IEEE Transactions on Mobile Computing最新文献_第7页

Robust Task Offloading and Resource Allocation Under Imperfect Computing Capacity Information in Edge Intelligence Systems 边缘智能系统不完全计算能力信息下的鲁棒任务卸载与资源分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-06 DOI: 10.1109/TMC.2025.3539296

Zhaojun Nan;Yunchu Han;Jintao Yan;Sheng Zhou;Zhisheng Niu

{"title":"Robust Task Offloading and Resource Allocation Under Imperfect Computing Capacity Information in Edge Intelligence Systems","authors":"Zhaojun Nan;Yunchu Han;Jintao Yan;Sheng Zhou;Zhisheng Niu","doi":"10.1109/TMC.2025.3539296","DOIUrl":"https://doi.org/10.1109/TMC.2025.3539296","url":null,"abstract":"In edge intelligence systems, task offloading and resource allocation policies critically depend on the required computing capacity of the task, which can only be accurately measured after execution, presenting significant design challenges. In this paper, we address the problem of robust task offloading and resource allocation under imperfect computing capacity information, where the exact value as well as distribution knowledge of the required computing capacity cannot be obtained in advance. Specifically, we formulate the <italic>energy-time cost</i> (ETC) minimization problem using min-max robust optimization. To tackle this challenging issue, we propose a decoupling method. This method first assumes the offloading policy is predetermined and derives two independent subproblems: local ETC and edge ETC. Then, we provide a closed-form optimal solution for the local ETC problem. The edge ETC problem is equivalently transformed into a geometric programming (GP) problem, and we introduce an effective iterative algorithm to obtain a stationary point, utilizing successive convex approximation (SCA). Finally, we design a coordinate descent (CD)-based algorithm to optimize the offloading policy effectively. Extensive simulations demonstrate that the proposed policy significantly outperforms other benchmark methods, achieving near-optimal performance even in the presence of high estimation errors in computing capacity.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6154-6167"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243698","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

Topology-Aware Microservice Architecture in Edge Networks: Deployment Optimization and Implementation 边缘网络中拓扑感知微服务架构：部署优化与实现

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-06 DOI: 10.1109/TMC.2025.3539312

Yuang Chen;Chang Wu;Fangyu Zhang;Chengdi Lu;Yongsheng Huang;Hancheng Lu

{"title":"Topology-Aware Microservice Architecture in Edge Networks: Deployment Optimization and Implementation","authors":"Yuang Chen;Chang Wu;Fangyu Zhang;Chengdi Lu;Yongsheng Huang;Hancheng Lu","doi":"10.1109/TMC.2025.3539312","DOIUrl":"https://doi.org/10.1109/TMC.2025.3539312","url":null,"abstract":"As a ubiquitous deployment paradigm, integrating microservice architecture (MSA) into edge networks promises to enhance the flexibility and scalability of services. However, it also presents significant challenges stemming from dispersed node locations and intricate network topologies. In this paper, we have proposed a topology-aware MSA characterized by a three-tier network traffic model encompassing the service, microservices, and edge node layers. This model meticulously characterizes the complex dependencies between edge network topologies and microservices, mapping microservice deployment onto link traffic to accurately estimate communication delay. Building upon this model, we have formulated a weighted sum communication delay optimization problem considering different types of services. Then, a novel topology-aware and individual-adaptive microservices deployment (TAIA-MD) scheme is proposed to solve the problem efficiently, which accurately senses the network topology and incorporates an individual-adaptive mechanism in a genetic algorithm to accelerate the convergence and avoid local optima. Extensive simulations show that, compared to the existing deployment schemes, TAIA-MD improves the communication delay performance by approximately 30% to 60% and effectively enhances the overall network performance. Furthermore, we implement the TAIA-MD scheme on a practical microservice physical platform. The experimental results demonstrate that TAIA-MD achieves superior robustness in withstanding link failures and network fluctuations.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6090-6105"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243695","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

Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks 移动边缘计算网络中联合类平衡的客户端选择和带宽分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-06 DOI: 10.1109/TMC.2025.3539284

Jian Tang;Xiuhua Li;Hui Li;Penghua Li;Xiaofei Wang;Victor C. M. Leung

{"title":"Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks","authors":"Jian Tang;Xiuhua Li;Hui Li;Penghua Li;Xiaofei Wang;Victor C. M. Leung","doi":"10.1109/TMC.2025.3539284","DOIUrl":"https://doi.org/10.1109/TMC.2025.3539284","url":null,"abstract":"Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution; For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5681-5698"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255610","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

Multi-Agent Deep Reinforcement Learning With Trajectory Prediction for Task Migration-Assisted Computation Offloading 基于轨迹预测的多智能体深度强化学习任务迁移辅助计算卸载

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-06 DOI: 10.1109/TMC.2025.3539945

Xinyi Zhang;Chunyang Wang;Yanmin Zhu;Jian Cao;Tong Liu

{"title":"Multi-Agent Deep Reinforcement Learning With Trajectory Prediction for Task Migration-Assisted Computation Offloading","authors":"Xinyi Zhang;Chunyang Wang;Yanmin Zhu;Jian Cao;Tong Liu","doi":"10.1109/TMC.2025.3539945","DOIUrl":"https://doi.org/10.1109/TMC.2025.3539945","url":null,"abstract":"Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5839-5856"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255680","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

Joint Frame Drop and Object Detection Task Offloading for Mobile Devices via RL With Lyapunov Optimization 基于Lyapunov优化的RL移动设备联合帧丢弃和目标检测任务卸载

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-06 DOI: 10.1109/TMC.2025.3539356

Vaughn Sohn;Suhwan Kim;Hyang-Won Lee

{"title":"Joint Frame Drop and Object Detection Task Offloading for Mobile Devices via RL With Lyapunov Optimization","authors":"Vaughn Sohn;Suhwan Kim;Hyang-Won Lee","doi":"10.1109/TMC.2025.3539356","DOIUrl":"https://doi.org/10.1109/TMC.2025.3539356","url":null,"abstract":"Object detection has become an increasingly important application for mobile devices. However, state-of-the-art object detection relies heavily on deep neural network, which is often burdensome to compute on mobile devices. To this end, we develop a layering framework for joint video frame drop and object detection task offloading. In the lower layer, by invoking Lyapunov optimization, we devise an algorithm for partitioning and offloading the computation tasks of deep neural networks. This algorithm also specifies the flow control for admitting the application traffic into the network. In the upper layer, we use the flow control as a form of guidance in the action space in order to develop a reinforcement learning (RL) algorithm that selectively drops video frames with object detection performance in consideration. By the nature of design, this Lyapunov-guided RL guarantees the network stability. We show through simulations that our Lyapunov-guided RL drops video frames with reasonable object detection performance and reduced latency while keeping the network stable. We also implemented our algorithm on the remote-controlled (RC) car equipped with microprocessor and GPU, and demonstrate the applicability of our algorithm to real-time object detection tasks from the video stream generated as the RC car moves.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6168-6182"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243808","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

2024 Reviewers List 2024审稿人名单

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-05 DOI: 10.1109/TMC.2025.3527174

引用次数: 0

Burst-Sensitive Traffic Forecast via Multi-Property Personalized Fusion in Federated Learning 基于联邦学习的多属性个性化融合突发敏感流量预测

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-05 DOI: 10.1109/TMC.2025.3538871

Jingjing Xue;Sheng Sun;Min Liu;Yuwei Wang;Xuying Meng;Jingyuan Wang;JunBo Zhang;Ke Xu

{"title":"Burst-Sensitive Traffic Forecast via Multi-Property Personalized Fusion in Federated Learning","authors":"Jingjing Xue;Sheng Sun;Min Liu;Yuwei Wang;Xuying Meng;Jingyuan Wang;JunBo Zhang;Ke Xu","doi":"10.1109/TMC.2025.3538871","DOIUrl":"https://doi.org/10.1109/TMC.2025.3538871","url":null,"abstract":"For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose <italic>Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS)</i>, which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5598-5614"},"PeriodicalIF":7.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255681","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

Multi-Channel Analog Beamforming Transceiver for mmWave Communications 用于毫米波通信的多通道模拟波束形成收发器

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-05 DOI: 10.1109/TMC.2025.3539169

Haotian Zhao;Kamran Entesari;Sebastian Hoyos

{"title":"Multi-Channel Analog Beamforming Transceiver for mmWave Communications","authors":"Haotian Zhao;Kamran Entesari;Sebastian Hoyos","doi":"10.1109/TMC.2025.3539169","DOIUrl":"https://doi.org/10.1109/TMC.2025.3539169","url":null,"abstract":"This paper introduces an analog multi-channel millimeter-wave transceiver architecture that offers advantages in terms of low hardware complexity and computational efficiency compared to digital beamforming and hybrid beamforming techniques. Also, it is known that analog beamforming with a single phase-shifter network faces limitations in maintaining consistent accuracy across a wideband spectrum. To this end, the proposed architecture leverages the inherent bandwidth-splitting property of the multi-channel transceiver. Thus, each sub-band signal is processed by its corresponding channel in the transceiver with an independent analog beamformer per channel. This approach can significantly improve the beamforming accuracy in a wideband communication system such as 5G and future 6G cellular networks. The simulation results demonstrate that increasing the channels in the multi-channel transceiver enables multi-channel analog beamforming to achieve a comparable bit-error-rate (BER) performance to digital beamforming when interference is not considered. Moreover, when interference is present, the proposed multi-channel analog beamforming exhibits enhanced resilience to high power interference compared with digital beamforming with limited analog-to-digital conversion resolution.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6106-6118"},"PeriodicalIF":7.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243701","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

How to Prevent Social Media Platforms From Knowing the Images You Share With Friends 如何防止社交媒体平台知道你和朋友分享的照片

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-05 DOI: 10.1109/TMC.2025.3538885

Dawei Li;Yuxiao Guo;Di Liu;Qifan Liu;Song Bian;Zhenyu Guan

{"title":"How to Prevent Social Media Platforms From Knowing the Images You Share With Friends","authors":"Dawei Li;Yuxiao Guo;Di Liu;Qifan Liu;Song Bian;Zhenyu Guan","doi":"10.1109/TMC.2025.3538885","DOIUrl":"https://doi.org/10.1109/TMC.2025.3538885","url":null,"abstract":"The surge in image sharing on social media platforms escalates private information extraction for commercial use, increasing user demand for privacy protection. However, the dynamics of group communication within online social networks and the image compression imposed by platforms present significant challenges to secure key exchange and reliable image sharing in existing solutions. In this paper, we propose PrivSocial to prevent social media platforms from extracting private information in images shared within group communications. Specifically, we propose two frameworks, a server-based framework and a subscription-based framework, making PrivSocial applicable to different social media platforms and providing users with optional security levels, enhancing the flexibility and efficiency. To achieve intra-group key agreement and ensure image privacy protection, both frameworks integrate optimized continuous group key agreement and a novel image encryption scheme resisting compression. We implement an Android-based Priv-raster application and deploy a prototype on Twitter. Furthermore, we evaluate the proposed encryption scheme, and experimental results show that it has efficient encryption and decryption performance while being resistant to jigsaw puzzle solver attacks. The multi-user simulation experiments also demonstrate that the processing time of a single user is mere milliseconds, and the scheme can efficiently support tens of thousands of groups.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5808-5823"},"PeriodicalIF":7.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255657","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

Securing Autonomous UAV Cluster With Blockchain-Based Threshold Key Management System Utilizing Crypto-Asset and Multisignature 利用加密资产和多重签名的基于区块链的阈值密钥管理系统保护无人机集群

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-04 DOI: 10.1109/TMC.2025.3538462

Mebanjop Kharjana;Subhas Chandra Sahana;Goutam Saha

{"title":"Securing Autonomous UAV Cluster With Blockchain-Based Threshold Key Management System Utilizing Crypto-Asset and Multisignature","authors":"Mebanjop Kharjana;Subhas Chandra Sahana;Goutam Saha","doi":"10.1109/TMC.2025.3538462","DOIUrl":"https://doi.org/10.1109/TMC.2025.3538462","url":null,"abstract":"Unmanned aerial vehicles deployed in remote locations rely on self-governed key management for their protection. However, conventional key management depends on a centralized ground-based station or single vehicle. Such a system is vulnerable to compromised certificate authority problems and single-points-of-failure. This paper proposed to resolve these vulnerabilities using a blockchain-based threshold key management system. The proposed system utilized blockchain’s concepts of crypto-asset and multisignature. Keys are defined as crypto-assets to improve their management in the blockchain network. Multisignature facilitates collaboration during key management based on a threshold value. The threshold value is also configurable to meet systems’ security and performance requirements. The proposed system secured the process of re-enforcement, sub-clustering, re-merging, and inter-cluster migration. Security analysis revealed that the proposed system complied with most key management security guidelines. The custom signature module used to authenticate intra-cluster communication was also verified as safe. Threats to the cluster were identified, assessed for risk, and mitigated accordingly. Performance analysis found that both AODV and DSDV routing protocols offer consistent performance but DSDV prevailed during the worst-case network scenario. The paper finally identified research gaps, including the requirement for an optimized mechanism for collecting consent signatures.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"5765-5778"},"PeriodicalIF":7.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255660","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