IEEE Transactions on Mobile Computing最新文献_第10页

Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-Enabled Multi-Agent Reinforcement Learning 基于动态切换的多智能体强化学习的多auv协同水下多目标跟踪

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3521889

Shengbo Wang;Chuan Lin;Guangjie Han;Shengchao Zhu;Zhixian Li;Zhenyu Wang;Yunpeng Ma

{"title":"Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-Enabled Multi-Agent Reinforcement Learning","authors":"Shengbo Wang;Chuan Lin;Guangjie Han;Shengchao Zhu;Zhixian Li;Zhenyu Wang;Yunpeng Ma","doi":"10.1109/TMC.2024.3521889","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521889","url":null,"abstract":"In recent years, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration or underwater tracking, etc. In this paper, we propose a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account. We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process. Then, based on software-defined networking (SDN), we regard the AUV swarm as a underwater ad-hoc network and propose a hierarchical software-defined multi-AUV reinforcement learning (HSARL) architecture. Based on the proposed HSARL architecture, we propose the “Dynamic-Switching” mechanism, it includes “Dynamic-Switching Attention” and “Dynamic-Switching Resampling” mechanisms which accelerate the HSARL algorithm's convergence speed and effectively prevents it from getting stuck in a local optimum state. Additionally, we introduce the reward reshaping mechanism for further accelerating the convergence speed of the proposed HSARL algorithm in early phase. Finally, based on a proposed AUV classification method, we propose a cooperative tracking algorithm called <bold>Dynamic-<bold>Switching-<bold>Based <bold>MARL (DSBM)-driven tracking algorithm. Evaluation results demonstrate that our proposed DSBM tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of various important metrics.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4296-4311"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability-Optimal UAV-Assisted Mobile Edge Computing: Joint Resource Allocation, Data Transmission Scheduling and Motion Control 可靠性优化的无人机辅助移动边缘计算：联合资源分配、数据传输调度和运动控制

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3521934

Jianshan Zhou;Mingqian Wang;Daxin Tian;Kaige Qu;Guixian Qu;Xuting Duan;Xuemin Shen

{"title":"Reliability-Optimal UAV-Assisted Mobile Edge Computing: Joint Resource Allocation, Data Transmission Scheduling and Motion Control","authors":"Jianshan Zhou;Mingqian Wang;Daxin Tian;Kaige Qu;Guixian Qu;Xuting Duan;Xuemin Shen","doi":"10.1109/TMC.2024.3521934","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521934","url":null,"abstract":"Uncrewed aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC) within space-air-ground integrated networks. They serve as aerial cloudlets, enabling task processing in close proximity to ground users. While numerous joint trajectory design and resource allocation schemes aim to enhance energy efficiency or computation rate, few focus on improving system reliability, which is often challenged by stochastic channels and node mobility. This paper presents a stochastic modeling perspective to derive a system reliability expression. Our reliability formulation incorporates the impacts of stochastic Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) air-to-ground communication channels, application data load, available bandwidth, offloading time, and transmission power. This comprehensive approach leads to a reliability-oriented joint optimization model that considers not only resource allocation and user data transmission scheduling but also the motion of UAVs. To solve this problem, we propose a low-complexity algorithm. By utilizing augmented Lagrangian multipliers, the algorithm transforms nonlinear constraints into a tractable formulation, enabling the utilization of legacy unconstrained optimization techniques. We provide a proof of convergence for this algorithm. Through simulations, we demonstrate that our proposed method guarantees convergence within finite iterations and improves the average communication reliability in comparison with several other joint optimization schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4217-4234"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Noise-Robust Federated Learning With Model Heterogeneous Clients 模型异构客户端的噪声鲁棒联邦学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3522573

Xiuwen Fang;Mang Ye

{"title":"Noise-Robust Federated Learning With Model Heterogeneous Clients","authors":"Xiuwen Fang;Mang Ye","doi":"10.1109/TMC.2024.3522573","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522573","url":null,"abstract":"Federated Learning (FL) enables multiple devices to collaboratively train models without sharing their raw data. Considering that clients may prefer to design their own models independently, model heterogeneous FL has emerged. Additionally, due to the annotation uncertainty, the collected data usually contain unavoidable and varying noise, which cannot be effectively addressed by existing FL algorithms. This paper presents a novel solution that simultaneously handles model heterogeneity and label noise in a single framework. It is featured in three aspects: (1) For the communication between heterogeneous models, we directly align the model feedback by utilizing the easily-accessible public data, which does not require additional global models or relevant data for collaboration. (2) For internal label noise in each client, we design a dynamic label refinement strategy to mitigate the negative effects. (3) For challenging noisy feedback from other participants, we design an enhanced client confidence re-weighting scheme, which adaptively assigns corresponding weights to each client in the collaborative learning stage. Extensive experiments validate the effectiveness of our approach in mitigating the negative effects of various noise rates and types under both model homogeneous and heterogeneous FL settings.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4053-4071"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FairSTG: Countering Performance Heterogeneity via Collaborative Sample-Level Optimization FairSTG：通过协作样本级优化来对抗性能异质性

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3522476

Gengyu Lin;Zhengyang Zhou;Qihe Huang;Kuo Yang;Shifen Cheng;Yang Wang

{"title":"FairSTG: Countering Performance Heterogeneity via Collaborative Sample-Level Optimization","authors":"Gengyu Lin;Zhengyang Zhou;Qihe Huang;Kuo Yang;Shifen Cheng;Yang Wang","doi":"10.1109/TMC.2024.3522476","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522476","url":null,"abstract":"Spatiotemporal learning plays a crucial role in mobile computing techniques to empower smart cites. While existing research has made great efforts to achieve accurate predictions on the overall dataset, they still neglect the significant performance heterogeneity across samples. In this work, we designate the performance heterogeneity as the reason for unfair spatiotemporal learning, which not only degrades the practical functions of models, but also brings serious potential risks to real-world urban applications. To fix this gap, we propose a model-independent Fairness-aware framework for SpatioTemporal Graph learning (FairSTG), which inherits the idea of exploiting advantages of well-learned samples to challenging ones with collaborative mix-up. Specifically, FairSTG consists of a spatiotemporal feature extractor for model initialization, a collaborative representation enhancement for knowledge transfer between well-learned samples and challenging ones, and fairness objectives for immediately suppressing sample-level performance heterogeneity. Experiments on four spatiotemporal datasets demonstrate that our FairSTG significantly improves the fairness quality while maintaining comparable forecasting accuracy. Case studies show FairSTG can counter both spatial and temporal performance heterogeneity by our sample-level retrieval and compensation, and our work can potentially alleviate the risks on spatiotemporal resource allocation for underrepresented urban regions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4153-4168"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-Factor Authentication Based on Acoustic Fingerprinting in Modulation Domain 基于调制域声指纹的双因素认证

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-24 DOI: 10.1109/TMC.2024.3522077

Yanzhi Ren;Tingyuan Yang;Yufei Zhou;Hongbo Liu;Jiadi Yu;Haomiao Yang;Hongwei Li

{"title":"Two-Factor Authentication Based on Acoustic Fingerprinting in Modulation Domain","authors":"Yanzhi Ren;Tingyuan Yang;Yufei Zhou;Hongbo Liu;Jiadi Yu;Haomiao Yang;Hongwei Li","doi":"10.1109/TMC.2024.3522077","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522077","url":null,"abstract":"The two-factor authentication (2FA) has been increasingly used with the popularity of mobile devices. Currently, many existing 2FA schemes extract the devices’ acoustic fingerprints as the second factor. Nevertheless, they mainly consider deriving fingerprints from the raw acoustic waveforms for authentication, which are susceptible to the fingerprint variations caused by the environmental noise or the varying distance between devices. To address these vulnerabilities, we propose a robust system utilizing the distortions of modulated signals, which are incurred by the acoustic elements of mobile devices, as the proof for 2FA. Specifically, our system first designs a channel delay estimation scheme to accurately estimate the propagation delay from the speaker to the microphone by deriving the phase change of the received sinusoidal signal. To perform a robust authentication, we design a new acoustic fingerprinting scheme to remove the impacts of the varying distance and environmental noise from the demodulated PSK signals for fingerprint extraction. Moreover, our device authentication component designs a transfer learning-based scheme to capture the subtle differences in devices’ fingerprints for accurate device authentication. To the best of our knowledge, this is the first 2FA system that could extract acoustic fingerprints in modulation domain and can effectively withstand the impacts of channel distortions. We also confirm the accuracy and security of our system through extensive user experiments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4235-4247"},"PeriodicalIF":7.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial Internet of Things With Large Language Models (LLMs): An Intelligence-Based Reinforcement Learning Approach 具有大型语言模型的工业物联网：基于智能的强化学习方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-24 DOI: 10.1109/TMC.2024.3522130

Yuzheng Ren;Haijun Zhang;Fei Richard Yu;Wei Li;Pincan Zhao;Ying He

{"title":"Industrial Internet of Things With Large Language Models (LLMs): An Intelligence-Based Reinforcement Learning Approach","authors":"Yuzheng Ren;Haijun Zhang;Fei Richard Yu;Wei Li;Pincan Zhao;Ying He","doi":"10.1109/TMC.2024.3522130","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522130","url":null,"abstract":"Large Language Models (LLMs), as advanced AI technologies for processing and generating natural language text, bring substantial benefits to the Industrial Internet of Things (IIoT) by enhancing efficiency, decision-making, and automation. Nevertheless, their deployment faces significant obstacles due to high computational and energy demands, which often exceed the capabilities of many industrial devices. To overcome these challenges, edge-cloud collaboration has become increasingly essential, assisting in offloading LLMs tasks to reduce the computational load. However, traditional reinforcement learning (RL)-based strategies for LLMs task offloading encounter difficulties with generalization ability and defining explicit, appropriate reward functions. Therefore, in this paper, we propose a novel framework for offloading LLMs inference tasks in IIoT, utilizing a Decentralized Identifier (DID)-based identity management system for trusted task offloading. Furthermore, we introduce an intelligence-based RL (IRL) approach, which sidesteps the need for defining specific reward functions. Instead, it uses “intelligence” as a metric to evaluate cognitive improvements and adapt to varying environmental preferences, significantly improving generalizability. In our experiments, we employ the GPT-J-6B model and utilize the Human Eval dataset to assess its ability to tackle programming challenges, demonstrating the superior performance of our proposed solution compared to existing methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4136-4152"},"PeriodicalIF":7.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain Assisted Trust Management for Data-Parallel Distributed Learning 数据并行分布式学习的辅助信任管理

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-23 DOI: 10.1109/TMC.2024.3521443

Yuxiao Song;Daojing He;Minghui Dai;Sammy Chan;Kim-Kwang Raymond Choo;Mohsen Guizani

{"title":"Blockchain Assisted Trust Management for Data-Parallel Distributed Learning","authors":"Yuxiao Song;Daojing He;Minghui Dai;Sammy Chan;Kim-Kwang Raymond Choo;Mohsen Guizani","doi":"10.1109/TMC.2024.3521443","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521443","url":null,"abstract":"Machine learning models can support decision-making in mobile terminals (MTs) deployments, but their training generally requires massive datasets and abundant computation resources. This is challenging in practice due to the resource constraints of many MTs. To address this issue, data-parallel distributed learning can be conducted by offloading computation tasks from MTs to the edge-layer nodes. To facilitate the establishment of trust, one can leverage trust management, say to use trust values derived from local model quality and evaluations by other nodes as access criteria. Nonetheless, security and performance considerations remain unsolved. In this paper, we propose a blockchain-assisted dynamic trust management scheme for distributed learning, which comprises nodes attributes registration, trust calculation, information saving, and block writing. The proof of stake (PoS) consensus mechanism is leveraged to enable efficient consensus among the nodes using trust values as stakes. The incentive mechanism and corresponding dynamic optimization are then proposed to further improve system performance and security. The reinforcement-learning approach is leveraged to provide the optimal strategy for nodes’ local iterations and selection. Simulations and security analysis demonstrate that our proposed scheme can achieve an optimal trade-off between efficiency and quality of distributed learning while maintaining system security.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3826-3843"},"PeriodicalIF":7.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Taming Event Cameras With Bio-Inspired Architecture and Algorithm: A Case for Drone Obstacle Avoidance 驯服事件相机与生物启发的架构和算法：无人机避障案例

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-23 DOI: 10.1109/TMC.2024.3521044

Danyang Li;Jingao Xu;Zheng Yang;Yishujie Zhao;Hao Cao;Yunhao Liu;Longfei Shangguan

{"title":"Taming Event Cameras With Bio-Inspired Architecture and Algorithm: A Case for Drone Obstacle Avoidance","authors":"Danyang Li;Jingao Xu;Zheng Yang;Yishujie Zhao;Hao Cao;Yunhao Liu;Longfei Shangguan","doi":"10.1109/TMC.2024.3521044","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521044","url":null,"abstract":"Fast and accurate obstacle avoidance is crucial to drone safety. Yet existing on-board sensor modules such as frame cameras and radars are ill-suited for doing so due to their low temporal resolution or limited field of view. This paper presents BioDrone, a new design paradigm for drone obstacle avoidance using stereo event cameras. At the heart of BioDrone are three simple yet effective system designs inspired by the mammalian visual system, namely, a chiasm-inspired event filtering, a lateral geniculate nucleus (LGN)-inspired event matching, and a dorsal stream-inspired obstacle tracking. We implement BioDrone on FPGA through software-hardware co-design and deploy it on an industrial drone. In comparative experiments against two state-of-the-art event-based systems, BioDrone consistently achieves an obstacle detection rate of <inline-formula><tex-math>$> $</tex-math></inline-formula>90%, and an obstacle tracking error of <inline-formula><tex-math>$<$</tex-math></inline-formula>5.8 cm across all flight modes with an end-to-end latency of <inline-formula><tex-math>$<$</tex-math></inline-formula>6.4 ms, outperforming both baselines by over 44%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4202-4216"},"PeriodicalIF":7.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Delay-Oriented Joint Optimization Approach for RIS-Assisted MEC-MIMO System ris辅助MEC-MIMO系统面向延迟的联合优化方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-23 DOI: 10.1109/TMC.2024.3521012

Shanshan Jiang;Xue Wang;Junhao Lin;Chongwen Huang;Zhihong Qian;Zhu Han

引用次数: 0

Multi-Modal Federated Learning Based Resources Convergence for Satellite-Ground Twin Networks 基于多模态联邦学习的星地双网资源融合

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-23 DOI: 10.1109/TMC.2024.3521399

Yongkang Gong;Haipeng Yao;Zehui Xiong;Dongxiao Yu;Xiuzhen Cheng;Chau Yuen;Mehdi Bennis;Mérouane Debbah

{"title":"Multi-Modal Federated Learning Based Resources Convergence for Satellite-Ground Twin Networks","authors":"Yongkang Gong;Haipeng Yao;Zehui Xiong;Dongxiao Yu;Xiuzhen Cheng;Chau Yuen;Mehdi Bennis;Mérouane Debbah","doi":"10.1109/TMC.2024.3521399","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521399","url":null,"abstract":"Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intelligent control in terrestrial industrial cyber-physical systems, whose system-level privacy security can be strengthened via blockchain based consensus protocol. Moreover, digital twin (DT) can shorten the gap between physical unities and digital space to enable instant data mapping in SGTNs environments. However, complex multi-modal network environments, such as stochastic task size, dynamic low earth orbit location, and time-varying channel gains, hinder better performance metrics in terms of energy consumption, throughput and privacy overhead. Hence, we establish a SGTN integrated cloud-fog automation model to transfer task data to low earth orbit satellites, and then execute broad communication access, powerful computation offloading, and efficient twin control. Next, we propose a Lyapunov stability theory based multi-modal federated learning (LST-MMFL) method to optimize the battery energy, the size of block, computation frequency, and the number of twin control for minimizing the total energy consumption and privacy overhead. Furthermore, we design a novel blockchain based transaction verification protocol to strengthen privacy security, derive performance upper bounds of SGTN model, and fulfill the long-term average task as well as energy queue constraints. Finally, massive simulation results show that the proposed LST-MMFL algorithm outperforms existing state-of-the-art benchmarks in line with energy consumption, available battery level, networked control and privacy protection overhead.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4104-4117"},"PeriodicalIF":7.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0