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学者姓名：林瑞全
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Abstract ：
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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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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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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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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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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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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随着无线终端的大规模普及，用户设备（User Equipment,UE）对无线网络的内容分发服务提出了更高的要求。提出了利用设备对设备（Device to Device,D2D）通信技术进行协作中继传输，使得任何UE都可作为潜在中继节点，并且令中继节点为其他UE中继数据，可以提升整体网络的内容分发质量。为弥补UE作为中继节点产生的能耗，采用能量采集（Energy Harvesting,EH）的激励机制，将携能信号作为奖励发送至UE，以提高UE为其他用户中继数据的意愿。同时，为解决中继选择问题，提出了基于联盟博弈方法，对UE和中继节点的合作行为进行分析，为UE选取最优的中继节点，以获取最优的内容分发服务。仿真结果表明，所提方法与贪婪搜索算法相比，可以更大程度地提高系统的吞吐量。
Keyword ：
D2D通信 D2D通信 中继 中继 联盟博弈 联盟博弈 能量采集 能量采集
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| GB/T 7714 | 刘佳鑫 , 林瑞全 , 丘航丁 et al. D2D网络中基于联盟博弈的中继选择方法 [J]. | 通信技术 , 2023 , 56 (01) : 42-48 . |
| MLA | 刘佳鑫 et al. "D2D网络中基于联盟博弈的中继选择方法" . | 通信技术 56 . 01 (2023) : 42-48 . |
| APA | 刘佳鑫 , 林瑞全 , 丘航丁 , 王锐亮 , 鲍家旺 , 徐浩东 . D2D网络中基于联盟博弈的中继选择方法 . | 通信技术 , 2023 , 56 (01) , 42-48 . |
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With the rapid development of technologies such as wireless communications and the Internet of Things (IoT), the proliferation of IoT devices will intensify the competition for spectrum resources. The introduction of cognitive radio technology in IoT can minimize the shortage of spectrum resources. However, the open environment of cognitive IoT may involve free-riding problems. Due to the selfishness of the participants, there are usually a large number of free-riders in the system who opportunistically gain more rewards by stealing the spectrum sensing results from other participants and accessing the spectrum without spectrum sensing. However, this behavior seriously affects the fault tolerance of the system and the motivation of the participants, resulting in degrading the system's performance. Based on the energy-harvesting cognitive IoT model, this paper considers the free-riding problem of Secondary Users (SUs). Since free-riders can harvest more energy in spectrum sensing time slots, the application of energy harvesting technology will exacerbate the free-riding behavior of selfish SUs in Cooperative Spectrum Sensing (CSS). In order to prevent the low detection performance of the system due to the free-riding behavior of too many SUs, a penalty mechanism is established to stimulate SUs to sense the spectrum normally during the sensing process. In the system model with multiple primary users (PUs) and multiple SUs, each SU considers whether to free-ride and which PU's spectrum to sense and access in order to maximize its own interests. To address this issue, a two-layer game-based cooperative spectrum sensing and access method is proposed to improve spectrum utilization. Simulation results show that compared with traditional methods, the average throughput of the proposed TL-CSAG algorithm increased by 26.3% and the proposed method makes the SUs allocation more fair.
Keyword ：
cognitive IoT cognitive IoT cooperative spectrum sensing cooperative spectrum sensing dynamic spectrum access dynamic spectrum access energy harvesting energy harvesting game theory game theory
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| GB/T 7714 | Jiang, Kejian , Ma, Chi , Lin, Ruiquan et al. Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks [J]. | SENSORS , 2023 , 23 (13) . |
| MLA | Jiang, Kejian et al. "Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks" . | SENSORS 23 . 13 (2023) . |
| APA | Jiang, Kejian , Ma, Chi , Lin, Ruiquan , Wang, Jun , Jiang, Weibing , Hou, Haifeng . Free-Rider Games for Cooperative Spectrum Sensing and Access in CIoT Networks . | SENSORS , 2023 , 23 (13) . |
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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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近年来，卷积神经网络由于其出色的性能被广泛应用在各个领域，如图像识别、语音识别与翻译和自动驾驶等；但是传统卷积神经网络(CNN,convolutional neural network)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；针对上述问题，采用量化感知训练(QAT,quantization aware training)的方式在保证图像分类准确率的前提下，将网络参数总量压缩为原网络的1/4;将网络权重全部部署在FPGA的片内资源上，克服了片外存储带宽的限制，减少了访问片外存储资源带来的功耗；在MobileNetV2网络的层内以及相邻的点卷积层之间提出一种协同配合的流水线结构，极大地提高了网络的实时性；提出一种存储器与数据读取的优化策略，根据并行度调整数据的存储排列方式及读取顺序，进一步节约了片内BRAM资源。最终在Xilinx的Virtex-7 VC707开发板上实现了一套性能优、功耗小的轻量级卷积神经网络MobileNetV2识别系统，200 MHz时钟下达到了170.06 GOP/s的吞吐量，功耗仅为6.13 W,能耗比达到了27.74 GOP/s/W,是CPU的92倍，GPU的25倍，性能较其他实现有明显的优势。
Keyword ：
MobileNet MobileNet 并行计算 并行计算 流水线结构 流水线结构 硬件加速 硬件加速 量化感知训练 量化感知训练
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| GB/T 7714 | 孙小坚 , 林瑞全 , 方子卿 et al. 基于FPGA加速的低功耗的MobileNetV2网络识别系统 [J]. | 计算机测量与控制 , 2023 , 31 (05) : 221-227,234 . |
| MLA | 孙小坚 et al. "基于FPGA加速的低功耗的MobileNetV2网络识别系统" . | 计算机测量与控制 31 . 05 (2023) : 221-227,234 . |
| APA | 孙小坚 , 林瑞全 , 方子卿 , 马驰 . 基于FPGA加速的低功耗的MobileNetV2网络识别系统 . | 计算机测量与控制 , 2023 , 31 (05) , 221-227,234 . |
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