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Blockchain and MEC-Assisted Reliable Billing Data Transmission over Electric Vehicular Network:An Actor–Critic RL Approach 认领 引用 被引量：5
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作者 Xinyu Ye Meng Li +3 位作者 Pengbo Si Ruizhe Yang Enchang Sun Yanhua Zhang 《China Communications》 SCIE EI CSCD 2021年第8期279-296,共18页
Recently,electric vehicles(EVs)have been widely used under the call of green travel and environmental protection,and diverse requirements for charging are also increasing gradually.In order to ensure the authenticity ... Recently,electric vehicles(EVs)have been widely used under the call of green travel and environmental protection,and diverse requirements for charging are also increasing gradually.In order to ensure the authenticity and privacy of charging information interaction,blockchain technology is proposed and applied in charging station billing systems.However,there are some issues in blockchain itself,including lower computing efficiency of the nodes and higher energy consumption in the consensus process.To handle the above issues,in this paper,combining blockchain and mobile edge computing(MEC),we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process.By jointly optimizing the primary and replica nodes offloading decisions,block size and block interval,the transaction throughput of the blockchain system is maximized,as well as the latency and energy consumption of the system are minimized.Moreover,we formulate the joint optimization problem as a Markov decision process(MDP).To tackle the dynamic and continuity of the system state,the reinforcement learning(RL)is introduced to solve the MDP problem.Finally,simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes. 展开更多
关键词 electric vehicles billing data interaction blockchain mobile edge computing reinforcement learning
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Analysis and Optimization of Validation Procedure in Blockchain-Enhanced Wireless Resource Sharing and Transactions 认领 引用
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作者 Enyu Du Yang Gao +3 位作者 Wenjun Wu Zhaoxin Yang Yufeng Yin Pengbo Si 《China Communications》 SCIE CSCD 2023年第10期245-261,共17页
To ensure the security of resource and intelligence sharing in 6G,blockchain has been widely adopted in wireless communications and applications.Although blockchain can ensure the traceability and non-tamperability of... To ensure the security of resource and intelligence sharing in 6G,blockchain has been widely adopted in wireless communications and applications.Although blockchain can ensure the traceability and non-tamperability of data in the concatenated blocks,it cannot guarantee the honest behaviors of users in the application before the generation of transactions.Thus,additional technologies are required to ensure that the source of blockchain data is reliable.In this paper,the detailed procedure is designed for the application-oriented task validation in the blockchainenhanced computing resource sharing and transactions in ultra dense networks(UDN).The corresponding queuing model is built and analyzed with the consideration of the wireless re-transmission and the probability of malicious deception by users.Based on the analysis results,the UDN deployment is optimized to save network cost while ensuring latency performance.Numerical results verify our analysis,and the optimized system deployment including the number and service capacities of both base stations and mobile edge computing(MEC)servers are also given with various system settings. 展开更多
关键词 blockchain queuing theory wireless resource sharing validation procedure
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Proximal Policy Optimization-Based Committee Selection Algorithm in Blockchain-Enabled Mobile Edge Computing Systems 认领 引用 被引量：4
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作者 Wenjun Wu Dehao Sun +2 位作者 Kaiqi Jin Yang Sun Pengbo Si 《China Communications》 SCIE CSCD 2022年第6期50-65,共16页
To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles(Io V)and Industrial Internet of Things(IIo T),the blockchain-enabled Mobile Edge Computing(MEC)sy... To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles(Io V)and Industrial Internet of Things(IIo T),the blockchain-enabled Mobile Edge Computing(MEC)system has received extensive attention.However,blockchain is a computing and communication intensive technology due to the complex consensus mechanisms.To facilitate the implementation of blockchain in the MEC system,this paper adopts the committee-based Practical Byzantine Fault Tolerance(PBFT)consensus algorithm and focuses on the committee selection problem.Vehicles and IIo T devices generate the transactions which are records of the application tasks.Base Stations(BSs)with MEC servers,which serve the transactions according to the wireless channel quality and the available computing resources,are blockchain nodes and candidates for committee members.The income of transaction service fees,the penalty of service delay,the decentralization of the blockchain and the communication complexity of the consensus process constitute the performance index.The committee selection problem is modeled as a Markov decision process,and the Proximal Policy Optimization(PPO)algorithm is adopted in the solution.Simulation results show that the proposed PPO-based committee selection algorithm can adapt to the system design requirements with different emphases and outperforms other comparison methods. 展开更多
关键词 blockchain mobile edge computing deep reinforcement learning consensus mechanism
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Optimal Spectrum Management with Dynamic Service and Cost Constraints in Multihop CR Networks 认领 引用
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作者 Qiuran Li Pengbo Si +1 位作者 Ruizhe Yang Yanhua Zhang 《信息工程期刊（中英文版）》 2016年第4期86-92,共7页
To cope with the challenging issue that spectrum bands for wireless communications are almost used up, cognitive radio （CR） technologies have been proposed, allowing the secondary users （SUs） to harvest and utiliz... To cope with the challenging issue that spectrum bands for wireless communications are almost used up, cognitive radio （CR） technologies have been proposed, allowing the secondary users （SUs） to harvest and utilize the spectrum broads that are licensed to primary users （PUs） but not utilized currently. Besides, the multihop cognitive radio network architecture is also introduced to efficiently utilize the harvested spectrum broads by adopting the fixed-location relay stations （RSs）. In this paper, we investigate the spectrum management problem to schedule the harvested spectrum bands to the txansmission links between the RSs in multihop cognitive radio networks （CRNs）. As spectrum trading is considered, the CR network optimally selects the available spectrum bands according to their dynamic service requirements and renting prices. To solve this NP-hard optimization problem, two heuristic algorithms are also proposed to obtain approximate results while reducing the computational complexity. Extensive simulation results demonstrate the performance improvement of the proposed scheme 展开更多
关键词 动态服务 管理问题 光谱 网络 CR 费用 认知无线电 计算复杂性
Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in internet of vehicles 认领 引用 被引量：7
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作者 Junyu Dong Wenjun Wu +2 位作者 Yang Gao Xiaoxi Wang Pengbo Si 《Intelligent and Converged Networks》 EI 2020年第3期234-242,共9页
Nowadays,Edge Information System(EIS)has received a lot of attentions.In EIS,Distributed Machine Learning(DML),which requires fewer computing resources,can implement many artificial intelligent applications efficientl... Nowadays,Edge Information System(EIS)has received a lot of attentions.In EIS,Distributed Machine Learning(DML),which requires fewer computing resources,can implement many artificial intelligent applications efficiently.However,due to the dynamical network topology and the fluctuating transmission quality at the edge,work node selection affects the performance of DML a lot.In this paper,we focus on the Internet of Vehicles(IoV),one of the typical scenarios of EIS,and consider the DML-based High Definition(HD)mapping and intelligent driving decision model as the example.The worker selection problem is modeled as a Markov Decision Process(MDP),maximizing the DML model aggregate performance related to the timeliness of the local model,the transmission quality of model parameters uploading,and the effective sensing area of the worker.A Deep Reinforcement Learning(DRL)based solution is proposed,called the Worker Selection based on Policy Gradient(PG-WS)algorithm.The policy mapping from the system state to the worker selection action is represented by a deep neural network.The episodic simulations are built and the REINFORCE algorithm with baseline is used to train the policy network.Results show that the proposed PG-WS algorithm outperforms other comparation methods. 展开更多
关键词 edge information system internet of vehicles distributed machine learning deep reinforcement learning worker selection
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