IEEE Transactions on Mobile Computing最新文献

Corrections to “Learning Domain-Invariant Model for WiFi-Based Indoor Localization” 对“基于wifi的室内定位学习域不变模型”的修正

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-06-05 DOI: 10.1109/TMC.2025.3539443

Guanzhong Wang;Dongheng Zhang;Tianyu Zhang;Shuai Yang;Qibin Sun;Yan Chen

引用次数: 0

Correction to “CV-Cast: Computer Vision–Oriented Linear Coding and Transmission” 更正“CV-Cast：计算机视觉导向的线性编码和传输”

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-06-05 DOI: 10.1109/TMC.2025.3565860

Jakub Žádník;Michel Kieffer;Anthony Trioux;Markku Mäkitalo;Pekka Jääskeläinen

引用次数: 0

Differential Private Data Stream Analytics in the Local and Shuffle Models 本地和Shuffle模型中的差异私有数据流分析

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-15 DOI: 10.1109/TMC.2025.3559621

Shaowei Wang;Jin Li;Yun Peng;Kongyang Chen;Wei Yang;Hui Jiang;Jin Li

{"title":"Differential Private Data Stream Analytics in the Local and Shuffle Models","authors":"Shaowei Wang;Jin Li;Yun Peng;Kongyang Chen;Wei Yang;Hui Jiang;Jin Li","doi":"10.1109/TMC.2025.3559621","DOIUrl":"https://doi.org/10.1109/TMC.2025.3559621","url":null,"abstract":"We study online data analytics with differential privacy (DP) in decentralized settings. Specifically, online data analytics with local DP protection is widely adopted in real-world applications. Despite numerous endeavors in this field, significant gaps in utility and functionality remain when compared to its offline counterpart. We present an optimal, streamable mechanism: <monospace>ExSub</monospace>, for local DP sparse vector estimation. The mechanism enables a range of online analytics on streaming binary vectors, including multi-dimensional binary, categorical, or set-valued data. By leveraging the negative correlation of occurrence events in the sparse vector, we attain an optimal error rate under local privacy constraints, only requiring streamable computations. To surpass the error barrier of local privacy, we also study <monospace>ExSub</monospace> randomizer in the newly emerging (single-message) shuffle model of DP, and provide nearly-tight privacy amplification bounds therein. Additionally, we leverage the online shuffle model that independently permutes users’ messages at each timestamp, to design a simplified randomization strategy that can approximately reach Gaussian accuracy in central DP. Through experiments with both synthetic and real-world datasets, <monospace>ExSub</monospace> mechanism in the local model have been shown to reduce error by 40%–60% compared to SOTA approaches. The <monospace>ExSub</monospace> in the shuffle model can further reduce over 85% error, and the online shuffle protocol reduces over 99.7% error.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6701-6717"},"PeriodicalIF":7.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219585","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 Adaptation for Mobile 360-Degree Video Streaming and Enhancement 移动360度视频流的联合适配与增强

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-01 DOI: 10.1109/TMC.2025.3555322

Haotian Guo;Feng Wang;Wei Zhang;Yifei Zhu;Laizhong Cui;Jiangchuan Liu;Fei Richard Yu;Lei Zhang

{"title":"Joint Adaptation for Mobile 360-Degree Video Streaming and Enhancement","authors":"Haotian Guo;Feng Wang;Wei Zhang;Yifei Zhu;Laizhong Cui;Jiangchuan Liu;Fei Richard Yu;Lei Zhang","doi":"10.1109/TMC.2025.3555322","DOIUrl":"https://doi.org/10.1109/TMC.2025.3555322","url":null,"abstract":"Tile-based streaming and super resolution (SR) are two representative technologies adopted to improve bandwidth efficiency of 360° video streaming. The former allows selective downloading of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to enhance the received video to higher quality using advanced neural network models. In this work, we propose a Collaborated Streaming and Enhancement (CSE) adaptation framework for mobile 360° videos, which integrates super resolution with tile-based streaming to optimize the user experience with dynamic bandwidth and limited computing capability. To effectively enhance the tile-based video streaming through SR, we propose to adaptively group the tiles for quality enhancement adapting to the content similarity. We also identify and address several key design issues to integrate SR into tile-based video streaming including unified video quality assessment, computational complexity model for super resolution, and buffer analysis considering the interplay between transmission and enhancement. We further formulate the quality-of-experience (QoE) maximization problem for mobile 360° video streaming and propose a rate adaptation algorithm to make the best decisions for download and for enhancement based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which demonstrates stable performance with considerable QoE improvement, while enabling a trade-off between playback smoothness and video quality.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7726-7741"},"PeriodicalIF":7.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550158","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

UltraAdv: An Ultrasonic Adversarial Attack on Closed-Box Speech Recognition Systems UltraAdv：一种针对闭盒语音识别系统的超声波对抗性攻击

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-31 DOI: 10.1109/TMC.2025.3555680

Guoming Zhang;Xiaohui Ma;Huiting Zhang;Riccardo Spolaor;Yanni Yang;Xiaoyu Ji;Xiuzhen Cheng;Pengfei Hu

{"title":"UltraAdv: An Ultrasonic Adversarial Attack on Closed-Box Speech Recognition Systems","authors":"Guoming Zhang;Xiaohui Ma;Huiting Zhang;Riccardo Spolaor;Yanni Yang;Xiaoyu Ji;Xiuzhen Cheng;Pengfei Hu","doi":"10.1109/TMC.2025.3555680","DOIUrl":"https://doi.org/10.1109/TMC.2025.3555680","url":null,"abstract":"Attacks on speech recognition systems often use adversarial or inaudible commands. However, a challenge is that adversarial perturbations typically fall within the audible frequency range, making it difficult to achieve inaudibility. Additionally, the non-linear effects of loudspeakers often cause inaudible commands to become audible at higher power levels. Therefore, minimizing the power requirements of the attack is essential to maintain inaudibility. Another significant obstacle is the conversion of variable-length commands, especially longer ones, into shorter target commands. In this paper, we present UltraAdv, a method for generating long-range adversarial perturbations capable of compromising commands of arbitrary length in closed-box setting. By combining the ultrasonic signal with the normal one, rather than negating it as in DolphinAttack, we significantly improve the energy efficiency, thus enhancing its attack distance. We also propose a dynamically adjustable suppression-interference method based on automatic gain control to address the challenge of mismatched durations between long commands and target commands (length-independent). Experiments demonstrate that using a single perturbation, we achieve impressive success rates of 98.84% and 96.62% and 98.32% across a diverse set of 12,260 speeches on DeepSpeech, iFlytek, and Whisper. The attack range reaches up to 15 m, surpassing DolphinAttack's 5 m range at equivalent power.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7648-7662"},"PeriodicalIF":7.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550522","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

COLoRIS: Localization-Agnostic Smart Surfaces Enabling Opportunistic ISAC in 6G Networks COLoRIS：在6G网络中实现机会ISAC的定位不可知智能表面

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-28 DOI: 10.1109/TMC.2025.3556326

Guillermo Encinas-Lago;Francesco Devoti;Marco Rossanese;Vincenzo Sciancalepore;Marco Di Renzo;Xavier Costa-Pérez

{"title":"COLoRIS: Localization-Agnostic Smart Surfaces Enabling Opportunistic ISAC in 6G Networks","authors":"Guillermo Encinas-Lago;Francesco Devoti;Marco Rossanese;Vincenzo Sciancalepore;Marco Di Renzo;Xavier Costa-Pérez","doi":"10.1109/TMC.2025.3556326","DOIUrl":"https://doi.org/10.1109/TMC.2025.3556326","url":null,"abstract":"The integration of Smart Surfaces in 6G communication networks, also dubbed as Reconfigurable Intelligent Surfaces (RISs), is a promising paradigm change gaining significant attention given its disruptive features. RISs are a key enabler in the realm of 6G Integrated Sensing and Communication (ISAC) systems where novel services can be offered together with the future mobile networks communication capabilities. This paper addresses the critical challenge of precisely localizing users within a communication network by leveraging the controlled-reflective properties of RIS elements without relying on more power-hungry traditional methods, e.g., GPS, adverting the need of deploying additional infrastructure and even avoiding interfering with communication efforts. Moreover, we go one step beyond: we build COLoRIS, an Opportunistic ISAC approach that leverages localization-agnostic RIS configurations to accurately position mobile users via trained learning models. Extensive experimental validation and simulations in large-scale synthetic scenarios show <inline-formula><tex-math>$mathbf{5%}$</tex-math></inline-formula> positioning errors (with respect to field size) under different conditions. Further, we show that a low-complexity version running in a limited off-the-shelf (embedded, low-power) system achieves positioning errors in the <inline-formula><tex-math>$mathbf{11%}$</tex-math></inline-formula> range at a negligible <inline-formula><tex-math>$mathbf{+2.7%}$</tex-math></inline-formula> energy expense with respect to the classical RIS.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"6812-6826"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550610","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

CrowdHMTware: A Cross-Level Co-Adaptation Middleware for Context-Aware Mobile DL Deployment crowdhmware：用于上下文感知移动DL部署的跨级别协同自适应中间件

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-27 DOI: 10.1109/TMC.2025.3549399

Sicong Liu;Bin Guo;Shiyan Luo;Yuzhan Wang;Hao Luo;Cheng Fang;Yuan Xu;Ke Ma;Yao Li;Zhiwen Yu

{"title":"CrowdHMTware: A Cross-Level Co-Adaptation Middleware for Context-Aware Mobile DL Deployment","authors":"Sicong Liu;Bin Guo;Shiyan Luo;Yuzhan Wang;Hao Luo;Cheng Fang;Yuan Xu;Ke Ma;Yao Li;Zhiwen Yu","doi":"10.1109/TMC.2025.3549399","DOIUrl":"https://doi.org/10.1109/TMC.2025.3549399","url":null,"abstract":"There are many deep learning (DL) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives. To enable robust and private mobile sensing, DL models are often deployed locally on resource-constrained mobile devices using techniques such as model compression or offloading. However, existing methods, either front-end algorithm level (i.e. DL model compression/partitioning) or back-end scheduling level (i.e. operator/resource scheduling), cannot be locally online because they require offline retraining to ensure accuracy or rely on manually pre-defined strategies, struggle with dynamic adaptability. The primary challenge lies in feeding back runtime performance from the back-end level to the front-end level optimization decision. Moreover, the adaptive mobile DL model porting middleware with cross-level co-adaptation is less explored, particularly in mobile environments with diversity and dynamics. In response, we introduce CrowdHMTware, a dynamic context-adaptive DL model deployment middleware for heterogeneous mobile devices. It establishes an automated adaptation loop between cross-level functional components, i.e. elastic inference, scalable offloading, and model-adaptive engine, enhancing scalability and adaptability. Experiments with four typical tasks across 15 platforms and a real-world case study demonstrate that <inline-formula><tex-math>${sf CrowdHMTware}$</tex-math></inline-formula> can effectively scale DL model, offloading, and engine actions across diverse platforms and tasks. It hides run-time system issues from developers, reducing the required developer expertise.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7615-7631"},"PeriodicalIF":7.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550670","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

Budget-Feasible Diffusion Mechanisms for Mobile Crowdsourcing in Social Networks 社交网络中移动众包的预算可行扩散机制

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-24 DOI: 10.1109/TMC.2025.3549751

Xiang Liu;Weiwei Wu;Minming Li;Wanyuan Wang;Yifan Qin;Yingchao Zhao;Junzhou Luo

{"title":"Budget-Feasible Diffusion Mechanisms for Mobile Crowdsourcing in Social Networks","authors":"Xiang Liu;Weiwei Wu;Minming Li;Wanyuan Wang;Yifan Qin;Yingchao Zhao;Junzhou Luo","doi":"10.1109/TMC.2025.3549751","DOIUrl":"https://doi.org/10.1109/TMC.2025.3549751","url":null,"abstract":"Mobile crowdsourcing has emerged as a popular approach for organizations to leverage the collective intelligence of a crowd of users to obtain services. Considering users’ costs for providing services, it is vital for the requester to design incentive mechanisms to encourage users’ participation in crowdsourcing under the budget constraint. This aligns with the concept of budget-feasible mechanism design. Existing budget-feasible mechanisms often assume immediate user reachability and willingness of joining the crowdsourcing, which is unrealistic. To address this issue, a promising approach is to have participating users diffuse auction information to potential users in the social network. However, this brings another challenge in that participating users can be strategic and therefore hesitant to invite more potential competitors to join the crowdsourcing platform. In this paper, we focus on developing diffusion mechanisms that incentivize strategic users to actively diffuse auction information through the social network. This helps to attract more informed users and ultimately increases the value of the procured services. Specifically, we propose optimal budget-feasible diffusion mechanisms that simultaneously guarantee individual rationality, budget-feasibility, strong budget-balance, incentive-compatibility (i.e., users report real costs and diffuse auction information to all their neighbors) and approximation. Experiment results under real datasets further demonstrate the efficiency of proposed mechanisms.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7189-7205"},"PeriodicalIF":7.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550536","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

AIChronoLens: AI/ML Explainability for Time Series Forecasting in Mobile Networks AIChronoLens：移动网络中时间序列预测的AI/ML可解释性

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-24 DOI: 10.1109/TMC.2025.3554035

Pablo Fernández Pérez;Claudio Fiandrino;Eloy Pérez Gómez;Hossein Mohammadalizadeh;Marco Fiore;Joerg Widmer

{"title":"AIChronoLens: AI/ML Explainability for Time Series Forecasting in Mobile Networks","authors":"Pablo Fernández Pérez;Claudio Fiandrino;Eloy Pérez Gómez;Hossein Mohammadalizadeh;Marco Fiore;Joerg Widmer","doi":"10.1109/TMC.2025.3554035","DOIUrl":"https://doi.org/10.1109/TMC.2025.3554035","url":null,"abstract":"Forecasting is increasingly considered a fundamental enabler for the management of next-generation mobile networks. While deep neural networks excel at short- and long-term forecasting, their complexity hinders interpretability, a crucial factor for production deployment. The existing EXplainable Artificial Intelligence (XAI) techniques, primarily designed for computer vision and natural language processing, struggle with time series data due to their lack of understanding of temporal characteristics of the input data. In this paper, we take the research on EXplainable Artificial Intelligence (XAI) for time series forecasting one step further by proposing <sc>AIChronoLens, a new tool that links legacy XAI explanations with the temporal properties of the input. <sc>AIChronoLens allows diving deep into the behavior of time series predictors and spotting, among other aspects, the hidden causes of forecast errors. We show that <sc>AIChronoLens’s output can be utilized for meta-learning to predict when the original time series forecasting model makes errors and fix them in advance, thereby improving the accuracy of the predictors. Extensive evaluations with real-world mobile traffic traces pinpoint model behaviors that would not be possible to identify otherwise and show how model performance can be improved by 32 % upon re-training and by up to 39 % with meta-learning.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7757-7772"},"PeriodicalIF":7.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550755","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

Reads: A Personalized Federated Learning Framework With Fine-Grained Layer Aggregation and Decentralized Clustering 阅读：具有细粒度层聚合和分散聚类的个性化联邦学习框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-19 DOI: 10.1109/TMC.2025.3552982

Haoyu Fu;Fengsen Tian;Guoqiang Deng;Lingyu Liang;Xinglin Zhang

{"title":"Reads: A Personalized Federated Learning Framework With Fine-Grained Layer Aggregation and Decentralized Clustering","authors":"Haoyu Fu;Fengsen Tian;Guoqiang Deng;Lingyu Liang;Xinglin Zhang","doi":"10.1109/TMC.2025.3552982","DOIUrl":"https://doi.org/10.1109/TMC.2025.3552982","url":null,"abstract":"The heterogeneity of local data and client performance, along with real-world system risks, is driving the evolution of federated learning (FL) towards personalized, model-heterogeneous, and decentralized approaches. However, due to the differing structures of heterogeneous models, it is hard to use them to identify clients with similar data distributions and further enhance the personalization of local models. Therefore, how to deal with data heterogeneity to obtain superior personalized local models for clients, while simultaneously addressing model heterogeneity and system risks is a challenging problem. In this paper, we propose a novel personalized FL framework with fine-gRained layEr aggregAtion and Decentralized cluStering (<inline-formula><tex-math>${sf Reads}$</tex-math></inline-formula>), which integrates four key components: (1) deep mutual learning with privacy guarantee for model training and privacy preservation, (2) fine-grained layer similarity computation among heterogeneous model layers, (3) fully decentralized clustering for soft clustering of clients based on layer similarities, and (4) personalized layer aggregation for capturing common knowledge from other clients. Through <inline-formula><tex-math>${sf Reads}$</tex-math></inline-formula>, clients obtain personalized models that accommodate model heterogeneity, while the system ensures robustness against a single point of failure. Extensive experiments demonstrate the efficacy of <inline-formula><tex-math>${sf Reads}$</tex-math></inline-formula> in achieving these goals.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7709-7725"},"PeriodicalIF":7.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550754","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