b>) SNPID and cPID Output Vent Opening Change Curve.</p> Full article ">Figure 31 Cont.
<p>Variation curves of controlled temperature and vent opening of greenhouse. (<b>a</b>) Controlled Temperature Curves of Greenhouse Under SNPID And cPID Control and (<b>b</b>) SNPID and cPID Output Vent Opening Change Curve.</p> Full article ">Figure 32
<p>Details of temperature profiles before and after t = 3.33 h.</p> Full article ">Figure 33
<p>Schematic diagram of the working state of the main control board. (<b>a</b>) the working status of the system, (<b>b</b>) current temperature, (<b>c</b>) relative humidity, (<b>d</b>) vent opening and (<b>e</b>) motor current.</p> Full article ">Figure 34
<p>Internal and external views of the experimental Chinese solar greenhouse.</p> Full article ">Figure 35
<p>Plot of variation in greenhouse temperature and air opening size. (<b>a</b>): Internal Air Temperature and (<b>b</b>) Tuyere Size.</p> Full article ">Figure 36
<p>Greenhouse humidity variation graph.</p> Full article ">
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