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学者姓名：陈哲毅
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Abstract ：
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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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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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
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| 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 . |
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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
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| 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 . |
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In mobile edge computing (MEC) systems, unmanned aerial vehicles (UAVs) facilitate edge service providers (ESPs) offering flexible resource provisioning with broader communication coverage and thus improving the Quality of Service (QoS). However, dynamic system states and various traffic patterns seriously hinder efficient cooperation among UAVs. Existing solutions commonly rely on prior system knowledge or complex neural network models, lacking adaptability and causing excessive overheads. To address these critical challenges, we propose the DisOff, a novel profit-aware cooperative offloading framework in UAV-enabled MEC with lightweight deep reinforcement learning (DRL). First, we design an improved DRL with twin critic-networks and delay mechanism, which solves the $Q$ -value overestimation and high variance and thus approximates the optimal UAV cooperative offloading and resource allocation. Next, we develop a new multiteacher distillation mechanism for the proposed DRL model, where the policies of multiple UAVs are integrated into one DRL agent, compressing the model size while maintaining superior performance. Using the real-world datasets of user traffic, extensive experiments are conducted to validate the effectiveness of the proposed DisOff. Compared to benchmark methods, the DisOff enhances ESP profits while reducing the DRL model size and training costs.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Computational modeling Computational modeling Computation offloading Computation offloading deep reinforcement learning (DRL) deep reinforcement learning (DRL) Internet of Things Internet of Things mobile edge computing (MEC) mobile edge computing (MEC) model compression model compression Optimization Optimization Quality of service Quality of service Resource management Resource management Training Training unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
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| GB/T 7714 | Chen, Zheyi , Zhang, Junjie , Zheng, Xianghan et al. Profit-Aware Cooperative Offloading in UAV-Enabled MEC Systems Using Lightweight Deep Reinforcement Learning [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) : 21325-21336 . |
| MLA | Chen, Zheyi et al. "Profit-Aware Cooperative Offloading in UAV-Enabled MEC Systems Using Lightweight Deep Reinforcement Learning" . | IEEE INTERNET OF THINGS JOURNAL 11 . 12 (2024) : 21325-21336 . |
| APA | Chen, Zheyi , Zhang, Junjie , Zheng, Xianghan , Min, Geyong , Li, Jie , Rong, Chunming . Profit-Aware Cooperative Offloading in UAV-Enabled MEC Systems Using Lightweight Deep Reinforcement Learning . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) , 21325-21336 . |
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With flexible mobility and broad communication coverage, unmanned aerial vehicles (UAVs) have become an important extension of multiaccess edge computing (MEC) systems, exhibiting great potential for improving the performance of federated graph learning (FGL). However, due to the limited computing and storage resources of UAVs, they may not well handle the redundant data and complex models, causing the inference inefficiency of FGL in UAV-assisted MEC systems. To address this critical challenge, we propose a novel LightWeight FGL framework, named LW-FGL, to accelerate the inference speed of classification models in UAV-assisted MEC systems. Specifically, we first design an adaptive information bottleneck (IB) principle, which enables UAVs to obtain well-compressed worthy subgraphs by filtering out the information that is irrelevant to downstream classification tasks. Next, we develop improved tiny graph neural networks (GNNs), which are used as the inference models on UAVs, thus reducing the computational complexity and redundancy. Using real-world graph data sets, extensive experiments are conducted to validate the effectiveness of the proposed LW-FGL. The results show that the LW-FGL achieves higher classification accuracy and faster inference speed than state-of-the-art methods.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Biological system modeling Biological system modeling Classification inference Classification inference Computational modeling Computational modeling Data models Data models federated graph learning (FGL) federated graph learning (FGL) Graph neural networks Graph neural networks lightweight model lightweight model multiaccess edge computing (MEC) multiaccess edge computing (MEC) Task analysis Task analysis Training Training unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
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| GB/T 7714 | Zhong, Luying , Chen, Zheyi , Cheng, Hongju et al. Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) : 21180-21190 . |
| MLA | Zhong, Luying et al. "Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems" . | IEEE INTERNET OF THINGS JOURNAL 11 . 12 (2024) : 21180-21190 . |
| APA | Zhong, Luying , Chen, Zheyi , Cheng, Hongju , Li, Jie . Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) , 21180-21190 . |
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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.
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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在边缘计算中,为缓解移动设备计算能力、存储容量受限问题,通常将部分计算密集型任务卸载至边缘服务器.然而,由于移动设备计算能力的差异,无法为所有的移动设备制定统一的卸载方案.若对每个设备均单独进行训练,则无法满足时延需求.针对这一问题,本文提出了一种差异化设备上基于联邦深度强化学习的任务卸载方法.该方法使用环境内已有移动设备的卸载经验,结合深度Q网络和联邦学习框架,构建了 一个全局模型.随后,使用新移动设备上少量经验在全局模型上微调以构建个人模型.基于多种场景的大量实验,将本文所提出方法与理想方案、Naive、全局模型和Rule-based算法进行对比.实验结果验证了本文所提出方法针对差异化设备任务卸载问题的有效性,能在花费较短时延的同时得到接近理想方案的卸载方案.
Keyword ：
任务卸载 任务卸载 依赖感知 依赖感知 深度强化学习 深度强化学习 联邦学习 联邦学习 边缘计算 边缘计算
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| GB/T 7714 | 余楚佳 , 胡晟熙 , 林欣郁 et al. 针对差异化设备的任务卸载方法 [J]. | 小型微型计算机系统 , 2024 , 45 (8) : 1816-1824 . |
| MLA | 余楚佳 et al. "针对差异化设备的任务卸载方法" . | 小型微型计算机系统 45 . 8 (2024) : 1816-1824 . |
| APA | 余楚佳 , 胡晟熙 , 林欣郁 , 陈哲毅 , 陈星 . 针对差异化设备的任务卸载方法 . | 小型微型计算机系统 , 2024 , 45 (8) , 1816-1824 . |
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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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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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