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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 ：
The recent advent of the Industry 4.0 era has led to the need to transform the industrial Internet of Things towards green, low-carbon and sustainable development. This is due to the fact that traditional industries consume too much energy. It is urgent to make use of digital technology for energy saving and emission reduction. However, there are still some unresolved issues in the transformation process: 1) the inability to use equipment resources thoroughly and efficiently, 2) the waste caused by overly simple resource management. In this paper, based on the above issues, we develop the ambient backscatter system to optimize the overall resource scheduling scheme and combine intelligent algorithms to solve the problem of offloading tasks. The solution optimizes offloading decisions to minimize system energy consumption and latency. Meanwhile, the proposed optimization problem is designed as a Markov decision process by combining the proposed federated learning assigned with asynchronous advantage actor-critic algorithm to obtain the optimal policy. The final evaluation results significantly show that the system performance indicator based on our proposed solution is better than others.
Keyword ：
energy efficiency energy efficiency Industrial Internet of Things Industrial Internet of Things performance optimization performance optimization ambient backscatter ambient backscatter
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| GB/T 7714 | Huang, Yudian , Li, Meng , Yu, F. Richard et al. Performance Optimization for Energy-Efficient Industrial Internet of Things Based on Ambient Backscatter Communication: An A3C-FL Approach [J]. | IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING , 2023 , 7 (3) : 1121-1134 . |
| MLA | Huang, Yudian et al. "Performance Optimization for Energy-Efficient Industrial Internet of Things Based on Ambient Backscatter Communication: An A3C-FL Approach" . | IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 7 . 3 (2023) : 1121-1134 . |
| APA | Huang, Yudian , Li, Meng , Yu, F. Richard , Si, Pengbo , Zhang, Yanhua . Performance Optimization for Energy-Efficient Industrial Internet of Things Based on Ambient Backscatter Communication: An A3C-FL Approach . | IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING , 2023 , 7 (3) , 1121-1134 . |
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Recently, the rise of the Internet of Vehicles (IoV) has driven the broad development of intelligent transportation and smart cities. In order to promote the computing power of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is recognized as a promising computational paradigm and used in vehicular networks. However, there are some essential issues to be considered: 1) privacy and authenticity of data transmission in IoV, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is introduced and adopted to ensure the accuracy and reliability of data transmission and interaction. Meanwhile, we develop an intelligent framework of resource allocation 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 weighted consumption costs of energy consumption and computation overheads can be decreased, and the transactional throughput of the blockchain can be increased. Moreover, due to the continuity and dynamic of the available resources of mobile vehicles and computing servers, the optimization problem is modeled as a Markov decision process (MDP). Facing the large-scale and dynamic characteristics of the system, the asynchronous advantage actor-critic (A3C) approach is introduced to deal with the optimization problem. Simulation results show that our proposed scheme achieves significant advantages over other comparison schemes, such as the total reward of the proposed scheme is about 14% higher than that of the deep Q-network based scheme.
Keyword ：
blockchain blockchain asynchronous advantage actor-critic asynchronous advantage actor-critic mobile edge computing mobile edge computing Internet of Vehicles Internet of Vehicles resource allocation resource allocation
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| GB/T 7714 | Ye, Xinyu , Li, Meng , Si, Pengbo et al. Collaborative and Intelligent Resource Optimization for Computing and Caching in IoV With Blockchain and MEC Using A3C Approach [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2023 , 72 (2) : 1449-1463 . |
| MLA | Ye, Xinyu et al. "Collaborative and Intelligent Resource Optimization for Computing and Caching in IoV With Blockchain and MEC Using A3C Approach" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 72 . 2 (2023) : 1449-1463 . |
| APA | Ye, Xinyu , Li, Meng , Si, Pengbo , Yang, Ruizhe , Wang, Zhuwei , Zhang, Yanhua . Collaborative and Intelligent Resource Optimization for Computing and Caching in IoV With Blockchain and MEC Using A3C Approach . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2023 , 72 (2) , 1449-1463 . |
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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 ：
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 ：
Immutability, decentralization, and linear promoted scalability make the sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportion of cross-shard transactions (CSTs). On the other hand, the assemblage characteristic of the collaborative computing in IoT has not been received attention. Therefore, in this article, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: K-means-clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.
Keyword ：
K-means clustering K-means clustering dynamic graph analysis dynamic graph analysis deep reinforcement learning (DRL) deep reinforcement learning (DRL) Collaborative computing Collaborative computing Internet of Things (IoT) Internet of Things (IoT) sharded blockchain sharded blockchain
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| GB/T 7714 | Yang, Zhaoxin , Yang, Ruizhe , Yu, F. Richard et al. Sharded Blockchain for Collaborative Computing in the Internet of Things: Combined of Dynamic Clustering and Deep Reinforcement Learning Approach [J]. | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (17) : 16494-16509 . |
| MLA | Yang, Zhaoxin et al. "Sharded Blockchain for Collaborative Computing in the Internet of Things: Combined of Dynamic Clustering and Deep Reinforcement Learning Approach" . | IEEE INTERNET OF THINGS JOURNAL 9 . 17 (2022) : 16494-16509 . |
| APA | Yang, Zhaoxin , Yang, Ruizhe , Yu, F. Richard , Li, Meng , Zhang, Yanhua , Teng, Yinglei . Sharded Blockchain for Collaborative Computing in the Internet of Things: Combined of Dynamic Clustering and Deep Reinforcement Learning Approach . | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (17) , 16494-16509 . |
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Abstract ：
Artificial intelligence (AI)-enabled Internet of Things (IoT) has attracted great interests. The accuracy of data training model in AI is vital for further development of IoT. In addition, with the increasing number of intelligent IoT devices, the amounts of data available for transmission, learning and training can lead to serious communication burdens and data reliability issues. In order to address these issues, we study novel network architectures in future 6G networks to support the intelligent IoT. Moreover, inspired by the collective learning of humans, we introduce and adopt a novel method named as collective reinforcement learning (CRL) in the intelligent IoT to realize the sharing of learning and training results. To ensure security and privacy, as well as improve computing efficiency, blockchain, mobile edge computing (MEC) and cloud computing are applied to protect data security and enrich computing resources. On this basis, we formulate an optimization problem in the intelligent IoT based on the proposed framework to optimize transmission latency and energy consumption. Simulation results demonstrate that the system performance has improved significantly. At last, some research challenges and open issues are pointed out to the intelligent IoT in future networks.
Keyword ：
Artificial intelligence Artificial intelligence Training Training Optimization Optimization Internet of Things Internet of Things Cloud computing Cloud computing Blockchains Blockchains 6G mobile communication 6G mobile communication
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| GB/T 7714 | Li, Meng , Yu, F. Richard , Si, Pengbo et al. Intelligent Resource Optimization for Blockchain-Enabled IoT in 6G via Collective Reinforcement Learning [J]. | IEEE NETWORK , 2022 , 36 (6) : 175-182 . |
| MLA | Li, Meng et al. "Intelligent Resource Optimization for Blockchain-Enabled IoT in 6G via Collective Reinforcement Learning" . | IEEE NETWORK 36 . 6 (2022) : 175-182 . |
| APA | Li, Meng , Yu, F. Richard , Si, Pengbo , Zhang, Yanhua , Qian, Yi . Intelligent Resource Optimization for Blockchain-Enabled IoT in 6G via Collective Reinforcement Learning . | IEEE NETWORK , 2022 , 36 (6) , 175-182 . |
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Abstract ：
Immutability, decentralization, and linear promoted scalability make sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportions of cross-shard transactions (CST). On the other hand, assemblage characteristics of collaborative computing in IoT have not been received attention. Therefore, in this paper, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: k-means clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.
Keyword ：
Internet of Things (IoT) Internet of Things (IoT) dynamic graph analysis dynamic graph analysis deep reinforcement learning (DRL) deep reinforcement learning (DRL) collaborative computing collaborative computing sharded blockchain sharded blockchain k-means clustering k-means clustering
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| GB/T 7714 | Yang, Zhaoxin , Li, Meng , Yang, Ruizhe et al. Blockchain Sharding Strategy for Collaborative Computing Internet of Things Combining Dynamic Clustering and Deep Reinforcement Learning [J]. | IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) , 2022 : 2786-2791 . |
| MLA | Yang, Zhaoxin et al. "Blockchain Sharding Strategy for Collaborative Computing Internet of Things Combining Dynamic Clustering and Deep Reinforcement Learning" . | IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) (2022) : 2786-2791 . |
| APA | Yang, Zhaoxin , Li, Meng , Yang, Ruizhe , Yu, F. Richard , Zhang, Yanhua . Blockchain Sharding Strategy for Collaborative Computing Internet of Things Combining Dynamic Clustering and Deep Reinforcement Learning . | IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) , 2022 , 2786-2791 . |
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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 ：
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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