IEEE Transactions on Mobile Computing最新文献

{"title":"Toward Universal Personalization in Federated Learning via Collaborative Foundation Generative Models","authors":"Chenrui Wu;Zexi Li;Fangxin Wang;Hongyang Chen;Jiajun Bu;Haishuai Wang","doi":"10.1109/TMC.2025.3564880","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564880","url":null,"abstract":"Personalized federated learning (PFL) enhances the performance of customized client models through collaborative training without compromising data privacy and ownership. Some previous PFL methods rely on rich prior knowledge about the types of data heterogeneity (such as class imbalance or feature skew), which greatly limits their application ranges. In this paper, we study the <bold>Universal Personalization in Federated Learning (UniPFL)</b>, the problem that has no prior knowledge about the types of data heterogeneity. In real-world PFL scenarios, UniPFL is potential because the data distributions of clients are usually heterogeneous and unknown to the server, where quantity imbalance, class imbalance, feature skew, or hybrid heterogeneity are possible contingencies. To address UniPFL, we propose <bold>FedFD</b>, a novel framework with local data augmentation and global concept fusion, which is based on the recent advances in <italic>the foundation generative models</i> (e.g., diffusion models, BLIP-2). On the client side, FedFD utilizes a diffusion model to assist local training by generating augmented data samples, and is then efficiently fine-tuned to be personalized. On the server side, we customize the aggregation strategies based on model similarities to learn both personalized models and diverse feature concepts. Extensive experiments show that FedFD reaches the state-of-the-art on (1) CIFAR-10 and CIFAR-100 for class imbalance; (2) DomainNet and Office-10 for feature skew, and (3) hybrid heterogeneity with both class and feature shifts.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9695-9708"},"PeriodicalIF":9.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021314","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}

{"title":"Location and Reward Privacy-Preserving Based Secure Task Allocation in Mobile Crowdsensing","authors":"Zhetao Li;Weifan Shi;Young-June Choi;Hiroo Sekiya;Qingyong Deng","doi":"10.1109/TMC.2025.3564404","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564404","url":null,"abstract":"Online multi-task allocation has become an essential research topic in Mobile Crowdsensing (MCS). Most existing studies merely focus on minimizing the total distance that workers need to travel, but ignore considering the total task rewards, which could lead to a reduction in the willingness of workers to complete tasks. In this paper, to incentivize workers to participate in tasks and protect their privacy, we propose a Location and Reward Privacy-Preserving based Secure Task Allocation(LRPP-STA) scheme. First, we design a secure distance computation method to obtain the distance from the workers to the tasks under location privacy preserving. Second, considering fixed reward for the task, we propose a Fixed Rewarding Secure Task Allocation(FR-STA) scheme, where a secure utility calculation method is proposed to calculate the encrypted utility of the worker upon completing tasks under rewards privacy preserving, along with the path planning for workers to maximize the total utility of the system through an Extended Maximum-Utility Flow model(EMUF). Third, considering the situation of dynamic task reward adjusted by requesters based on the supply and demand relationship as well as the urgency of the task, we propose a Dynamic Rewarding Secure Task Allocation(DR-STA) scheme to optimize the task allocation for workers while improving requesters satisfaction. Finally, we theoretically analyze the security of location and reward privacy-preserving scheme, and conduct extensive experiments with real-world datasets to verify that the secure task allocation scheme is effective in improving the total utility of workers compared to other baseline online tasking schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9951-9964"},"PeriodicalIF":9.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021379","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}

{"title":"FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Personalized Autonomous Vehicles With Guaranteed Efficiency","authors":"Pengzhan Zhou;Yijun Zhai;Yuepeng He;Fang Qu;Zhida Qin;Xianlong Jiao;Fulin Luo;Chao Chen;Songtao Guo","doi":"10.1109/TMC.2025.3564402","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564402","url":null,"abstract":"The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV) that adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. Specifically, the architecture employs a designated hypernetwork to learn personalized mask vectors per vehicle used in the linear combination of models shared by vehicles in the same region. This approach ensures that the updated vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on four real-world autonomous driving datasets in various heterogeneous settings. Extensive experiment results demonstrate that FedRAV framework achieves superior performance than the state-of-the-art algorithms, and improves the accuracy by 9.36%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9599-9618"},"PeriodicalIF":9.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036920","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}

{"title":"Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction","authors":"Yiyong Sun;Jiajun He;Zhidi Lin;Wenqiang Pu;Feng Yin;Hing Cheung So","doi":"10.1109/TMC.2025.3564260","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564260","url":null,"abstract":"Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of <italic>channel aging</i> in highly dynamic scenarios. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert knowledge, while emerging data-driven methods typically require substantial labeled data for effective training and often lack interpretability. To address these issues, this paper proposes a novel hybrid method that integrates a data-driven neural network into a conventional model-based workflow based on a state-space model (SSM), implicitly tracking complex channel dynamics from data without requiring precise expert knowledge. Additionally, a novel unsupervised learning strategy is developed to train the embedded neural network solely with unlabeled data. Theoretical analyses and ablation studies are conducted to interpret the enhanced benefits gained from the hybrid integration. Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven. Furthermore, extensive experiments validate its robustness against various challenging factors, including among others severe channel variations.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9743-9759"},"PeriodicalIF":9.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021239","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}

{"title":"SignCRF: Scalable Channel-Agnostic Data-Driven Radio Authentication System","authors":"Amani Al-Shawabka;Philip Pietraski;Sudhir B Pattar;Pedram Johari;Tommaso Melodia","doi":"10.1109/TMC.2025.3564556","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564556","url":null,"abstract":"Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (“fingerprint”) the device itself based on variations introduced in the transmitted waveform. Key impediments in developing robust and scalable Radio Frequency Fingerprinting through Deep Learning (RFFDL) techniques that are practical in dynamic and mobile environments are the non-stationary behavior of the wireless channel and other impairments introduced by the propagation conditions. To date, the existing RFFDL-based techniques have only been able to demonstrate a desirable performance when the training and testing environment remains the same, which makes the solutions impractical. <italic>SignCRF</i> brings to the RFFDL landscape what it has been missing so far: a scalable, channel-agnostic data-driven radio authentication platform with unmatched precision in fingerprinting wireless devices based on their unique manufacturing impairments that is <italic>independent of the dynamic nature of the environment or channel irregularities caused by mobility</i>. <italic>SignCRF</i> consists of: (i) a classifier developed in a base-environment with minimum channel dynamics, and finely trained to authenticate devices with high accuracy and at scale; (ii) an environment translator that is carefully designed and trained to remove the dynamic channel impact from RF signals while maintaining the radio's specific “signature”; and (iii) a Max Rule module that selects the highest precision authentication technique between the baseline classifier and the environment translator per radio. We design, train, and validate the performance of <italic>SignCRF</i> for multiple technologies in dynamic environments and at scale (100 LoRa and 20 WiFi devices, the largest datasets available in the literature). We assess the scalability of <italic>SignCRF</i> across various testbed scales by validating our system using small, medium, and large-scale testbeds, with sizes of 5, 20, and 100 devices, respectively. We demonstrate that <italic>SignCRF</i> can significantly improve the RFFDL performance by achieving as high as 100% correct authentication for WiFi devices and 80% correctly authenticated LoRa devices, a 5x and 8x improvement when compared to the state-of-the-art respectively. Furthermore, we show that <italic>SignCRF</i> is resilient to adversarial actions by reducing the device recognition accuracy from 73% to 6%, which translates into zero mis-authentication of adversary radios that try to impersonate legitimate devices, which has not been achieved by any prior RFFDL techniques.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9383-9394"},"PeriodicalIF":9.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036854","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}

{"title":"Task-Oriented Semantic Communication in Large Multimodal Models-Based Vehicle Networks","authors":"Baoxia Du;Hongyang Du;Dusit Niyato;Ruidong Li","doi":"10.1109/TMC.2025.3564543","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564543","url":null,"abstract":"Task-oriented semantic communication has emerged as a fundamental approach for enhancing performance in various communication scenarios. While recent advances in Generative Artificial Intelligence (GenAI), such as Large Language Models (LLMs), have been applied to semantic communication designs, the potential of Large Multimodal Models (LMMs) remains largely unexplored. In this paper, we investigate an LMM-based vehicle AI assistant using a Large Language and Vision Assistant (LLaVA) and propose a task-oriented semantic communication framework to facilitate efficient interaction between users and cloud servers. To reduce computational demands and shorten response time, we optimize LLaVA's image slicing to selectively focus on areas of utmost interest to users. Additionally, we assess the importance of image patches by combining objective and subjective user attention, adjusting energy usage for transmitting semantic information. This strategy optimizes resource utilization, ensuring precise transmission of critical information. We construct a Visual Question Answering (VQA) dataset for traffic scenarios to evaluate effectiveness. Experimental results show that our semantic communication framework significantly increases accuracy in answering questions under the same channel conditions, performing particularly well in environments with poor Signal-to-Noise Ratios (SNR). Accuracy can be improved by 13.4% at an SNR of 12 dB and 33.1% at 10 dB, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9822-9836"},"PeriodicalIF":9.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021152","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}

{"title":"WiCast: Parallel Cross-Technology Transmission for Connecting Heterogeneous IoT Devices","authors":"Dan Xia;Xiaolong Zheng;Liang Liu;Shanguo Huang;Huadong Ma","doi":"10.1109/TMC.2025.3564340","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564340","url":null,"abstract":"Cross-Technology Communication (CTC) is an emerging technique that enables direct interconnection among incompatible wireless technologies. However, for the downlink from WiFi to multiple IoT technologies, serially emulating and transmitting the data of each IoT technology has extremely low spectrum efficiency. In this paper, we propose <i>WiCast</i>, a parallel CTC that uses IEEE 802.11ax to emulate a composite signal that can be received by commodity BLE, ZigBee, and LoRa devices. By taking advantage of OFDMA in 802.11ax, <i>WiCast</i> uses a single Resource Unit (RU) for parallel CTC and sets other RUs free for high-rate WiFi users. But such a sophisticated composite signal is very easily distorted by emulation imperfections, dynamic channel noises, cyclic prefix, and center frequency offset. We propose a CTC link model that jointly models the emulation errors and channel distortions. Then we carve the emulated signal with elaborate compensations in both time and frequency domains. Based on the proposed CTC scheme, a unified Media Access Control approach is introduced to discover and synchronize the heterogeneous IoT devices. We implement a prototype of <i>WiCast</i> using USRP N210 platform along with commodity ZigBee, BLE, and LoRa devices. The extensive experiments demonstrate <i>WiCast</i> can achieve an efficient parallel transmission with the aggregated goodput up to <inline-formula><tex-math>$ 390.24 ;text{kbps}$</tex-math></inline-formula>.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9506-9523"},"PeriodicalIF":9.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036906","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}

{"title":"AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging","authors":"Senkang Hu;Zhengru Fang;Zihan Fang;Yiqin Deng;Xianhao Chen;Yuguang Fang;Sam Tak Wu Kwong","doi":"10.1109/TMC.2025.3564163","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564163","url":null,"abstract":"Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue and enhance the safety and efficiency of connected and autonomous vehicles (CAVs) at multi-lane merging zones, we propose a novel collaborative decision-making framework, named <italic>AgentsCoMerge</i>, to leverage large language models (LLMs). Specifically, we first design a scene observation and understanding module to allow an agent to capture the traffic environment. Then we propose a hierarchical planning module to enable the agent to make decisions and plan trajectories based on the observation and the agent’s own state. In addition, in order to facilitate collaboration among multiple agents, we introduce a communication module to enable the surrounding agents to exchange necessary information and coordinate their actions. Finally, we develop a reinforcement reflection guided training paradigm to further enhance the decision-making capability of the framework. Extensive experiments are conducted to evaluate the performance of our proposed method, demonstrating its superior efficiency and effectiveness for multi-agent collaborative decision-making under various ramp merging scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9791-9805"},"PeriodicalIF":9.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021343","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}

{"title":"Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks With Differential Privacy","authors":"Yu Yang;Kai Peng;Shangguang Wang;Xiaolong Xu;Peiyun Xiao;Victor C. M. Leung","doi":"10.1109/TMC.2025.3564301","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564301","url":null,"abstract":"As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9919-9933"},"PeriodicalIF":9.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021181","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}

{"title":"radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction From Millimeter-Wave Radar","authors":"Yuanyuan Zhang;Runwei Guan;Lingxiao Li;Rui Yang;Yutao Yue;Eng Gee Lim","doi":"10.1109/TMC.2025.3563945","DOIUrl":"https://doi.org/10.1109/TMC.2025.3563945","url":null,"abstract":"Radar-based cardiac monitoring has become a popular research direction recently, but the fine-grained electrocardiogram (ECG) signal is still hard to reconstruct from millimeter-wave radar signal. The key obstacle is to decouple cardiac activities in the electrical domain (i.e., ECG) from that in the mechanical domain (i.e., heartbeat), and most existing research only uses purely data-driven methods to map such domain transformation as a black box. Therefore, this work first proposes a signal model that considers the fine-grained cardiac feature sensed by radar, and a novel deep learning framework called radarODE is designed to extract both temporal and morphological features for generating ECG. In addition, ordinary differential equations are embedded in radarODE as a decoder to provide morphological prior, helping the convergence of the model training and improving the robustness under body movements. After being validated on the dataset, the proposed radarODE achieves better performance compared with the benchmark in terms of missed detection rate, root mean square error, Pearson correlation coefficient with improvements of 9%, 16% and 19%, respectively. The validation results imply that radarODE is capable of recovering ECG signals from radar signals with high fidelity and can potentially be implemented in real-life scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9806-9821"},"PeriodicalIF":9.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021357","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}