Query：
学者姓名：陈星
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
As a promising technique for offloading computation tasks from mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) utilizes UAVs as computational resources. A popular method for enhancing the quality of service (QoS) of UAV-assisted MEC systems is to jointly optimize UAV deployment and computation task offloading. This imposes the challenge of dynamically adjusting UAV deployment and computation offloading to accommodate the changing positions and computational requirements of mobile devices. Due to the real-time requirements of MEC computation tasks, finding an efficient joint optimization approach is imperative. This paper proposes an algorithm aimed at minimizing the average response delay in a UAV-assisted MEC system. The approach revolves around the joint optimization of UAV deployment and computation offloading through convex optimization. We break down the problem into three sub-problems: UAV deployment, Ground Device (GD) access, and computation tasks offloading, which we address using the block coordinate descent algorithm. Observing the $NP$NP-hardness nature of the original problem, we present near-optimal solutions to the decomposed sub-problems. Simulation results demonstrate that our approach can generate a joint optimization solution within seconds and diminish the average response delay compared to state-of-the-art algorithms and other advanced algorithms, with improvements ranging from 4.70% to 42.94%.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Block coordinate descent Block coordinate descent Cloud computing Cloud computing computation offloading computation offloading Computer architecture Computer architecture Delays Delays Heuristic algorithms Heuristic algorithms mobile edge computing mobile edge computing Mobile handsets Mobile handsets Multi-access edge computing Multi-access edge computing Optimization Optimization Relays Relays Servers Servers unmanned aerial vehicle deployment unmanned aerial vehicle deployment
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Jianshan , Luo, Haibo , Chen, Xing et al. Minimizing Response Delay in UAV-Assisted Mobile Edge Computing by Joint UAV Deployment and Computation Offloading [J]. | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2024 , 12 (4) : 1372-1386 . |
| MLA | Zhang, Jianshan et al. "Minimizing Response Delay in UAV-Assisted Mobile Edge Computing by Joint UAV Deployment and Computation Offloading" . | IEEE TRANSACTIONS ON CLOUD COMPUTING 12 . 4 (2024) : 1372-1386 . |
| APA | Zhang, Jianshan , Luo, Haibo , Chen, Xing , Shen, Hong , Guo, Longkun . Minimizing Response Delay in UAV-Assisted Mobile Edge Computing by Joint UAV Deployment and Computation Offloading . | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2024 , 12 (4) , 1372-1386 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Due to the complicated data dependencies between the tasks in a workflow application and the heterogeneous resources in edge-cloud environments, it is difficult to select an optimal tasks-servers solution for scheduling workflow applications in the complex environments. Current research on workflow applications scheduling is mainly concentrated on certain conditions, ignoring the fact that the scheduling environments usually fluctuate. In this article, we deal with reducing the execution cost of multiple workflow applications within the corresponding deadline constraints and improving the network robustness in fuzzy edge-cloud environments. Triangular Fuzzy Numbers (TFNs) are employed to describe the computing capacity of servers and the bandwidth between them in uncertain environments. Specially, a novel Scheduling Strategy based on Particle Swarm Optimization algorithm employing the Quadratic Penalty Function (SSPSO_QPF) is proposed for scheduling multiple workflow applications. Compared with other classic scheduling strategies, simulation results demonstrate that the proposed strategy can generate feasible scheduling schemes even with the strict deadline constraints, and significantly reduce the fuzzy execution cost of multiple workflow applications.
Keyword ：
Cloud computing Cloud computing Complex network robustness Complex network robustness Costs Costs Data communication Data communication Edge-cloud environments Edge-cloud environments Multi-constraint combinatorial optimization Multi-constraint combinatorial optimization Processor scheduling Processor scheduling Scheduling Scheduling Servers Servers Task analysis Task analysis Workflow scheduling Workflow scheduling
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Xing , Lin, Chaowei , Lin, Bing . An Intelligent Workflow Scheduling Scheme for Complex Network Robustness in Fuzzy Edge-Cloud Environments [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (1) : 1106-1123 . |
| MLA | Chen, Xing et al. "An Intelligent Workflow Scheduling Scheme for Complex Network Robustness in Fuzzy Edge-Cloud Environments" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 11 . 1 (2024) : 1106-1123 . |
| APA | Chen, Xing , Lin, Chaowei , Lin, Bing . An Intelligent Workflow Scheduling Scheme for Complex Network Robustness in Fuzzy Edge-Cloud Environments . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (1) , 1106-1123 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.
Keyword ：
Edge intelligence Edge intelligence federated graph learning federated graph learning neighbor generation neighbor generation semi-supervised learning semi-supervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhong, Luying , Pi, Yueyang , Chen, Zheyi et al. SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation [J]. | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 : 1141-1150 . |
| MLA | Zhong, Luying et al. "SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation" . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (2024) : 1141-1150 . |
| APA | Zhong, Luying , Pi, Yueyang , Chen, Zheyi , Yu, Zhengxin , Miao, Wang , Chen, Xing et al. SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 , 1141-1150 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
在这篇文章中,针对分片区块链(Sharded Blockchain)系统性能优化问题,提出了一种结合粒子群和遗传算法的系统性能优化方法(PSO-GA),目的是为了在尽可能满足当前网络环境情况下,提升其系统吞吐量.该方法考虑分片区块链中节点的计算能力、恶意节点的概率以及节点之间的传输速率等不同网络环境下,找到响应网络状态的最佳分片区块链系统参数;为了避免传统粒子群优化算法陷入局部最优的问题,引入遗传算法中的交叉操作和变异操作,有效提高方法的准确性.通过大量仿真实验对方法的有效性进行验证分析.实验结果表明,相比于其他的方法,本文所提出的方法可以在更短的时间取得更高的系统吞吐量.
Keyword ：
分片区块链 分片区块链 可扩展性 可扩展性 粒子群算法 粒子群算法 遗传算法 遗传算法
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 蒋腾聪 , 张建山 , 郑鸿强 et al. 基于PSO-GA的分片区块链系统性能优化方法 [J]. | 小型微型计算机系统 , 2024 , 45 (7) : 1756-1762 . |
| MLA | 蒋腾聪 et al. "基于PSO-GA的分片区块链系统性能优化方法" . | 小型微型计算机系统 45 . 7 (2024) : 1756-1762 . |
| APA | 蒋腾聪 , 张建山 , 郑鸿强 , 陈星 . 基于PSO-GA的分片区块链系统性能优化方法 . | 小型微型计算机系统 , 2024 , 45 (7) , 1756-1762 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
As an effective technique to relieve the problem of resource constraints on mobile devices (MDs), the computation offloading utilizes powerful cloud and edge resources to process the computation-intensive tasks of mobile applications uploaded from MDs. In cloud-edge computing, the resources (e.g., cloud and edge servers) that can be accessed by mobile applications may change dynamically. Meanwhile, the parallel tasks in mobile applications may lead to the huge solution space of offloading decisions. Therefore, it is challenging to determine proper offloading plans in response to such high dynamics and complexity in cloud-edge environments. The existing studies often preset the priority of parallel tasks to simplify the solution space of offloading decisions, and thus the proper offloading plans cannot be found in many cases. To address this challenge, we propose a novel real-time and Dependency-aware task Offloading method with Deep Q-networks (DODQ) in cloud-edge computing. In DODQ, mobile applications are first modeled as Directed Acyclic Graphs (DAGs). Next, the Deep Q-Networks (DQN) is customized to train the decision-making model of task offloading, aiming to quickly complete the decision-making process and generate new offloading plans when the environments change, which considers the parallelism of tasks without presetting the task priority when scheduling tasks. Simulation results show that the DODQ can well adapt to different environments and efficiently make offloading decisions. Moreover, the DODQ outperforms the state-of-art methods and quickly reaches the optimal/near-optimal performance.
Keyword ：
Cloud computing Cloud computing Cloud-edge computing Cloud-edge computing Computational modeling Computational modeling deep reinforcement learning deep reinforcement learning dependent and parallel tasks dependent and parallel tasks Heuristic algorithms Heuristic algorithms Mobile applications Mobile applications real-time offloading real-time offloading Real-time systems Real-time systems Servers Servers Task analysis Task analysis
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Xing , Hu, Shengxi , Yu, Chujia et al. Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning [J]. | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2024 , 35 (3) : 391-404 . |
| MLA | Chen, Xing et al. "Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning" . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 35 . 3 (2024) : 391-404 . |
| APA | Chen, Xing , Hu, Shengxi , Yu, Chujia , Chen, Zheyi , Min, Geyong . Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2024 , 35 (3) , 391-404 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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.
Keyword ：
computation offloading computation offloading deep reinforcement learning deep reinforcement learning Delays Delays Mobile edge computing Mobile edge computing personalized federated learning personalized federated learning Quality of service Quality of service resource allocation resource allocation Resource management Resource management Servers Servers Smart cities Smart cities Task analysis Task analysis Training Training
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Zheyi , Xiong, Bing , Chen, Xing 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) : 11604-11619 . |
| MLA | Chen, Zheyi 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) : 11604-11619 . |
| APA | Chen, Zheyi , Xiong, Bing , Chen, Xing , Min, Geyong , Li, Jie . 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) , 11604-11619 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
