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学者姓名：陈哲毅
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Abstract ：
针对车辆移动过程中服务质量(QoS)下降的问题,提出了一种基于凸优化使能深度强化学习的服务迁移(service migration via convex-optimization-enabled deep reinforcement learning,SeMiR)方法.将优化问题分解为两个子问题并分别求解;针对服务迁移子问题,设计了一种基于改进深度强化学习的服务迁移方法,以探索最优迁移策略;针对资源分配子问题,设计了 一种基于凸优化的资源分配方法,以推导给定迁移决策下每台MEC服务器的最优资源分配,提升服务迁移的性能.实验结果表明:与基准方法相比,SeMiR方法能够有效提升车辆的QoS,在各种场景下均展现出更加优越的性能.
Keyword ：
凸优化 凸优化 服务迁移 服务迁移 深度强化学习 深度强化学习 移动边缘计算 移动边缘计算 资源分配 资源分配 车联网 车联网
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| GB/T 7714 | 黄思进 , 文佳 , 陈哲毅 . 面向边缘车联网系统的智能服务迁移方法 [J]. | 系统仿真学报 , 2025 , 37 (2) : 379-391 . |
| MLA | 黄思进 等. "面向边缘车联网系统的智能服务迁移方法" . | 系统仿真学报 37 . 2 (2025) : 379-391 . |
| APA | 黄思进 , 文佳 , 陈哲毅 . 面向边缘车联网系统的智能服务迁移方法 . | 系统仿真学报 , 2025 , 37 (2) , 379-391 . |
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在大规模物联网(internet-of-things,IoT)系统中,无人机使能的移动边缘计算(mobile edge computing,MEC)可缓解终端IoT设备的性能限制.然而,由于不均匀的IoT设备分布与低效的问题求解效率,如何在大规模IoT系统中高效执行计算卸载面临着巨大的挑战.现有解决方案通常无法适应动态多变的多无人机场景,导致了低效的资源利用与过度的响应延迟.为解决这些重要挑战,提出了一种新型的面向大规模IoT系统的多无人机部署与协作卸载(multi-UAV deployment and collaborative offloading,MUCO)方法.设计了一种基于约束K-Means聚类的无人机部署方案,在提升服务覆盖率的同时保证覆盖均衡.设计了一种基于多智能体强化学习(multi-agent reinforcement learning,MARL)的多无人机协作卸载策略,将来自IoT设备的卸载请求进行拆分与分布式执行,进而实现高效的协作卸载.大量仿真实验验证了 MUCO方法的有效性.与基准方法相比,MUCO方法在不同场景中平均可以取得约23.82％和28.13％的无人机部署性能提升,且能取得更低的时延和能耗.
Keyword ：
K-Means聚类 K-Means聚类 多智能体强化学习 多智能体强化学习 无人机部署 无人机部署 物联网 物联网 移动边缘计算 移动边缘计算 计算卸载 计算卸载
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| GB/T 7714 | 黄智钦 , 卢恬英 , 陈哲毅 . 面向大规模IoT系统的多无人机部署与协作卸载 [J]. | 系统仿真学报 , 2025 , 37 (1) : 25-39 . |
| MLA | 黄智钦 等. "面向大规模IoT系统的多无人机部署与协作卸载" . | 系统仿真学报 37 . 1 (2025) : 25-39 . |
| APA | 黄智钦 , 卢恬英 , 陈哲毅 . 面向大规模IoT系统的多无人机部署与协作卸载 . | 系统仿真学报 , 2025 , 37 (1) , 25-39 . |
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To address the problem of QoS degradation during the vehicle movement, a novel service migration via convex-optimization-enabled deep reinforcement learning (SeMiR) method is proposed. The optimization problem is decomposed into two sub-problems and solved separately. For the service migration sub-problem, an improved deep reinforcement learning based service migration method is designed to explore the optimal migration policy. For the resource allocation sub-problem, a convex optimization based resource allocation method is developed to derive the optimal resource allocation for each MEC server under the given migration decisions, thereby improving the performance of service migration. Experimental results show that the SeMiR method can achieve better QoS and superior service migration performance than benchmark methods under various scenarios. © 2025 Acta Simulata Systematica Sinica. All rights reserved.
Keyword ：
convex optimization convex optimization DRL DRL Internet-of-Vehicles Internet-of-Vehicles MEC MEC resource allocation resource allocation service migration service migration
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| GB/T 7714 | Huang, S. , Wen, J. , Chen, Z. . Intelligent Service Migration towards MEC-based IoV Systems; [面向边缘车联网系统的智能服务迁移方法] [J]. | Journal of System Simulation , 2025 , 37 (2) : 379-391 . |
| MLA | Huang, S. 等. "Intelligent Service Migration towards MEC-based IoV Systems; [面向边缘车联网系统的智能服务迁移方法]" . | Journal of System Simulation 37 . 2 (2025) : 379-391 . |
| APA | Huang, S. , Wen, J. , Chen, Z. . Intelligent Service Migration towards MEC-based IoV Systems; [面向边缘车联网系统的智能服务迁移方法] . | Journal of System Simulation , 2025 , 37 (2) , 379-391 . |
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In large-scale internet-of-things (IoT) systems, unmanned aerial vehicles (UAV) enabled mobile edge computing (MEC) can alleviate the performance constraints on end IoT devices. However, due to the uneven distribution of IoT devices and inefficient problem-solving, how to efficiently perform computation offloading in large-scale IoT systems is a major challenge. Existing solutions generally cannot fit into dynamic multi-UAV scenarios, causing inefficient resource utilization and excessive response delay. To address these important challenges, this paper proposes a novel multi-UAV deployment and collaborative offloading (MUCO) method for large-scale IoT systems. A UAV deployment scheme based on constrained K-Means clustering is designed to enhance service coverage while ensuring balanced coverage. A multi-UAV collaborative offloading strategy based on multi-agent reinforcement learning (MARL) is developed to split the offloading requests from IoT devices and conduct distributed execution, thereby realizing efficient collaborative offloading. Extensive simulation experiments validate the effectiveness of the proposed MUCO method. Compared to benchmark methods, the MUCO method can achieve an average improvement of about 23.82% and 28.13% improvement in UAV deployment performance in different scenarios, and can achieve lower latency and energy consumption. © 2025 Acta Simulata Systematica Sinica. All rights reserved.
Keyword ：
computation offloading computation offloading internet-of-things internet-of-things K-Means clustering K-Means clustering mobile edge computing mobile edge computing multi-agent reinforcement learning multi-agent reinforcement learning UAV deployment UAV deployment
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| GB/T 7714 | Huang, Z. , Lu, T. , Chen, Z. . Multi-UAV Deployment and Collaborative Offloading for Large-scale IoT Systems; [面向大规模 IoT 系统的多无人机部署与协作卸载] [J]. | Journal of System Simulation , 2025 , 37 (1) : 25-39 . |
| MLA | Huang, Z. 等. "Multi-UAV Deployment and Collaborative Offloading for Large-scale IoT Systems; [面向大规模 IoT 系统的多无人机部署与协作卸载]" . | Journal of System Simulation 37 . 1 (2025) : 25-39 . |
| APA | Huang, Z. , Lu, T. , Chen, Z. . Multi-UAV Deployment and Collaborative Offloading for Large-scale IoT Systems; [面向大规模 IoT 系统的多无人机部署与协作卸载] . | Journal of System Simulation , 2025 , 37 (1) , 25-39 . |
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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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As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies.
Keyword ：
cache space partitioning cache space partitioning content popularity prediction content popularity prediction Multi-edge collaborative caching Multi-edge collaborative caching proactive cache replacement proactive cache replacement robust federated deep learning robust federated deep learning
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| GB/T 7714 | Chen, Zheyi , Liang, Jie , Yu, Zhengxin et al. Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 33 (2) : 654-669 . |
| MLA | Chen, Zheyi et al. "Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning" . | IEEE-ACM TRANSACTIONS ON NETWORKING 33 . 2 (2024) : 654-669 . |
| APA | Chen, Zheyi , Liang, Jie , Yu, Zhengxin , Cheng, Hongju , Min, Geyong , Li, Jie . Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 33 (2) , 654-669 . |
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The emerging Graph Convolutional Networks (GCNs) have attracted widespread attention in graph learning, due to their good ability of aggregating the information between higher-order neighbors. However, real-world graph data contains high noise and redundancy, making it hard for GCNs to accurately depict the complete relationships between nodes, which seriously degrades the quality of graph representations. Moreover, existing studies commonly ignore the distribution difference between feature and semantic spaces in graphs, causing inferior model generalization. To address these challenges, we propose DIB-RGCN, a novel robust GCN framework, to explore the optimal graph representation with the guidance of the well-designed dual information bottleneck principle. First, we analyze the reasons for distribution differences and theoretically prove that minimal sufficient representations in specific spaces cannot promise optimal performance for downstream tasks. Next, we design new dual channels to regularize feature and semantic spaces, eliminating the sharing of task-irrelevant information between spaces. Different from existing denoising algorithms that adopt a random dropping manner, we innovatively replace potential noisy features and edges with local neighboring representations. This design lowers edge-specific coefficient assignment, alleviating the interference of original representations while retaining graph structures. Further, we maximize the sharing of task-relevant information between feature and semantic spaces to alleviate the difference between them. Using real-world datasets, extensive experiments demonstrate the robustness of the proposed DIB-RGCN, which outperforms state-of-the-art methods on classification tasks.
Keyword ：
Graph Neural Networks Graph Neural Networks Information Theory Information Theory Robustness Robustness Semi-supervised Learning Semi-supervised Learning
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| GB/T 7714 | Zhong, Luying , Lin, Renjie , Li, Jiayin et al. Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information Theory Perspective [J]. | PROCEEDINGS OF THE 30TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2024 , 2024 : 4571-4582 . |
| MLA | Zhong, Luying et al. "Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information Theory Perspective" . | PROCEEDINGS OF THE 30TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2024 (2024) : 4571-4582 . |
| APA | Zhong, Luying , Lin, Renjie , Li, Jiayin , Wang, Shiping , Chen, Zheyi . Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information Theory Perspective . | PROCEEDINGS OF THE 30TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2024 , 2024 , 4571-4582 . |
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作为移动边缘计算(Mobile Edge Computing,MEC)的一项重要技术支撑,多边缘协作缓存的出现可更好满足终端智能应用的实时计算与数据存储需求进而提升用户体验.但是,多边缘协作缓存的性能通常受限于低效率的协作机制以及不合理的缓存资源配置策略.同时,如何在离散的用户特征分布与多样化的内容库之中寻找其潜在关联以提升缓存命中率仍是一个巨大的挑战.为了解决上述重要挑战,本文提出了一种新颖的基于联邦深度学习的多边缘协作缓存(Multi-edge Collaborative Cac-hing with Federated deep learning,M2CF)方法.在M2CF中,首先设计了一种新型的多维缓存空间划分机制,对MEC节点的缓存空间进行感知优化,使得用户在分类区间可获得精准的内容推荐.接着,设计了一种基于VQ-VAE的内容流行度预测算法,解决了后验坍塌问题并提高了区间用户内容流行度预测的准确性.最后,设计了一种基于联邦深度学习的模型训练与缓存替换策略,通过聚合各MEC节点的本地模型以生成全局共享模型,进而更好适应优化后的不同缓存资源配置,提升多边缘协作缓存的命中率.基于MovieLens电影评分真实数据集,本文在测试床上展开了大量对比实验对所提出的M2CF方法进行了全面的评估.实验结果表明,M2CF与其他缓存方法对比展现出了更优秀的缓存性能与时效性能,且可以适应更为复杂的多边缘场景.
Keyword ：
内容流行度预测 内容流行度预测 多维缓存空间划分 多维缓存空间划分 多边缘协作缓存 多边缘协作缓存 移动边缘计算 移动边缘计算 联邦深度学习 联邦深度学习
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| GB/T 7714 | 梁杰 , 郑家瑜 , 陈哲毅 et al. 基于联邦深度学习的多边缘协作缓存方法 [J]. | 小型微型计算机系统 , 2024 , 45 (12) : 2994-3001 . |
| MLA | 梁杰 et al. "基于联邦深度学习的多边缘协作缓存方法" . | 小型微型计算机系统 45 . 12 (2024) : 2994-3001 . |
| APA | 梁杰 , 郑家瑜 , 陈哲毅 , 于正欣 , 苗旺 . 基于联邦深度学习的多边缘协作缓存方法 . | 小型微型计算机系统 , 2024 , 45 (12) , 2994-3001 . |
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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
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| 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 . |
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5G网络切片与计算卸载技术的出现,有望支持移动边缘计算(Mobile Edge Computing,MEC)系统在降低服务延迟的同时提高资源利用率,进而更好地满足不同用户的需求.然而,由于MEC系统状态的动态性与用户需求的多变性,如何有效结合网络切片与计算卸载技术仍面临着巨大的挑战.现有解决方案通常依赖于静态网络资源划分或系统先验知识,无法适应动态多变的MEC环境,造成了过度的服务延时与不合理的资源供给.为解决上述重要挑战,本文提出了一种MEC环境中面向5G网络切片的计算卸载(Computation Offloading towards Network Slicing,CONS)方法.首先,基于对历史用户请求的分析,设计了一种门控循环神经网络对未来时隙的用户请求数量进行精确预测,结合用户资源需求对网络切片进行动态调整.接着,基于网络切片资源划分的结果,设计了一种双延迟深度强化学习对计算卸载与资源分配进行决策,通过解决Q值过高估计和高方差问题,进而有效逼近动态MEC环境下的最优策略.基于真实用户通信流量数据集,大量仿真实验验证了所提的CONS方法的可行性和有效性.与其他5种基准方法相比,CONS方法能够有效地提高服务提供商的收益,且在不同场景下均展现出了更加优越的性能.
Keyword ：
深度强化学习 深度强化学习 移动边缘计算 移动边缘计算 网络切片 网络切片 计算卸载 计算卸载 资源分配 资源分配
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| GB/T 7714 | 张俊杰 , 王鹏飞 , 陈哲毅 et al. MEC环境中面向5G网络切片的计算卸载方法 [J]. | 小型微型计算机系统 , 2024 , 45 (9) : 2285-2293 . |
| MLA | 张俊杰 et al. "MEC环境中面向5G网络切片的计算卸载方法" . | 小型微型计算机系统 45 . 9 (2024) : 2285-2293 . |
| APA | 张俊杰 , 王鹏飞 , 陈哲毅 , 于正欣 , 苗旺 . MEC环境中面向5G网络切片的计算卸载方法 . | 小型微型计算机系统 , 2024 , 45 (9) , 2285-2293 . |
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