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学者姓名：林瑞全
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Abstract ：
Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively. © 2024 The Authors
Keyword ：
Aflatoxin Aflatoxin Machine learning Machine learning Peanut Peanut Quality identification Quality identification Terahertz imaging technology Terahertz imaging technology
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| GB/T 7714 | Jiang, W. , Wang, J. , Lin, R. et al. Machine learning-based non-destructive terahertz detection of seed quality in peanut [J]. | Food Chemistry: X , 2024 , 23 . |
| MLA | Jiang, W. et al. "Machine learning-based non-destructive terahertz detection of seed quality in peanut" . | Food Chemistry: X 23 (2024) . |
| APA | Jiang, W. , Wang, J. , Lin, R. , Chen, R. , Chen, W. , Xie, X. et al. Machine learning-based non-destructive terahertz detection of seed quality in peanut . | Food Chemistry: X , 2024 , 23 . |
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The high-speed movement of Vehicle Users (VUs) in Cognitive Internet of Vehicles (CIoV) causes rapid changes in users location and path loss. In the case of imperfect control channels, the influence of high-speed movement increases the probability of error in sending local spectrum sensing decisions by VUs. On the other hand, Malicious Vehicle Users (MVUs) can launch Spectrum Sensing Data Falsification (SSDF) attacks to deteriorate the spectrum sensing decisions, mislead the final spectrum sensing decisions of Collaborative Spectrum Sensing (CSS), and bring serious security problems to the system. In addition, the high-speed movement can increases the concealment of the MVUs. In this paper, we study the scenario of VUs moving at high speeds, and data transmission in an imperfect control channel, and propose a blockchain-based method to defend against massive SSDF attacks in CIoV networks to prevennt independent and cooperative attacks from MVUs. The proposed method combines blockchain with spectrum sensing and spectrum access, abandons the decision-making mechanism of the Fusion Center (FC) in the traditional CSS, adopts distributed decision-making, and uses Prospect Theory (PT) modeling in the decision-making process, effectively improves the correct rate of final spectrum sensing decision in the case of multiple attacks. The local spectrum sensing decisions of VUs are packaged into blocks and uploaded after the final decision to achieve more accurate and secure spectrum sensing, and then identify MVUs by the reputation value. In addition, a smart contract that changes the mining difficulty of VUs based on their reputation values is proposed. It makes the mining difficulty of MVUs more difficult and effectively limits MVUs' access to the spectrum band. The final simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.
Keyword ：
blockchain blockchain Blockchains Blockchains Cognitive Internet of Vehicles (CIoV) Cognitive Internet of Vehicles (CIoV) Data communication Data communication Decision making Decision making History History Internet of Vehicles Internet of Vehicles prospect theory (PT) prospect theory (PT) Sensors Sensors smart contract smart contract Smart contracts Smart contracts spectrum sensing data falsification (SSDF) attack spectrum sensing data falsification (SSDF) attack
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| GB/T 7714 | Lin, Ruiquan , Li, Fushuai , Wang, Jun et al. A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (5) : 6954-6967 . |
| MLA | Lin, Ruiquan et al. "A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 73 . 5 (2024) : 6954-6967 . |
| APA | Lin, Ruiquan , Li, Fushuai , Wang, Jun , Hu, Jinsong , Zhang, Zaichen , Wu, Liang . A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (5) , 6954-6967 . |
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Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.
Keyword ：
6G mobile communication 6G mobile communication Cognitive radio (CR) network Cognitive radio (CR) network Communication system security Communication system security deep reinforcement learning (DRL) deep reinforcement learning (DRL) energy harvesting (EH) energy harvesting (EH) Internet of Things Internet of Things Jamming Jamming Mobile handsets Mobile handsets physical-layer security (PLS) enhancement physical-layer security (PLS) enhancement Resource management Resource management Wireless communication Wireless communication
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| GB/T 7714 | Lin, Ruiquan , Qiu, Hangding , Wang, Jun et al. Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) : 4899-4913 . |
| MLA | Lin, Ruiquan et al. "Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach" . | IEEE INTERNET OF THINGS JOURNAL 11 . 3 (2024) : 4899-4913 . |
| APA | Lin, Ruiquan , Qiu, Hangding , Wang, Jun , Zhang, Zaichen , Wu, Liang , Shu, Feng . Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) , 4899-4913 . |
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Cognitive Industrial Internet of Things (CIIoT) permits Secondary Users (SUs) to use the spectrum bands owned by Primary Users (PUs) opportunistically. However, in the absence of the PUs, the selfish SUs could mislead the normal SUs to leave the spectrum bands by initiating a Primary User Emulation Attack (PUEA). In addition, the application of Energy Harvesting (EH) technology can exacerbate the threat of security. Because the energy cost of initiating a PUEA is offset to some extent by EH technology which can proactively replenish the energy of the selfish nodes. Thus, EH technology can increase the motivation of the selfish SUs to initiate a PUEA. To address the higher motivation of the selfish SUs attacking in CIIoT scenario where the EH technology is applied, in this paper, an EH-PUEA system model is first established to study the security countermeasures in this severe scenario of PUEA problems. Next, a new reward and punishment defense management mechanism is proposed, and then the dynamics of the selfish SUs and the normal SUs in a CIIoT network are studied based on Evolutionary Game Theory (EGT), and the punishment parameter is adjusted according to the dynamics of the selfish SUs to reduce the proportion of the selfish SUs’ group choosing an attack strategy, so as to increase the throughput achieved by the normal SUs’ group. Finally, the simulation results show that the proposed mechanism is superior to the conventional mechanism in terms of throughput achieved by the normal SUs’ group in CIIoT scenario with EH technology applied. IEEE
Keyword ：
Cognitive Industrial Internet of Things (CIIoT) Cognitive Industrial Internet of Things (CIIoT) Energy harvesting Energy harvesting energy harvesting (EH) energy harvesting (EH) evolutionary game theory (EGT) evolutionary game theory (EGT) Games Games Game theory Game theory Industrial Internet of Things Industrial Internet of Things Jamming Jamming primary user emulation attack (PUEA) primary user emulation attack (PUEA) Security Security Throughput Throughput
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| GB/T 7714 | Wang, J. , Pei, H. , Wang, R. et al. Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things [J]. | IEEE Transactions on Network and Service Management , 2024 , 21 (4) : 1-1 . |
| MLA | Wang, J. et al. "Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things" . | IEEE Transactions on Network and Service Management 21 . 4 (2024) : 1-1 . |
| APA | Wang, J. , Pei, H. , Wang, R. , Lin, R. , Fang, Z. , Shu, F. . Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things . | IEEE Transactions on Network and Service Management , 2024 , 21 (4) , 1-1 . |
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In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks. © 2024 by the authors.
Keyword ：
anti-jamming method anti-jamming method EH-CIoT network EH-CIoT network linearly weighted deep deterministic policy gradient linearly weighted deep deterministic policy gradient resource allocation resource allocation
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| GB/T 7714 | Li, F. , Bao, J. , Wang, J. et al. Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm [J]. | Sensors , 2024 , 24 (16) . |
| MLA | Li, F. et al. "Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm" . | Sensors 24 . 16 (2024) . |
| APA | Li, F. , Bao, J. , Wang, J. , Liu, D. , Chen, W. , Lin, R. . Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm . | Sensors , 2024 , 24 (16) . |
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传统CNN存在参数多，计算量大，部署在CPU与GPU上推理速度慢、功耗大的问题，为满足将卷积神经网络(Convolutional Neural Network, CNN)部署于嵌入式设备，实现实时图像采集与分类的需求，提出了一种基于FPGA平台的Mobilenet V2轻量级卷积神经网络分类器的设计方案。采用Cameralink相机采集图像，设计了裁剪、乒乓缓存和量化的图像预处理方式，实现连续的图像采集，CNN每层分别占用资源与计算结构，实现连续图片处理。设计了一种PW与DW的流水线结构，全连接层的稀疏化计算优化策略，减少了计算量和处理延迟。单张图片分类耗时1.25ms,能耗比为14.50GOP/s/W。
Keyword ：
Cameralink Cameralink CNN CNN FPGA FPGA 流水线结构 流水线结构 稀疏化 稀疏化
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| GB/T 7714 | 方子卿 , 林瑞全 , 孙小坚 . 基于FPGA的CNN分类器设计 [J]. | 电气开关 , 2024 , 62 (01) : 64-68 . |
| MLA | 方子卿 et al. "基于FPGA的CNN分类器设计" . | 电气开关 62 . 01 (2024) : 64-68 . |
| APA | 方子卿 , 林瑞全 , 孙小坚 . 基于FPGA的CNN分类器设计 . | 电气开关 , 2024 , 62 (01) , 64-68 . |
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| GB/T 7714 | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin et al. Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023) [J]. | PLOS ONE , 2024 , 19 (12) . |
| MLA | Liao, Jianbin et al. "Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023)" . | PLOS ONE 19 . 12 (2024) . |
| APA | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin , Lin, Ruiquan , Wang, Jun . Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023) . | PLOS ONE , 2024 , 19 (12) . |
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The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.
Keyword ：
cognitive radio network cognitive radio network deep reinforcement learning deep reinforcement learning energy harvesting energy harvesting physical layer security physical layer security
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| GB/T 7714 | Lin, Ruiquan , Qiu, Hangding , Jiang, Weibin et al. Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks [J]. | SENSORS , 2023 , 23 (2) . |
| MLA | Lin, Ruiquan et al. "Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks" . | SENSORS 23 . 2 (2023) . |
| APA | Lin, Ruiquan , Qiu, Hangding , Jiang, Weibin , Jiang, Zhenglong , Li, Zhili , Wang, Jun . Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks . | SENSORS , 2023 , 23 (2) . |
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物联网通过采用认知无线电的动态频谱共享机制提高了频谱利用率，然而认知物联网（Cognitive Internet of Things,CIoT）容易受到多种攻击，包括干扰攻击和窃听攻击。首先，基于联盟博弈考虑一个合作模型，其中合法用户合作传输以提高信干噪比（Signal to Interference plus Noise Ratio,SINR），而干扰机合作以提高接收信号强度（Jammer Received Signal Strength,JRSS），窃听机旨在降低系统的保密速率。其次，基于演化博弈论研究了CIoT网络中合法用户和攻击者的动态特性，利用能量采集（Energy Harvesting,EH）技术提高用户的发射功率以提高SINR，从而提高用户的合作水平。此外，通过设置协作干扰节点劣化窃听信道以提高系统的保密速率。仿真结果表明，所提方法在应对干扰攻击和窃听攻击问题上是有效的，且在SINR和保密速率方面优于传统方法。
Keyword ：
协作干扰 协作干扰 干扰攻击 干扰攻击 演化博弈 演化博弈 窃听攻击 窃听攻击 联盟博弈 联盟博弈 能量采集 能量采集
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| GB/T 7714 | 王锐亮 , 王俊 , 林瑞全 et al. 认知物联网中基于博弈论的对抗干扰与窃听方法 [J]. | 通信技术 , 2023 , 56 (02) : 167-174 . |
| MLA | 王锐亮 et al. "认知物联网中基于博弈论的对抗干扰与窃听方法" . | 通信技术 56 . 02 (2023) : 167-174 . |
| APA | 王锐亮 , 王俊 , 林瑞全 , 刘佳鑫 , 徐浩东 , 丘航丁 . 认知物联网中基于博弈论的对抗干扰与窃听方法 . | 通信技术 , 2023 , 56 (02) , 167-174 . |
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This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.
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| GB/T 7714 | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin et al. Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks [J]. | PLOS ONE , 2023 , 18 (1) . |
| MLA | Liao, Jianbin et al. "Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks" . | PLOS ONE 18 . 1 (2023) . |
| APA | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin , Lin, Ruiquan , Wang, Jun . Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks . | PLOS ONE , 2023 , 18 (1) . |
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