IEEE Transactions on Mobile Computing最新文献_第2页

Reliability-Enhanced Network Slicing for Time-Varying Software-Defined Space Information Network 时变软件定义空间信息网络的可靠性增强网络切片

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TMC.2025.3642190

Huiting Yang;Feng Wang;Wei Liu;Wenqiang Pu;Haibo Zhou;Tony Q. S. Quek

{"title":"Reliability-Enhanced Network Slicing for Time-Varying Software-Defined Space Information Network","authors":"Huiting Yang;Feng Wang;Wei Liu;Wenqiang Pu;Haibo Zhou;Tony Q. S. Quek","doi":"10.1109/TMC.2025.3642190","DOIUrl":"https://doi.org/10.1109/TMC.2025.3642190","url":null,"abstract":"In software-defined satellite information networks (SD-SINs), each requested service can be characterized by a predetermined sequence of virtual network functions (VNFs), referred to as a service function chain (SFC). However, VNFs shared by multiple requested services are prone to failures, causing service interruptions. Furthermore, the rapid movement of satellites results in an intermittent yet predictable network topology. Moreover, efficient use of multi-dimensional heterogeneous resources can enhance reliability and network performance. Therefore, in this paper, we investigate reliability-enhanced network slicing by jointly exploiting communication, storage, and computation resources in time-varying SD-SINs. Specifically, we use the time-expanded graph (TEG) to model time-varying SD-SINs with multi-dimensional heterogeneous resources. Based on TEG, we propose a joint reliability-enhanced VNF deployment and flow routing strategy, formulated as an integer nonlinear programming (INLP) problem, to maximize the number of completed services with reliability requirements. To effectively solve the INLP problem, we propose two novel algorithms: the integer linear programming reformulation (ILPR) algorithm, which achieves optimal solutions but with high complexity, and the LP relaxation-based VNF deployment and routing (LPR-VDR) algorithm, which provides near-optimal solutions with significantly lower complexity. Simulation results demonstrate that the LPR-VDR algorithm performs very closely to the ILPR algorithm.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7354-7372"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588188","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

Efficient Mobile-Cloud Collaborative Aggregation for Federated Learning With Latency Resilience 基于延迟弹性的高效移动云协同聚合联邦学习

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1109/TMC.2025.3642433

Wei Tang;Jiliang Li;Xiaojun Zhang;Yinbin Miao;Zhou Su;Robert H. Deng

{"title":"Efficient Mobile-Cloud Collaborative Aggregation for Federated Learning With Latency Resilience","authors":"Wei Tang;Jiliang Li;Xiaojun Zhang;Yinbin Miao;Zhou Su;Robert H. Deng","doi":"10.1109/TMC.2025.3642433","DOIUrl":"https://doi.org/10.1109/TMC.2025.3642433","url":null,"abstract":"With the rapid growth of mobile and edge computing, federated learning (FL) has emerged as a key technology to enable collaborative model training on mobile devices while preserving user privacy. Secure aggregation is an essential component in FL to protect local gradients and compute the global model, but it is vulnerable to threats from high-latency. When some clients arrive late, the pairwise masks among clients cannot be canceled properly, forcing the server to learn the late clients’ masks in order to complete the aggregation. As a result, network uncertainty puts the aggregation process at risk of either service interruption or privacy leakage. While double masking is treated as the most effective solution to achieve both robustness and privacy, its computational and communication costs are prohibitive, especially for resource-constrained mobile devices. To address these challenges, we propose an Efficient Mobile-Cloud Collaborative Aggregation for Federated Learning with Latency Resilience (EFL-LR). We leverage Shamir’s secret sharing and a key-homomorphic pseudorandom function to ensure privacy for high-latency clients while reducing computation overheads to <inline-formula><tex-math>$mathcal {O}(nlog ^{2} n + d)$</tex-math></inline-formula> for clients and <inline-formula><tex-math>$mathcal {O}(n+d)$</tex-math></inline-formula> for the server. Formal security analysis confirms its latency resilience and privacy guarantees. Experimental results show that EFL-LR achieves 2–<inline-formula><tex-math>$3times$</tex-math></inline-formula> lower client-side computation cost and accelerates server-side aggregation recovery by at least <inline-formula><tex-math>$10times$</tex-math></inline-formula>.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7391-7407"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588248","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 Flexible and Scalable Multi-Agent Learning Framework for Dynamic RAN Slicing in 6G Native-AI Networks 6G Native-AI网络中动态RAN切片的灵活可扩展多智能体学习框架

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TMC.2025.3642213

Yuanhao Li;Haozhe Wang;Geyong Min;Xiaoyang Wang;Chao Wang

{"title":"A Flexible and Scalable Multi-Agent Learning Framework for Dynamic RAN Slicing in 6G Native-AI Networks","authors":"Yuanhao Li;Haozhe Wang;Geyong Min;Xiaoyang Wang;Chao Wang","doi":"10.1109/TMC.2025.3642213","DOIUrl":"https://doi.org/10.1109/TMC.2025.3642213","url":null,"abstract":"The advent of 6G networks necessitates fundamental advancements in Radio Access Network (RAN) slicing to accommodate unprecedented service demands with highly dynamic traffic patterns. Existing AI-driven approaches for RAN slicing face critical limitations: they typically require model retraining when slice configurations change, lack scalability for concurrent slice management, or overlook the significant computational overhead from frequent resource reconfigurations. This paper addresses these significant challenges by introducing a novel RAN Partially Observable Stochastic Game (RAN-POSG) framework that enables parallel decision-making while effectively incorporating global resource constraints. Firstly, we propose an Adaptive Retention Framework (ARF) that strategically balances stability and adaptability through an innovative Conditional Accumulative Reward mechanism, significantly reducing reconfiguration overhead while maintaining high Service Level Agreement (SLA) compliance. Our proposed Statistic-Field Deep Truncated Monte Carlo (SF-DTMC) algorithm leverages multi-agent reinforcement learning where agents make independent yet coordinated decisions, ensuring both flexibility with varying slice counts and scalability under large-scale networks. Lastly, a custom-built high-fidelity RAN slicing simulator based on NS-3 simulator with PettingZoo interfaces is developed for comprehensive evaluations across multiple test scenarios. The simulation results demonstrate the significant advantages of the proposed framework, including superior user satisfaction, resource utilization, and lower resource reconfiguration overhead compared to state-of-the-art methods, representing a crucial advancement toward Native-AI in 6G networks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7258-7273"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588184","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

FLAGuard: Efficient Verifiable Federated LoRA of Large Language Models FLAGuard：大型语言模型的高效可验证联邦LoRA

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TMC.2025.3641570

Tianyou Zhang;Haiyang Yu;Yuwen Chen;Shuo Wang;Zhen Yang

{"title":"FLAGuard: Efficient Verifiable Federated LoRA of Large Language Models","authors":"Tianyou Zhang;Haiyang Yu;Yuwen Chen;Shuo Wang;Zhen Yang","doi":"10.1109/TMC.2025.3641570","DOIUrl":"https://doi.org/10.1109/TMC.2025.3641570","url":null,"abstract":"Federated fine-tuning efficiently adapts large pre-trained models to new tasks by using additional data while minimizing re-training costs. This approach enhances data privacy and reduces computational demands but relies on a central server, often cloud-based, which is vulnerable to adversarial attacks that can compromise the aggregation process. We propose <inline-formula><tex-math>${sf FLAGuard}$</tex-math></inline-formula>, a novel and efficient verification scheme that specifically addresses these challenges in federated Low-Rank Adaptation (LoRA) settings. <inline-formula><tex-math>${sf FLAGuard}$</tex-math></inline-formula> is the first to introduce a two-stage verification process specifically designed for LoRA-based aggregation. In the first stage, the scheme independently verifies the correctness of the aggregated <inline-formula><tex-math>$A$</tex-math></inline-formula> and <inline-formula><tex-math>$B$</tex-math></inline-formula> matrices. In the second stage, it verifies the multiplication result of the aggregated <inline-formula><tex-math>$A$</tex-math></inline-formula> and <inline-formula><tex-math>$B$</tex-math></inline-formula> matrices, ensuring the correctness of the final LoRA parameters. Additionally, we introduce the Iterative Gradient Sampling and Convolutional Compression (IGSCC) technique, which combines probabilistic sampling with convolutional operations to efficiently reduce the dimensionality of gradient matrices. This enables secure verification without sacrificing model performance. Our comprehensive security analysis of <inline-formula><tex-math>${sf FLAGuard}$</tex-math></inline-formula> further establishes its reliability in federated learning environments. Extensive experimental results demonstrate that <inline-formula><tex-math>${sf FLAGuard}$</tex-math></inline-formula> achieves over a <inline-formula><tex-math>$100times$</tex-math></inline-formula> speedup in the aggregation verification phase and reduces communication overhead by more than 50% compared to state-of-the-art methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7182-7195"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588223","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

MaskSense: Motion-Robust Dynamic IBI Estimation via Deep RF Masked Learning 基于深度射频掩模学习的运动鲁棒动态IBI估计

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TMC.2025.3641949

Jianyang Wang;Binbin Zhang;Ruixu Geng;Dongheng Zhang;Yang Hu;Yan Chen

{"title":"MaskSense: Motion-Robust Dynamic IBI Estimation via Deep RF Masked Learning","authors":"Jianyang Wang;Binbin Zhang;Ruixu Geng;Dongheng Zhang;Yang Hu;Yan Chen","doi":"10.1109/TMC.2025.3641949","DOIUrl":"https://doi.org/10.1109/TMC.2025.3641949","url":null,"abstract":"Although radio frequency (RF) sensing offers a promising approach for monitoring human cardiac activity, it traditionally requires subjects to remain in a steady-state throughout the monitoring process to avoid motion artifacts—an inherently impractical constraint that hampers real-world adoption. However, existing methods either yield incorrect estimates from motion-affected segments or discard them entirely, leading to fragmented data and incomplete observations. To address the challenge, we introduce MaskSense, a novel framework designed to address motion interference in long-term monitoring. The key insight is that latent patterns within dynamic inter-beat interval (IBI) sequences allow for accurate heartbeat reconstruction from incomplete observations. Leveraging this insight, MaskSense treats motion-affected periods as ”masked” and steady-state periods as ”unmasked”, and employs a contrastive-learning-assisted masked modeling architecture to reconstruct the masked information. Our 400-hour evaluation with 18 participants confirms that MaskSense effectively recovers IBIs in the presence of motion artifacts, paving the way for more natural and unobtrusive RF-based cardiac activity monitoring.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7373-7390"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588283","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 Co-Design Framework for Container Deployment in Mobile Edge Computing Networks 移动边缘计算网络中容器部署协同设计框架

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1109/TMC.2025.3642137

Shihao Shen;Yicheng Feng;Xiaoxu Ren;Xiaofei Wang;Qiao Xiang;Hong Xu;Chenren Xu;Wenyu Wang

{"title":"A Co-Design Framework for Container Deployment in Mobile Edge Computing Networks","authors":"Shihao Shen;Yicheng Feng;Xiaoxu Ren;Xiaofei Wang;Qiao Xiang;Hong Xu;Chenren Xu;Wenyu Wang","doi":"10.1109/TMC.2025.3642137","DOIUrl":"https://doi.org/10.1109/TMC.2025.3642137","url":null,"abstract":"With the rapid advancement of mobile technologies, including self-driving cars and drones, the deployment of mobile software has become increasingly complex. In this context, virtualization plays a pivotal role by simplifying service deployment through containers and enabling container orchestration platforms to efficiently manage an expanding number of container clusters. This is achieved by leveraging standardized interfaces and minimizing resource optimization overhead. However, the use of distributed servers in mobile edge clusters introduces several challenges, such as bandwidth limitations, network performance fluctuations, and resource constraints, which complicate deployment in these dynamic and resource-constrained environments. In this paper, we rethink the layer-based structure, a fundamental container design, and analyze the challenges and potential of real edge platform traces. Consequently, we propose BREAK, an acceleration middleware for efficient container deployment. With the primary insight of enhancing layer-reuse and deriving benefits from it, we develop a co-design approach centered on layer structure for efficient deployment, ensuring backward compatibility: (<italic>i</i>) a container image refactoring solution that optimizes efficiency while preserving the stack-of-layers structure, (<italic>ii</i>) distributed shared layer-stack caches, dynamically optimized for collaborative container deployment among mobile edge clusters, (<italic>iii</i>) a customized Kubernetes (K8s) scheduler extending awareness of network performance, disk space, and container layer cache for container placement, and (<italic>iv</i>) a tailored storage-driver of the standard container runtime for efficient layer extraction. Results indicate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to the state-of-the-art approach.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7274-7290"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588242","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

AutoMOCHA: Automated Adaptation Hierarchy for Mobile Video Analytics Against Domain Shifts AutoMOCHA：针对领域转移的移动视频分析的自动适应层次结构

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TMC.2025.3641568

Maozhe Zhao;Rui Shen;Shengzhong Liu;Fan Wu;Guihai Chen

{"title":"AutoMOCHA: Automated Adaptation Hierarchy for Mobile Video Analytics Against Domain Shifts","authors":"Maozhe Zhao;Rui Shen;Shengzhong Liu;Fan Wu;Guihai Chen","doi":"10.1109/TMC.2025.3641568","DOIUrl":"https://doi.org/10.1109/TMC.2025.3641568","url":null,"abstract":"Mobile video analytics systems are often deployed in diverse environments, where domain shifts place higher demands on the responsiveness of adaptation for ”expert models” running on resource-constrained mobile devices. However, due to the lack of effective automated on-device model retrieval strategies and corresponding mobile adaptation mechanisms, most existing frameworks still rely on cloud-centric adaptation architectures rather than mobile-centric ones, leading to delayed responses to such domain shifts. We introduce a hierarchical mobile-cloud collaborative adaptation framework, AutoMOCHA, for continuous mobile video analytics in dynamically evolving environments. It includes three main contributions: (1) It facilitates efficient history expert model retrieval, without relying on dataset-based model validation, through an automatically constructed model taxonomy and indexing mechanism that does not require prior domain knowledge; (2) It proactively coordinates onboard lightweight model reuse and finetuning strategy with remote model retrieval and retraining to achieve responsive model adaptation with low latency; (3) It accelerates onboard model reuse and finetuning through designing a dedicated mobile model cache management strategy and sparse updating policy. Extensive evaluations on real-world video data across three DNN tasks demonstrate that AutoMOCHA improves model accuracy during adaptation by up to 3.4%, increases the post-clustering silhouette coefficient by up to 33%, and reduces retraining frequency by up to 10%, all while maintaining optimal response latency and retraining time.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7149-7165"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588272","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

Task-Oriented Integrated Sensing and Semantic Communications for Multi-Device Video Analytics 面向任务的多设备视频分析集成传感和语义通信

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1109/TMC.2025.3641899

Yinghui He;Xin Li;Jun Luo

{"title":"Task-Oriented Integrated Sensing and Semantic Communications for Multi-Device Video Analytics","authors":"Yinghui He;Xin Li;Jun Luo","doi":"10.1109/TMC.2025.3641899","DOIUrl":"https://doi.org/10.1109/TMC.2025.3641899","url":null,"abstract":"Video analytics plays a vital role in modern applications such as public safety and smart cities, yet transmitting high-resolution video over wireless networks is severely constrained by bandwidth and latency. Existing semantic communication approaches alleviate communication overhead by discarding irrelevant content, but they often impose prohibitive computational costs on resource-constrained surveillance devices. To overcome this limitation, we propose SenSem, a sensing-assisted semantic communication framework that uniquely leverages channel state information (CSI) to reduce both communication and computation overhead. SenSemfirst exploits location cues embedded in CSI to estimate the region of interest and crop frames before upload. On the cropped frames, a lightweight semantic evaluator scores blocks, and a joint block selection and transmit power control algorithm maximizes the analytics performance for multi-device uplink; at the edge, a sensing-assisted analytics network injects spatial cues to further boost inference. Extensive evaluations on the WARP platform demonstrate that SenSemconsistently outperforms state-of-the-art baselines, achieving superior video analytics accuracy under strict latency constraints. By seamlessly reducing both transmission and device-side computation overhead, SenSemoffers a scalable and efficient solution for next-generation wireless video analytics systems.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7323-7337"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588219","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

Stratified Programming of Joint Observation and Transmission for Multiple Agile Satellites in Mega Constellation Networks 大星座网络中多颗敏捷卫星联合观测与传输分层规划

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-04 DOI: 10.1109/TMC.2025.3640133

Ningxuan Guo;Yuqi Wang;Yufei Li;Liang Liu;Yupeng Gong;Ningyuan Wang;Anshou Li;Dan Liu;Xiaoqing Zhong

{"title":"Stratified Programming of Joint Observation and Transmission for Multiple Agile Satellites in Mega Constellation Networks","authors":"Ningxuan Guo;Yuqi Wang;Yufei Li;Liang Liu;Yupeng Gong;Ningyuan Wang;Anshou Li;Dan Liu;Xiaoqing Zhong","doi":"10.1109/TMC.2025.3640133","DOIUrl":"https://doi.org/10.1109/TMC.2025.3640133","url":null,"abstract":"With the improvement of satellites’ maneuverability, agile earth observation satellites (AEOSs) can pitch and roll themselves to observe targets with a longer visible time window (VTW), which enables more targets to be observed while bringing greater uncertainties of mission planning and more conflicts of resources. Meanwhile, mega constellation networks (MCNs) provide powerful tools to transmit massive observation data. In MCNs, AEOSs can observe targets agilely and access communication satellites (CSs) by inter-satellite links (ISLs) to offload data. Based on this architecture, we propose a Stratified Programming method of Joint Observation and Transmission planning for Multiple AEOSs (MA-SPJOT). This method comprises centralized mission allocation methods and a distributed Single-AEOS Joint Observation and Transmission (SA-JOT) model. The centralized allocation methods allocate targets and CSs to specific AEOSs to transform the multi-AEOS problem into several single-AEOS subproblems. The SA-JOT model is formulated as a Mixed Integer Quadratic Constraint Programming (MIQCP) problem based on a mission-based time slot division method, which can help simplify the observation time determination and ISL handover modeling. The proposed model can realize both the benefit maximization and the transmission delay minimization based on practical constraints of mission transition time, laser ISLs’ characteristics, and limited onboard resources. We verify the effectiveness of the proposed MA-SPJOT algorithm in MCNs with 720 CSs and different numbers of AEOSs. The results show that the proposed algorithm can obtain a solution very close to the global optimum of the centralized method and is applicable in MCNs with hundreds of satellites.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7225-7240"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588225","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

Performance Evaluation of SDN-Integrated MEC System for 5G Infrastructure Planning 面向5G基础设施规划的sdn集成MEC系统性能评价

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-03-01 Epub Date: 2025-12-08 DOI: 10.1109/TMC.2025.3641263

Erick Nascimento;Eduardo Tavares;Jamilson Dantas;Paulo Maciel

{"title":"Performance Evaluation of SDN-Integrated MEC System for 5G Infrastructure Planning","authors":"Erick Nascimento;Eduardo Tavares;Jamilson Dantas;Paulo Maciel","doi":"10.1109/TMC.2025.3641263","DOIUrl":"https://doi.org/10.1109/TMC.2025.3641263","url":null,"abstract":"Multi-access Edge Computing (MEC) is a key enabler of 5G networks, supporting handling latency, bandwidth, and connectivity requirements. However, integrating MEC with Software-Defined Networking (SDN) introduces significant challenges in resource management, Quality of Service (QoS) assurance, and scalability. Existing performance evaluation methods do not meet the expectations of addressing the complexity and reliability needed for SDN-based MEC systems. This paper proposes a stochastic modeling framework based on Stochastic Petri Nets (SPN) to assess the performance of MEC-SDN architectures. The model captures workload dynamics, system utilization, and failure conditions, providing a comprehensive and scalable performance evaluation tool. The method is validated through a real-world Vehicle-to-Infrastructure (V2I) scenario deployed on a cluster of single-board computers. Experimental results demonstrate up to 99.85% system utilization, low bandwidth consumption, and model validation within a 95% confidence interval. These outcomes confirm the model’s effectiveness in evaluating SDN-enabled MEC deployments’ resource allocation and performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 5","pages":"7210-7224"},"PeriodicalIF":9.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588246","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