IEEE Transactions on Mobile Computing最新文献_第9页

Accelerating Federated Codistillation via Adaptive Computation Amount at Network Edge 基于网络边缘自适应计算量的联邦共蒸馏加速

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-24 DOI: 10.1109/TMC.2025.3533591

Zhihao Zeng;Xiaoning Zhang;Yangming Zhao;Ahmed Zoha;Muhammad Ali Imran;Yan Zhang

{"title":"Accelerating Federated Codistillation via Adaptive Computation Amount at Network Edge","authors":"Zhihao Zeng;Xiaoning Zhang;Yangming Zhao;Ahmed Zoha;Muhammad Ali Imran;Yan Zhang","doi":"10.1109/TMC.2025.3533591","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533591","url":null,"abstract":"The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5584-5597"},"PeriodicalIF":7.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929818","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 Bargaining Approach for Service Placement in Multi-Access Edge Computing With Information Asymmetries 信息不对称下多访问边缘计算服务配置的讨价还价方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-23 DOI: 10.1109/TMC.2025.3533045

Bernd Simon;Paul Adrian;Patrick Weber;Patrick Felka;Oliver Hinz;Anja Klein

{"title":"A Bargaining Approach for Service Placement in Multi-Access Edge Computing With Information Asymmetries","authors":"Bernd Simon;Paul Adrian;Patrick Weber;Patrick Felka;Oliver Hinz;Anja Klein","doi":"10.1109/TMC.2025.3533045","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533045","url":null,"abstract":"Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve <inline-formula><tex-math>$text{79}{%}$</tex-math></inline-formula> of the optimal reduction in traffic given by a centralized optimal service placement strategy.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5464-5481"},"PeriodicalIF":7.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929723","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

Minimization of the Training Makespan in Hybrid Federated Split Learning 混合联邦分割学习中训练最大时间跨度的最小化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-23 DOI: 10.1109/TMC.2025.3533033

Joana Tirana;Dimitra Tsigkari;George Iosifidis;Dimitris Chatzopoulos

{"title":"Minimization of the Training Makespan in Hybrid Federated Split Learning","authors":"Joana Tirana;Dimitra Tsigkari;George Iosifidis;Dimitris Chatzopoulos","doi":"10.1109/TMC.2025.3533033","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533033","url":null,"abstract":"Parallel Split Learning (SL) allows resource-constrained devices that cannot participate in Federated Learning (FL) to train deep neural networks (NNs) by splitting the NN model into parts. In particular, such devices (clients) may offload the processing task of the largest model part to a computationally powerful helper, and multiple helpers may be employed and work in parallel. In hybrid federated and split learning (HFSL), on the other hand, devices can participate in the training process through any of the two protocols (SL and FL), depending on the system's characteristics. This could considerably reduce the maximum training time over all clients (makespan), especially in highly heterogeneous scenarios. In this paper, we study the joint problem of the training protocol selection, client-helper assignments, and scheduling decisions, to minimize the training makespan. We prove this problem is NP-hard and propose two solution methods: one based on the decomposition of the problem by leveraging its inherent symmetry, and a second fully scalable one. Through numerical evaluations using our testbed's measurements, we build a solution strategy comprising these methods. Moreover, this strategy finds a near-optimal solution and achieves a shorter makespan than the baseline schemes by up to 71%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5400-5417"},"PeriodicalIF":7.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10851417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AdapLDP-FL: An Adaptive Local Differential Privacy for Federated Learning AdapLDP-FL：用于联邦学习的自适应局部差分隐私

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-23 DOI: 10.1109/TMC.2025.3533090

Gaofeng Yue;Li Yan;Liuwang Kang;Chao Shen

{"title":"AdapLDP-FL: An Adaptive Local Differential Privacy for Federated Learning","authors":"Gaofeng Yue;Li Yan;Liuwang Kang;Chao Shen","doi":"10.1109/TMC.2025.3533090","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533090","url":null,"abstract":"Federated Learning (FL) is a technique that allows multiple participants to co-train machine learning models, while also enhancing privacy by avoiding the exposure of local data. However, it is important to note that despite its effectiveness, there is still a potential risk of leaking users’ private information through weight analysis during FL updates. Local Differential Privacy (LDP) is a technique used to prevent individual information leakage by adding noise to the user's model parameters. However, FL based on LDP lacks dynamic optimization and adaptation considering privacy and data utility, especially regarding noise constraints. This paper investigates FL under the scenario of noise optimization with LDP. Specifically, given a certain privacy budget, we design the adaptive LDP method via a noise scaler, which adaptively optimizes the noise size of every client. Second, we dynamically tailor the model direction after adding noise by the designed a direction matrix, to overcome the model drift problem caused by adding noises to the client model. Finally, our method achieves higher accuracy than some existing works with the same privacy level and the convergence speed is significantly improved.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5569-5583"},"PeriodicalIF":7.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929724","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

Predicting Mobile App Usage With Context-Aware Dynamic Hypergraphs 使用上下文感知的动态超图预测移动应用的使用情况

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-23 DOI: 10.1109/TMC.2025.3532992

Zihan Huang;Tong Li;Xing Wang;Kexin Yang;Chao Deng;Junlan Feng;Yong Li

{"title":"Predicting Mobile App Usage With Context-Aware Dynamic Hypergraphs","authors":"Zihan Huang;Tong Li;Xing Wang;Kexin Yang;Chao Deng;Junlan Feng;Yong Li","doi":"10.1109/TMC.2025.3532992","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532992","url":null,"abstract":"App usage prediction aims to predict the next app most likely to be used based on historical behaviors, which is beneficial for smartphone system optimization, such as system resource management, battery energy optimization, and user experience enhancement. Existing studies have treated it as a simple time series prediction problem and overlooked the sessionization characteristic of mobile app usage, i.e., neglecting the intent context in which the user interacts with apps. In this paper, we explore the context of user intents and incorporate app sessionization features into prediction models to improve prediction accuracy. Specifically, we first extract the semantic meaning of spatio-temporal contextual information of app usage by constructing an urban knowledge graph. Second, we devise a hypergraph-based embedding model to extract the hyper-relations of intra-session apps. Third, we utilize a self-attention mechanism to fuse intra-session apps’ representations and combine spatio-temporal contextual embedding to form the session representation. We further leverage a transformer for inter-session intent transition modeling to extract users’ dynamic intent (i.e., the semantic meaning of sessions) for app usage. Finally, we jointly fuse dynamic intent and recently used app features using the MLP model for the prediction. The novelty of our method is that we are the first to leverage dynamic hypergraphs for modeling sessionization features, and we model both inter-session and intra-session relations. We evaluate our model based on two real-world datasets collected in Shanghai and Nanchang. In terms of prediction accuracy, mean reciprocal rank, and normalized discounted cumulative gain, our proposed framework outperforms state-of-the-art baselines by more than 30% in the Shanghai dataset and 20% in the Nanchang dataset, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5511-5524"},"PeriodicalIF":7.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929748","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

ESPD-LP: Edge Service Pre-Deployment Based on Location Prediction in MEC 基于位置预测的MEC边缘服务预部署

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-23 DOI: 10.1109/TMC.2025.3533005

Liangjun Song;Gang Sun;Hongfang Yu;Dusit Niyato

{"title":"ESPD-LP: Edge Service Pre-Deployment Based on Location Prediction in MEC","authors":"Liangjun Song;Gang Sun;Hongfang Yu;Dusit Niyato","doi":"10.1109/TMC.2025.3533005","DOIUrl":"https://doi.org/10.1109/TMC.2025.3533005","url":null,"abstract":"The rise of real-time applications, services has made Multi-access Edge Computing (MEC) essential for delivering low-latency, high-performance computing. The effectiveness of MEC, however, is largely contingent on the efficient pre-deployment of services. Despite its importance, efficient service pre-deployment is challenged by the inherent unpredictability of user mobility, the fluctuating conditions of network environments. Accurately predicting user locations, dynamically optimizing resource allocation across geographically distributed MEC servers are complex tasks that are essential to minimizing latency, maximizing data transmission efficiency. The variability in user movement patterns, network bandwidth further exacerbates these challenges, often leading to increased latency, diminished performance, which can negate the advantages offered by MEC. To address these challenges, this paper introduces a novel edge service pre-deployment scheme based on location prediction (ESPD-LP). The ESPD-LP scheme leverages historical user trajectory data to predict future locations, facilitating proactive, strategic resource allocation via a user-centric bidirectional matching algorithm across multiple MEC servers. By pre-deploying services in anticipation of user needs, this approach optimizes data transmission rates, reduces pre-deployment latency, significantly enhancing the overall performance of MEC systems. A comprehensive analysis reveals that the ESPD-LP scheme consistently outperforms similar approaches, with a 41% increase in data transmission rates, a 31% reduction in pre-deployment latency compared to the JO-CDSD, MEC-RDESN schemes, demonstrating consistently superior performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5551-5568"},"PeriodicalIF":7.7,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929765","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 Offloading in Internet of Vehicles: A DRL-Based Approach With Representation Learning for DAG Scheduling 车联网任务分流：基于drl的DAG调度表示学习方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-21 DOI: 10.1109/TMC.2025.3531887

Xiaoheng Deng;Haoyu Yang;Jingjing Zhang;Jinsong Gui;Siyu Lin;Xin Wang;Geyong Min

引用次数: 0

Service Function Chain Deployment With Intrinsic Dynamic Defense Capability 具有内在动态防御能力的业务功能链部署

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-21 DOI: 10.1109/TMC.2025.3532210

Ran Wang;Lundan Cai;Qiang Wu;Dusit Niyato

{"title":"Service Function Chain Deployment With Intrinsic Dynamic Defense Capability","authors":"Ran Wang;Lundan Cai;Qiang Wu;Dusit Niyato","doi":"10.1109/TMC.2025.3532210","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532210","url":null,"abstract":"The Service Function Chain (SFC) leverages Network Function Virtualization (NFV) and Software-Defined Networking (SDN) for flexible deployment, creating customized service chains tailored to specific applications. As NFV and SDN technologies play crucial roles in the SFC implementation, any security risk that arises in an NFV/SDN network can potentially pose a threat to SFC. Thus, SFC becomes vulnerable to network security attacks. To address this, intrinsic security technologies, including moving target defense and mimic defense, offer proactive protection against both known and unknown threats. It is expected to break through traditional security protection mechanisms such as “enhanced”, “plug-in” and “passive” defense. This paper proposes an intrinsic dynamic defense architecture to equip SFC with active defense capabilities, shifting from passive reactive mechanism based on prior knowledge to an active defense against various attacks. The architecture comprises two models and five modules, including a sub-pool partitioning algorithm that enhances heterogeneity across sub-pools by splitting the heterogeneous replica pool into several sub-pools among replica VNFs. To meet Quality of Service (QoS) requirements like latency, cost, and security, we formulate a multi-objective optimization problem with three objectives: latency, cost, and defense success rate. Following that, we propose a dynamic Deep Reinforcement Learning (DRL)-based deployment algorithm. This algorithm selects appropriate VNFs based on heterogeneity and historical information, improving SFC and VNF security against external attacks. Extensive experiments validate that our architecture significantly enhances network security, provided that this improvement comes at the expense of limited cost and latency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5418-5432"},"PeriodicalIF":7.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929764","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

Real-Time Cross-Domain Gesture and User Identification via COTS WiFi 基于COTS WiFi的实时跨域手势和用户识别

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-21 DOI: 10.1109/TMC.2025.3532295

Chenhong Cao;Yue Ding;Miaoling Dai;Wei Gong;Xibin Zhao

{"title":"Real-Time Cross-Domain Gesture and User Identification via COTS WiFi","authors":"Chenhong Cao;Yue Ding;Miaoling Dai;Wei Gong;Xibin Zhao","doi":"10.1109/TMC.2025.3532295","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532295","url":null,"abstract":"WiFi-based gesture recognition has emerged as a promising alternative to computer vision, enabling seamless integration and enhanced interaction in human-computer interaction systems. Simultaneously identifying users during gesture recognition is vital for improving security and personalization. However, existing WiFi-based dual-task recognition approaches often rely on handcrafted features, which hinder precision and introduce delays in cross-domain scenarios. To address these challenges, we propose WiDual, a real-time system for cross-domain gesture recognition and user identification using WiFi signals. By integrating spatial and channel attention mechanisms, WiDual adaptively extracts crucial features for dual-task recognition. The system employs Channel State Information (CSI) visualization to convert WiFi signals into images, facilitating efficient feature extraction and minimizing information loss and latency. Furthermore, a collaborative module fuses gesture and user identity features, enhancing recognition performance. Experimental evaluations on a public dataset with six gestures and six users across diverse environments demonstrate WiDual's effectiveness. It achieves 96% accuracy in cross-domain gesture recognition and 91.27% in user identification. Compared to state-of-the-art methods, WiDual improves user identification accuracy by 26%, gesture recognition by 8%, and reduces processing time sixfold, showcasing its potential for real-time applications.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5124-5137"},"PeriodicalIF":7.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918803","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

Online Energy and Interference Management for Dynamic Target Tracking With Cellular-Connected UAV 蜂窝互联无人机动态目标跟踪的在线能量与干扰管理

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-21 DOI: 10.1109/TMC.2025.3532276

Cheng Zhan;Huan Yan;Rongfei Fan;Han Hu;Shubin Xu;Jian Yang

{"title":"Online Energy and Interference Management for Dynamic Target Tracking With Cellular-Connected UAV","authors":"Cheng Zhan;Huan Yan;Rongfei Fan;Han Hu;Shubin Xu;Jian Yang","doi":"10.1109/TMC.2025.3532276","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532276","url":null,"abstract":"Cellular-connected Unmanned Aerial Vehicles (UAVs) have significant potential for target tracking in future cellular networks due to their broad coverage and operational flexibility. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV for target tracking, which encounters challenges such as unpredictable flight energy consumption from the stochastic movements of the tracking target and severe uplink interference from ground devices (GDs). To tackle these challenges, we propose a multi-stage stochastic optimization framework focused on energy-efficient target tracking with interference coordination. Our objective is to optimize the long-term average uplink throughput of both aerial users and GDs by jointly optimizing the UAV's trajectory, power allocation, and cell association across multiple orthogonal communication resource blocks (RBs). The formulated stochastic non-convex problem is first transformed into a deterministic problem for each time slot by using the Lyapunov optimization framework. An online optimization strategy is proposed, utilizing the optimal structure, alternative optimization, and successive convex approximation (SCA) techniques. Simulation results show that the proposed approach significantly enhances network throughput and UAV energy queue stability compared to existing baseline schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5496-5510"},"PeriodicalIF":7.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929780","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