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学者姓名：张延华
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Abstract ：
本发明公开了自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法，通过构建多跳传输模型、区块链模型、MEC服务器计算模型，计算任务在列车之间多跳传输的时延、经济成本和区块链系统的时延，以及MEC服务器处理任务产生的时延和经济成本，从而根据系统状态通过训练深度神经网络，指导调整卸载路由路径的选择、卸载决策和区块大小的选择，完成场景内的最优资源分配。仿真实验表明，本发明在节省系统时延和系统总经济成本方面具有一定的优势。
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| GB/T 7714 | 李萌 , 田琳琳 , 司鹏搏 et al. 自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法 : CN202310010374.0[P]. | 2023-01-04 . |
| MLA | 李萌 et al. "自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法" : CN202310010374.0. | 2023-01-04 . |
| APA | 李萌 , 田琳琳 , 司鹏搏 , 杨睿哲 , 孙艳华 , 孙恩昌 et al. 自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法 : CN202310010374.0. | 2023-01-04 . |
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Abstract ：
Driven by numerous emerging mobile devices and various Quality-of-Service (QoS) requirements, mobile-edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource; 2) simple or nonintelligent resource management; and 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing, and avoid excessive consumption of system resources. Based on the designed network model, a cloud-edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval, and transmission power, it aims to minimize the consumption overheads of system energy and latency. Then, the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.
Keyword ：
Internet of Things (IoT) Internet of Things (IoT) Internet of Things Internet of Things Blockchain Blockchain Blockchains Blockchains Resource management Resource management 6G mobile communication 6G mobile communication mobile-edge computing (MEC) mobile-edge computing (MEC) collective reinforcement learning (CRL) collective reinforcement learning (CRL) Computational modeling Computational modeling Servers Servers sixth generation (6G) sixth generation (6G) Optimization Optimization
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| GB/T 7714 | Li, Meng , Pei, Pan , Yu, F. Richard et al. Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach [J]. | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (22) : 23115-23129 . |
| MLA | Li, Meng et al. "Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach" . | IEEE INTERNET OF THINGS JOURNAL 9 . 22 (2022) : 23115-23129 . |
| APA | Li, Meng , Pei, Pan , Yu, F. Richard , Si, Pengbo , Li, Yu , Sun, Enchang et al. Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach . | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (22) , 23115-23129 . |
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Abstract ：
本发明公开了一种基于区块链和国密算法的文件信息数据存储方法，为了更安全便捷的存储文件，防止被篡改，保护用户个人隐私和利益。将区块链和国密算法结合起来，使用去中心化的公开账本区块链做为存储工具。使用国密SM3哈希算法生成哈希值，提取文件信息，可占用较小的存储空间记录关键信息。采用POST请求的方式，安全快捷的传输数据。区块链后台系统中使用Kafka算法高效完成节点之间的共识，使数据一经上链便无法篡改。使用HTML\CSS\JavaScript搭建前端，查询到数据以及对应的区块信息在前端界面显示。最终能安全有效便捷的存储文件信息数据。
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| GB/T 7714 | 司鹏搏 , 周宇泽 , 李萌 et al. 一种基于区块链和国密算法的文件信息数据存储方法 : CN202211161053.2[P]. | 2022-09-22 . |
| MLA | 司鹏搏 et al. "一种基于区块链和国密算法的文件信息数据存储方法" : CN202211161053.2. | 2022-09-22 . |
| APA | 司鹏搏 , 周宇泽 , 李萌 , 杨睿哲 , 孙艳华 , 张延华 . 一种基于区块链和国密算法的文件信息数据存储方法 : CN202211161053.2. | 2022-09-22 . |
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Abstract ：
Heterogeneous networks (HetNets) with end-to-end (E2E) network slicing are regarded as effective approaches to meet diverse service requirements from vertical industries. Due to the dense deployment of base stations (BSs) and the complicated associations between BSs and E2E network slices (NSs) in the scenario, the handoff problem faces challenges of the huge system state space and handoff action space and the considerable communication overhead. In this paper, we take these issues into account and consider a distributed E2E NS handoff decision framework in the HetNet. A decentralized Markov decision process (DEC-MDP)-based model is formulated for the distributed E2E NS handoff problem, and the jointly observable and random characteristics of the DEC-MDP are analyzed. To obtain a theoretical performance reference, the original distributed E2E NS handoff problem is simplified, and a Nash equilibrium-based performance bound is given. More practically, the multi-agent double deep Q-network-based distributed handoff (MA-DDQN-DH) algorithm with the centralized training and decentralized executing framework is proposed. Simulation results show that the Nash equilibrium-based performance bound is reasonable, and the proposed MA-DDQN-DH algorithm performs well in the comparison.
Keyword ：
Heuristic algorithms Heuristic algorithms Bandwidth Bandwidth Distributed network slice handoff Distributed network slice handoff decentralized Markov decision process decentralized Markov decision process Nash equilibrium Nash equilibrium Quality of service Quality of service Real-time systems Real-time systems Costs Costs multi-agent deep reinforcement learning multi-agent deep reinforcement learning Training Training Network slicing Network slicing
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| GB/T 7714 | Wu, Wenjun , Yang, Feng , Gao, Yang et al. Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution [J]. | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2022 , 21 (12) : 11222-11236 . |
| MLA | Wu, Wenjun et al. "Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution" . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 21 . 12 (2022) : 11222-11236 . |
| APA | Wu, Wenjun , Yang, Feng , Gao, Yang , Wang, Xiaoxi , Si, Pengbo , Zhang, Yanhua et al. Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2022 , 21 (12) , 11222-11236 . |
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Abstract ：
随着第五代(the fifth generation,5G)移动通信系统商用进程的推进,设备到设备(device-to-device,D2D)通信越发受到人们的关注.频率、功率等资源分配作为优化D2D通信的关键技术成为重要的研究课题,因此对蜂窝网络中D2 D通信资源分配研究进行综述.首先介绍D2 D通信模型和模式;其次从数学理论角度出发,分析蜂窝网络中D2 D通信资源分配的主要方法:基于图论、超图理论、博弈论、机器学习和启发式算法等,对这些算法进行对比分析;最后总结上述D2 D资源分配方法存在的主要不足,并对蜂窝网络中D2 D通信的未来发展进行展望.
Keyword ：
资源分配 资源分配 机器学习 机器学习 博弈论 博弈论 D2D通信 D2D通信 超图理论 超图理论 5G网络 5G网络
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| GB/T 7714 | 孙恩昌 , 屈晗星 , 袁永仪 et al. 蜂窝网络中D2D通信资源分配方法综述 [J]. | 北京工业大学学报 , 2021 , 47 (10) : 1188-1200 . |
| MLA | 孙恩昌 et al. "蜂窝网络中D2D通信资源分配方法综述" . | 北京工业大学学报 47 . 10 (2021) : 1188-1200 . |
| APA | 孙恩昌 , 屈晗星 , 袁永仪 , 张延华 . 蜂窝网络中D2D通信资源分配方法综述 . | 北京工业大学学报 , 2021 , 47 (10) , 1188-1200 . |
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Abstract ：
With continuous emergence of the new mobile applications, multi-access edge computing (MEC) is generally regarded as a promising technology to enable the computing-intensive and delay-sensitive services at the mobile devices by pushing more computing resources to the network edge. However, computation offloading, which has been a hot topic for MEC networks, is still facing the challenges due to the diversified task characteristics of the new mobile applications and the multidimensional resource conditions of the MEC networks. In this paper, we take the time-dependent logic characteristics of the tasks into consideration and propose a more flexible computation offloading and task scheduling strategy based on the multi-connectivity technology to further minimize the MEC network cost. We model our problem as a multi-objective optimization problem and propose a genetic algorithm-based flexible computation offloading and task scheduling algorithm (GA-FCOTS) to search for the optimal solution iteratively. Simulation results verify the convergence of the proposed algorithm, and show that the proposed algorithm can balance multiple performances and reduce the network cost effectively compared with the other baseline schemes.
Keyword ：
computation offloading computation offloading task scheduling task scheduling Multi-access edge computing Multi-access edge computing genetic algorithm genetic algorithm
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| GB/T 7714 | Sun, Yang , Li, Huixin , Wei, Tingting et al. Dependency-Aware Flexible Computation Offloading and Task Scheduling for Multi-access Edge Computing Networks [J]. | 24TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC 2021): PAVING THE WAY FOR DIGITAL AND WIRELESS TRANSFORMATION , 2021 . |
| MLA | Sun, Yang et al. "Dependency-Aware Flexible Computation Offloading and Task Scheduling for Multi-access Edge Computing Networks" . | 24TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC 2021): PAVING THE WAY FOR DIGITAL AND WIRELESS TRANSFORMATION (2021) . |
| APA | Sun, Yang , Li, Huixin , Wei, Tingting , Zhang, Yanhua , Wang, Zhuwei , Wu, Wenjun et al. Dependency-Aware Flexible Computation Offloading and Task Scheduling for Multi-access Edge Computing Networks . | 24TH INTERNATIONAL SYMPOSIUM ON WIRELESS PERSONAL MULTIMEDIA COMMUNICATIONS (WPMC 2021): PAVING THE WAY FOR DIGITAL AND WIRELESS TRANSFORMATION , 2021 . |
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Abstract ：
In order to enable more and more multimedia content to be shared in the vehicular network, edge caching is a promising approach to cache content near the vehicles to reduce the burden of communication link and improve quality of service. However, the high mobility of vehicles and change in content popularity bring new challenges to edge caching in dynamic environment. Under the limitation of cache capacity, we propose a collaborative caching strategy in vehicular network to maximize the data throughput obtained from edge devices. Specifically, we first use Hawkes process to adapt to the dynamic change of contents' popularity. Then, a cooperative content caching scheme based on deep reinforcement learning (DRL) is proposed. Finally, the performance of the scheme is evaluated by simulation experiments.
Keyword ：
deep reinforcement learning deep reinforcement learning edge caching edge caching Hawkes process Hawkes process vehicular networks vehicular networks
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| GB/T 7714 | Xing, Yuping , Sun, Yanhua , Qiao, Lan et al. Deep Reinforcement Learning for Cooperative Edge Caching in Vehicular Networks [C] . 2021 : 144-149 . |
| MLA | Xing, Yuping et al. "Deep Reinforcement Learning for Cooperative Edge Caching in Vehicular Networks" . (2021) : 144-149 . |
| APA | Xing, Yuping , Sun, Yanhua , Qiao, Lan , Wang, Zhuwei , Si, Pengbo , Zhang, Yanhua . Deep Reinforcement Learning for Cooperative Edge Caching in Vehicular Networks . (2021) : 144-149 . |
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Abstract ：
Nowadays, the rise of the Internet of Vehicles (IoV) has led to the rapid development of smart transportation. To increase the computing capacity of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is considered as an indispensable solution. However, there are some essential issues to be considered: 1) security and privacy of data transmission, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is adopted to ensure reliable transmission and interaction of data. Meanwhile, we develop an intelligent resource framework about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the energy consumption and computation overheads can be decreased, and the data throughput of the blockchain can be increased significantly. Moreover, the proposed optimization problem is modeled and formulated as a Markov decision process. Facing the complexity and dynamic of resource allocation, the asynchronous advantage actor-critic approach is considered and applied to solve the optimization problem. Experiment results demonstrate that the advantages of the proposed optimization scheme are obvious compared with other existing schemes.
Keyword ：
mobile edge computing (MEC) mobile edge computing (MEC) Internet of Vehicles Internet of Vehicles blockchain blockchain resource allocation resource allocation asynchronous advantage actor-critic (A3C) approach asynchronous advantage actor-critic (A3C) approach
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| GB/T 7714 | Ye, Xinyu , Li, Meng , Yu, F. Richard et al. MEC and Blockchain-Enabled Energy-Efficient Internet of Vehicles Based on A3C Approach [J]. | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) , 2021 . |
| MLA | Ye, Xinyu et al. "MEC and Blockchain-Enabled Energy-Efficient Internet of Vehicles Based on A3C Approach" . | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) (2021) . |
| APA | Ye, Xinyu , Li, Meng , Yu, F. Richard , Si, Pengbo , Wang, Zhuwei , Zhang, Yanhua . MEC and Blockchain-Enabled Energy-Efficient Internet of Vehicles Based on A3C Approach . | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) , 2021 . |
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Abstract ：
A compact and reconfigurable low noise amplifier (LNA) is proposed by combining an input transistor, composite transistors with Darlington configuration as the amplification and output transistor, T-type structure composite resistors instead of a simplex structure resistor, a shunt inductor feedback realized by a tunable active inductor (AI), a shunt inductor peaking technique realized by another tunable AI. The division and collaboration among different resistances in the T-type structure composite resistor realize simultaneously input impedance matching, output impedance matching and good noise performance; the shunt feedback and peaking technique using two tunable AIs not only extend frequency bandwidth and improve gain flatness, but also make the gain and frequency band can be tuned simultaneously by the external bias of tunable AIs; the Darlington configuration of composite transistors provides high gain; furthermore, the adoption of the small size AIs instead of large size passive spiral inductor, and the use of composite resistors make the LNA have a small size. The LNA is fabricated and verified by GaAs/InGaP hetero-junction bipolar transistor (HBT) process. The results show that at the frequency of 7GHz, the gain S21 is maximum and up to 19dB; the S21 can be tuned from 17dB to 19dB by tuning external bias of tunable AIs, that is, the tunable amount of S21 is 2dB, and similarly at 8GHz; the tunable range of 3dB bandwidth is 1GHz. In addition, the gain S21 flatness is better than 0.4dB under frequency from 3.1GHz to 10.6GHz; the size of the LNA only has 760μm×1260μm (including PADs). Therefore, the proposed strategies in the paper provide a new solution to the design of small size and reconfigurable ultra-wideband (UWB) LNA and can be used further to adjust the variations of gain and bandwidth of radio frequency integrated circuits (RFICs) due to package, parasitic and the variation of fabrication process and temperature. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.
Keyword ：
Transistors Transistors Bandwidth Bandwidth Resistors Resistors Composite structures Composite structures Electric inductors Electric inductors Ultra-wideband (UWB) Ultra-wideband (UWB) Impedance matching (electric) Impedance matching (electric) Gallium arsenide Gallium arsenide Feedback amplifiers Feedback amplifiers Low noise amplifiers Low noise amplifiers III-V semiconductors III-V semiconductors
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| GB/T 7714 | Zhang, Zheng , Zhang, Yanhua , Yang, Ruizhe et al. A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques [J]. | High Technology Letters , 2021 , 27 (1) : 38-42 . |
| MLA | Zhang, Zheng et al. "A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques" . | High Technology Letters 27 . 1 (2021) : 38-42 . |
| APA | Zhang, Zheng , Zhang, Yanhua , Yang, Ruizhe , Shen, Pei , Ding, Chunbao , Liu, Yaze et al. A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques . | High Technology Letters , 2021 , 27 (1) , 38-42 . |
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Abstract ：
Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes. © 2014 IEEE.
Keyword ：
Industrial internet of things (IIoT) Industrial internet of things (IIoT) Energy utilization Energy utilization Reinforcement learning Reinforcement learning Markov processes Markov processes Deep learning Deep learning Blockchain Blockchain Energy efficiency Energy efficiency
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| GB/T 7714 | Yang, Le , Li, Meng , Si, Pengbo et al. Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning [J]. | IEEE Internet of Things Journal , 2021 , 8 (4) : 2318-2329 . |
| MLA | Yang, Le et al. "Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning" . | IEEE Internet of Things Journal 8 . 4 (2021) : 2318-2329 . |
| APA | Yang, Le , Li, Meng , Si, Pengbo , Yang, Ruizhe , Sun, Enchang , Zhang, Yanhua . Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning . | IEEE Internet of Things Journal , 2021 , 8 (4) , 2318-2329 . |
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