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学者姓名：陈星
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Abstract ：
现今社会上数据的规模和种类变得越来越庞大和多样化,如何安全可信地共享异构数据资源成为了亟待解决的问题.为实现大数据的可信互联,提出基于Hyperledger Fabric的数据可信共享平台.首先,针对数据异源异构的问题,定义了数据架构的转换规则;然后,以数据提供方和数据需求方之间的数据共享全过程为导向,提出了数据可信追溯机制,保证了数据共享的真实性和完整性;此外,文中设计了一种数据处理即服务的数据共享框架,在确保数据可信的前提下,支撑数据调用、数据训练和数据匹配操作.通过对执行效率和智能合约性能进行验证分析,证明了本平台的有效性和实用性.
Keyword ：
Hyperledger Fabric Hyperledger Fabric 区块链 区块链 可信凭证 可信凭证 数据共享 数据共享 智能合约 智能合约
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| GB/T 7714 | 林哲旭 , 陈汉林 , 刘漳辉 et al. 基于Hyperledger Fabric的数据可信共享平台 [J]. | 小型微型计算机系统 , 2025 , 46 (1) : 189-199 . |
| MLA | 林哲旭 et al. "基于Hyperledger Fabric的数据可信共享平台" . | 小型微型计算机系统 46 . 1 (2025) : 189-199 . |
| APA | 林哲旭 , 陈汉林 , 刘漳辉 , 陈星 , 莫毓昌 . 基于Hyperledger Fabric的数据可信共享平台 . | 小型微型计算机系统 , 2025 , 46 (1) , 189-199 . |
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随着物联网和5G的不断发展,用户对流畅体验感的需求日益迫切,对于数据传输速率、响应延迟和服务质量的要求不断提高.边缘计算范式能够使服务器更加接近用户和设备,更快地响应数据请求,提高网络的效率和可扩展性,从而提供更好的用户体验.在此基础上了,本文提出了一种多边缘计算服务器协同提供计算服务的网络系统模型,并定义了服务部署和计算任务卸载联合优化问题.针对该问题,提出了一种基于蚁群优化算法(Ant Colony Optimization,ACO)的服务部署和计算任务卸载联合优化问题解决策略.实验结果表明,相较于基准策略,所提出的策略能够显著降低任务完成时延和能耗,并有效提高网络的效率和可扩展性.
Keyword ：
任务卸载 任务卸载 服务部署 服务部署 蚁群优化算法 蚁群优化算法 边缘计算 边缘计算
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| GB/T 7714 | 邓福康 , 许英豪 , 张建山 et al. ACO使能的边缘计算系统服务部署和计算任务卸载方法 [J]. | 小型微型计算机系统 , 2025 , 46 (2) : 314-320 . |
| MLA | 邓福康 et al. "ACO使能的边缘计算系统服务部署和计算任务卸载方法" . | 小型微型计算机系统 46 . 2 (2025) : 314-320 . |
| APA | 邓福康 , 许英豪 , 张建山 , 陈星 . ACO使能的边缘计算系统服务部署和计算任务卸载方法 . | 小型微型计算机系统 , 2025 , 46 (2) , 314-320 . |
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Mobile Edge Computing (MEC) can augment the capability of Internet of Things (IoT) mobile devices (MDs) through offloading the computation-intensive tasks to their adjacent servers. Synergistic computation offloading among MEC servers is one possible solution to reduce the completion time of system during peak hours. However, due to the large number of servers and the long distance between base stations (BSs), synchronizing the information of all servers takes a long time, which is not applicable to the fluctuant environments. Meanwhile, each server from different BSs is typically selfish and rational, and can only obtain the imperfect information from its adjacent servers, which is a challenge for computation offloading among servers from a global perspective. This article proposes a game-based computation offloading scheme with imperfect information in multi-edge environments. First, a non-cooperative game with imperfect information is designed to analyze the complex interactions during synergistic computation offloading among MEC servers. Second, a Synergistic Balancing Offloading Algorithm (SBOA) through distributed decision-making manner to obtain the optimal offloading decision is proposed, which guarantees that the game converges to a Nash Equilibrium (NE) point. Extensive simulation results reveal the fast convergence of SBOA. As the percentage of high-load servers rises and the number of heavy tasks increases, SBOA performs better than other benchmark algorithms in terms of timeliness, effectiveness, and system completion time.
Keyword ：
Cloud computing Cloud computing computation offloading computation offloading Decision making Decision making Delays Delays Games Games imperfect information imperfect information Internet of Things Internet of Things Internet of Things (IoT) Internet of Things (IoT) Load management Load management mobile edge computing (MEC) mobile edge computing (MEC) non-cooperative game non-cooperative game Performance evaluation Performance evaluation Servers Servers Simulation Simulation Technological innovation Technological innovation
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| GB/T 7714 | Lin, Bing , Weng, Jie , Chen, Xing et al. A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments [J]. | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2025 , 18 (1) : 1-14 . |
| MLA | Lin, Bing et al. "A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments" . | IEEE TRANSACTIONS ON SERVICES COMPUTING 18 . 1 (2025) : 1-14 . |
| APA | Lin, Bing , Weng, Jie , Chen, Xing , Ma, Yun , Hsu, Ching-Hsien . A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments . | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2025 , 18 (1) , 1-14 . |
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Mobile edge computing (MEC) offers low-latency and flexible computing services for mobile devices (MDs) in Industrial Internet of Things (IIoT). Edge servers (ESs) in general belong to different subjects and will focus on their own interests. They may be reluctant to provide computation resources to MDs without appropriate incentives. Meanwhile, there is a trust issue in trading computation resources between ESs and MDs. Due to the complex interaction between ESs and MDs, it is a challenge for ESs to gain satisfactory revenue through reasonable resource pricing strategies, and for MDs to improve their Quality of Experience (QoE) through efficient computation offloading strategies. This article proposes a Stackelberg game-based computation offloading and resource pricing scheme (SGCS) in blockchain-enable MEC for IIoT. First, a blockchain-based resource trading framework is designed to enable trusted resource transactions. Second, a multileader multifollower Stackelberg game is presented to analyze the complex interactions in the multi-ES and multi-MD environments. Finally, the iterative proximal algorithm (IPA) for MDs' offloading decision and the subgradient-based iterative pricing algorithm (SIPA) for ESs' pricing decision are proposed, respectively, which guarantees that the game converges to a Stackelberg equilibrium (SE). Compared with multiagent deep deterministic policy gradient, genetic algorithm and PSO-GA (i.e., benchmark strategies), the average disutility of MDs with our proposed scheme is reduced by 6.95%-13.07%, 3.09%-20.41%, and 2.36%-17.22%, respectively. Moreover, with the increase of the number of MDs, our proposed scheme has better robustness, which can effectively deal with large-scale scenarios.
Keyword ：
Computation offloading Computation offloading Industrial Internet of Things (IIoT) Industrial Internet of Things (IIoT) mobile edge computing (MEC) mobile edge computing (MEC) Stackelberg game Stackelberg game
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| GB/T 7714 | Lin, Bing , Chen, Xuzhan , Chen, Xing et al. SGCS: An Intelligent Stackelberg-Game-Based Computation Offloading and Resource Pricing Scheme in Blockchain-Enabled MEC for IIoT [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (16) : 26727-26740 . |
| MLA | Lin, Bing et al. "SGCS: An Intelligent Stackelberg-Game-Based Computation Offloading and Resource Pricing Scheme in Blockchain-Enabled MEC for IIoT" . | IEEE INTERNET OF THINGS JOURNAL 11 . 16 (2024) : 26727-26740 . |
| APA | Lin, Bing , Chen, Xuzhan , Chen, Xing , Ma, Yun , Xiong, Neal N. . SGCS: An Intelligent Stackelberg-Game-Based Computation Offloading and Resource Pricing Scheme in Blockchain-Enabled MEC for IIoT . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (16) , 26727-26740 . |
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Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.
Keyword ：
Artificial intelligence (AI) Artificial intelligence (AI) Deep reinforcement learning Deep reinforcement learning Multi-hop offloading Multi-hop offloading Vehicle Edge Computing (VEC) Vehicle Edge Computing (VEC) Workflow application Workflow application
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| GB/T 7714 | Lin, B. , Chen, Q. , Chen, X. et al. DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks [J]. | IEEE Transactions on Consumer Electronics , 2024 . |
| MLA | Lin, B. et al. "DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks" . | IEEE Transactions on Consumer Electronics (2024) . |
| APA | Lin, B. , Chen, Q. , Chen, X. , Jia, W.-K. , Lu, Y. , Xiong, N.N. . DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks . | IEEE Transactions on Consumer Electronics , 2024 . |
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Mobile edge computing (MEC) offers a promising technology that deploys computing resources closer to mobile devices for improving performance. Most of the existing studies support on-demand remote execution of the computing tasks in applications through program transformation, but they commonly assume that mobile devices merely resort to a single server for computation offloading, which cannot make full use of the scattered and changeable computing resources. Thus, for object-oriented applications, we propose a novel approach, called FUNOff, to support the dynamic offloading of applications in MEC at the function granularity. First, we extract a call tree via code analysis and locate the function invocations that are suitable for offloading. Next, we refactor the code of related object functions according to a specific program structure. Finally, we make offloading decisions referring to the context at runtime and send function invocations to multiple remote servers for execution. We evaluate the proposed FUNOff on two real-world applications. The results show that, compared with other approaches, FUNOff better supports the computation offloading of object-oriented applications in MEC, which reduces the response time by 10.7%-58.2%.
Keyword ：
code analysis code analysis computation offloading computation offloading Mobile edge computing Mobile edge computing object-oriented application object-oriented application software adaptation software adaptation
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| GB/T 7714 | Chen, Xing , Li, Ming , Zhong, Hao et al. FUNOff: Offloading Applications at Function Granularity for Mobile Edge Computing [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (2) : 1717-1734 . |
| MLA | Chen, Xing et al. "FUNOff: Offloading Applications at Function Granularity for Mobile Edge Computing" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 23 . 2 (2024) : 1717-1734 . |
| APA | Chen, Xing , Li, Ming , Zhong, Hao , Chen, Xiaona , Ma, Yun , Hsu, Ching-Hsien . FUNOff: Offloading Applications at Function Granularity for Mobile Edge Computing . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (2) , 1717-1734 . |
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为了更好地支持边缘计算服务提供商进行资源的提前配置与合理分配，负载预测被认为是边缘计算中的一项重要的技术支撑.传统的负载预测方法在面对具有明显趋势或规律性的负载时能取得良好的预测效果，但是它们无法有效地对边缘环境中高度变化的负载取得精确的预测.此外，这些方法通常将预测模型拟合到独立的时间序列上，进而进行单点负载实值预测.但是在实际边缘计算场景中，得到未来负载变化的概率分布情况会比直接预测未来负载的实值更具应用价值.为了解决上述问题，本文提出了一种基于深度自回归循环神经网络的边缘负载预测方法(Edge Load Prediction with Deep Auto-regressive Recurrent networks, ELP-DAR).所提出的ELP-DAR方法利用边缘负载时序数据训练深度自回归循环神经网络，将LSTM集成至S2S框架中，进而直接预测下一时间点负载概率分布的所有参数.因此，ELP-DAR方法能够高效地提取边缘负载的重要表征，学习复杂的边缘负载模式进而实现对高度变化的边缘负载精确的概率分布预测.基于真实的边缘负载数据集，通过大量仿真实验对所提出ELP-DAR方法的有效性进行了验证与分析.实验结果表明，相比于其他基准方法，所提出的ELP-DAR方法可以取得更高的预测精度，并且在不同预测长度下均展现出了优越的性能表现.
Keyword ：
循环神经网络 循环神经网络 概率分布 概率分布 深度自回归 深度自回归 负载预测 负载预测 边缘计算 边缘计算
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| GB/T 7714 | 陈礼贤 , 梁杰 , 黄一帆 et al. 基于深度自回归循环神经网络的边缘负载预测 [J]. | 小型微型计算机系统 , 2024 , 45 (02) : 359-366 . |
| MLA | 陈礼贤 et al. "基于深度自回归循环神经网络的边缘负载预测" . | 小型微型计算机系统 45 . 02 (2024) : 359-366 . |
| APA | 陈礼贤 , 梁杰 , 黄一帆 , 陈哲毅 , 于正欣 , 陈星 . 基于深度自回归循环神经网络的边缘负载预测 . | 小型微型计算机系统 , 2024 , 45 (02) , 359-366 . |
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针对不确定性云边协同环境下工作流应用调度问题，考虑服务器的负载压力、网络拥塞等计算环境因素造成计算性能和传输带宽的不稳定性，采用三角模糊数表示模糊云边协同环境中服务器的计算性能和传输带宽.对于泊松到达的多工作流应用，提出一种基于局部关键路径的多工作流应用调度策略，将局部关键路径作为调度单元进行统一调度，充分避免任务之间的数据传输，旨在满足多工作流应用截止日期约束的前提下，降低其模糊执行代价.仿真结果表明，与其他基准策略相比，在不同的截止时间约束下，该策略都能获得多工作流应用最优的可行调度方案，同时实现了模糊执行代价的有效优化.
Keyword ：
云边协同计算 云边协同计算 局部关键路径 局部关键路径 工作流应用调度 工作流应用调度 模糊不确定性 模糊不确定性
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| GB/T 7714 | 林潮伟 , 林兵 , 陈星 . 云边协同环境下基于局部关键路径的工作流应用调度策略 [J]. | 小型微型计算机系统 , 2024 , 45 (02) : 335-344 . |
| MLA | 林潮伟 et al. "云边协同环境下基于局部关键路径的工作流应用调度策略" . | 小型微型计算机系统 45 . 02 (2024) : 335-344 . |
| APA | 林潮伟 , 林兵 , 陈星 . 云边协同环境下基于局部关键路径的工作流应用调度策略 . | 小型微型计算机系统 , 2024 , 45 (02) , 335-344 . |
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Through deploying computing resources at the network edge, Mobile Edge Computing (MEC) alleviates the contradiction between the high requirements of intelligent mobile applications and the limited capacities of mobile End Devices (EDs) in smart communities. However, existing solutions of computation offloading and resource allocation commonly rely on prior knowledge or centralized decision-making, which cannot adapt to dynamic MEC environments with changeable system states and personalized user demands, resulting in degraded Quality-of-Service (QoS) and excessive system overheads. To address this important challenge, we propose a novel Personalized Federated deep Reinforcement learning based computation Offloading and resource Allocation method (PFR-OA). This innovative PFR-OA considers the personalized demands in smart communities when generating proper policies of computation offloading and resource allocation. To relieve the negative impact of local updates on global model convergence, we design a new proximal term to improve the manner of only optimizing local Q-value loss functions in classic reinforcement learning. Moreover, we develop a new partial-greedy based participant selection mechanism to reduce the complexity of federated aggregation while endowing sufficient exploration. Using real-world system settings and testbed, extensive experiments demonstrate the effectiveness of the PFR-OA. Compared to benchmark methods, the PFR-OA achieves better trade-offs between delay and energy consumption and higher task execution success rates under different scenarios. IEEE
Keyword ：
computation offloading computation offloading deep reinforcement learning deep reinforcement learning Mobile edge computing Mobile edge computing personalized federated learning personalized federated learning resource allocation resource allocation
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| GB/T 7714 | Chen, Z. , Xiong, B. , Chen, X. et al. Joint Computation Offloading and Resource Allocation in Multi-edge Smart Communities with Personalized Federated Deep Reinforcement Learning [J]. | IEEE Transactions on Mobile Computing , 2024 , 23 (12) : 1-16 . |
| MLA | Chen, Z. et al. "Joint Computation Offloading and Resource Allocation in Multi-edge Smart Communities with Personalized Federated Deep Reinforcement Learning" . | IEEE Transactions on Mobile Computing 23 . 12 (2024) : 1-16 . |
| APA | Chen, Z. , Xiong, B. , Chen, X. , Min, G. , Li, J. . Joint Computation Offloading and Resource Allocation in Multi-edge Smart Communities with Personalized Federated Deep Reinforcement Learning . | IEEE Transactions on Mobile Computing , 2024 , 23 (12) , 1-16 . |
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无人机(Unmanned Aerial Vehicles, UAVs)与多接入边缘计算(Multi-access Edge Computing, MEC)技术的结合突破了传统地面通信的局限性，已成为解决MEC中任务卸载问题的重要手段。由于单无人机可提供的计算资源和能量有限，为了应对日益扩大的网络规模，考虑了多无人机辅助MEC环境中的任务卸载问题。基于问题定义，任务卸载过程可以视为一个在平行链路上进行的、具有玩家特定延迟函数的Wardrop路由博弈，目的是得到均衡状态和最优状态下的卸载策略，并量化分析两者间的差距。由于均衡解难以计算，因此构造了一个新的势函数，将均衡问题转换成最小化势函数问题。同时使用Frank-Wolfe算法最终获得均衡和最优卸载策略。算法在每次迭代中将目标函数线性化，通过求解线性规划得到可行方向，进而沿此方向在可行域内作一维搜索。仿真实验表明，相比其他基准测试方法，基于平行链路Wardrop路由博弈的均衡卸载策略能够有效降低模型总成本，且与最优卸载策略下总成本的比值约为1。
Keyword ：
Frank-Wolfe算法 Frank-Wolfe算法 Wardrop路由博弈 Wardrop路由博弈 任务卸载 任务卸载 多接入边缘计算 多接入边缘计算 无人机 无人机
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| GB/T 7714 | 汪昕隆 , 林兵 , 陈星 . 多无人机辅助MEC环境中基于Wardrop路由博弈的计算卸载 [J]. | 计算机科学 , 2024 , 51 (03) : 309-316 . |
| MLA | 汪昕隆 et al. "多无人机辅助MEC环境中基于Wardrop路由博弈的计算卸载" . | 计算机科学 51 . 03 (2024) : 309-316 . |
| APA | 汪昕隆 , 林兵 , 陈星 . 多无人机辅助MEC环境中基于Wardrop路由博弈的计算卸载 . | 计算机科学 , 2024 , 51 (03) , 309-316 . |
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