IEEE Transactions on Mobile Computing最新文献

{"title":"Utility-Enhanced Personalized Privacy Preservation in Hierarchical Federated Learning","authors":"Jianan Chen;Honglu Jiang;Qin Hu","doi":"10.1109/TMC.2025.3531919","DOIUrl":"https://doi.org/10.1109/TMC.2025.3531919","url":null,"abstract":"Federated learning (FL) is a distributed learning framework that allows clients to jointly train a model by uploading parameter updates rather than sharing local data. FL deployed on a client-edge-cloud hierarchical architecture, named Hierarchical Federated Learning (HFL), can accelerate model training and accommodate more clients with reduced communication cost via edge aggregation. Unfortunately, HFL suffers from privacy risks since the submitted parameters from clients are vulnerable to privacy attacks. To address this issue, we propose a novel Differential Privacy (DP) definition tailored for HFL, i.e., Group Local Differential Privacy (GLDP). We design the Sampling-Randomizing-Shuffling (SRS) mechanism to implement GLDP in HFL, where the sampling process is employed to achieve a stronger level of privacy protection with less noise added. By combining the randomized response and the shuffling mechanism, our proposed SRS mechanism can achieve client-level personalization within <inline-formula><tex-math>$rho _{k}$</tex-math></inline-formula>-GLDP for privacy preservation while balancing model performance and privacy protection in HFL. Privacy analysis and convergence analysis are conducted to provide theoretical performance guarantees. Experimental results based on real-world datasets verify the effectiveness of SRS.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5264-5279"},"PeriodicalIF":7.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918775","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":"Towards Resilience 5G-V2N: Efficient and Privacy-Preserving Authentication Protocol for Multi-Service Access and Handover","authors":"Ye Bi;Chunfu Jia","doi":"10.1109/TMC.2025.3532120","DOIUrl":"https://doi.org/10.1109/TMC.2025.3532120","url":null,"abstract":"The booming 5G cellular networks sparked tremendous interest in supporting more sophisticated critical use cases through vehicle-to-network (V2N) communications. However, the inherent technical vulnerabilities and densification of 5G raise new security and efficiency challenges. The existing secondary authentication fails to support multi-service access. The random access process lacks authentication of the gNB, possibly leading to fake base station attacks (FBS). Moreover, related research extends key forward/backward secrecy (KF/BS) to require that it also applies to gNBs, thus invalidating most existing schemes. This paper introduces a comprehensive security framework for 5G-V2N that seamlessly integrates with existing standardized architecture to provide privacy-preserving mutual authentication and key agreement for the full service cycle. Specifically, we propose new secondary authentication involving gNBs and support single request access to multi-services. Second, incorporating the service migration idea, we design the g2g (gNB-to-gNB) channel establishment phase to promote secure context share. Finally, the proposed efficient handover phase achieves the security properties of enhanced KF/BS, known randomness secrecy and privacy-preserving, and avoids FBS. We verify the proposed protocol using three different formal techniques: provably secure, BAN-logic, and AVISPA tool. Extensive experimental results and comparison show that our scheme excels in computational and communication efficiencies, and detecting malicious events.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5446-5463"},"PeriodicalIF":7.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929816","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":"DeExp: Revealing Model Vulnerabilities for Spatio-Temporal Mobile Traffic Forecasting With Explainable AI","authors":"Serly Moghadas Gholian;Claudio Fiandrino;Narseo Vallina-Rodríguez;Marco Fiore;Joerg Widmer","doi":"10.1109/TMC.2025.3531544","DOIUrl":"https://doi.org/10.1109/TMC.2025.3531544","url":null,"abstract":"The ability to perform mobile traffic forecasting effectively with Deep Neural Networks (DNN) is instrumental to optimize resource management in 5G and beyond generation mobile networks. However, despite their capabilities, these Deep Neural Networks (DNN)s often act as complex opaque-boxes with decisions that are difficult to interpret. Even worse, they have proven vulnerable to adversarial attacks which undermine their applicability in production networks. Unfortunately, although existing state-of-the-art EXplainable Artificial Intelligence (XAI) techniques are often demonstrated in computer vision and Natural Language Processing (NLP), they may not fully address the unique challenges posed by spatio-temporal time-series forecasting models. To address these challenges, we introduce <sc>DeExp</small> in this paper, a tool that flexibly builds upon legacy EXplainable Artificial Intelligence (XAI) techniques to synthesize compact explanations by making it possible to understand which Base Stations (BSs) are more influential for forecasting from a spatio-temporal perspective. Armed with such knowledge, we run state-of-the-art Adversarial Machine Learning (AML) techniques on those BSs to measure the accuracy degradation of the predictors under adversarial attacks. Our comprehensive evaluation uses real-world mobile traffic datasets and demonstrates that legacy XAI techniques spot different types of vulnerabilities. While Gradient-weighted Class Activation Mapping (GC) is suitable to spot BSs sensitive to moderate/low traffic injection, LayeR-wise backPropagation (LRP) is suitable to identify BSs sensitive to high traffic injection. Under moderate adversarial attacks, the prediction error of the BSs identified as vulnerable can increase by more than 250%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5245-5263"},"PeriodicalIF":7.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918646","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":"Rethinking the Effect of Sparse Data Completion on Sparse Mobile Crowdsensing Tasks","authors":"Yuanbo Xu;Jiawei Liu;En Wang;Bo Yang;Dongming Luan;Yongjian Yang;Jing Deng","doi":"10.1109/TMC.2025.3531362","DOIUrl":"https://doi.org/10.1109/TMC.2025.3531362","url":null,"abstract":"<monospace>M</monospace>obile <monospace>c</monospace>rowd<monospace>s</monospace>ensing (<monospace>MCS</monospace>) is a powerful technique that enables a variety of urban tasks, including temperature monitoring, location-based services, and urban path recommendations. However, these tasks often face the challenge of sparse and incomplete sensing data, undermining their effectiveness and reliability. <monospace>S</monospace>parse <monospace>d</monospace>ata <monospace>c</monospace>ompletion (<monospace>SDC</monospace>) methods have been developed to infer missing or unobserved data by leveraging spatio-temporal correlations to tackle this issue. This forms the core concept of the <monospace>s</monospace>parse <monospace>m</monospace>obile <monospace>c</monospace>rowd<monospace>s</monospace>ensing problem (<monospace>SMCS</monospace>), which aims to improve the performance of downstream tasks through inferred data. Despite the potential benefits, most existing SMCS methods fail to consider the trade-off between the cost of SDC and the benefits for downstream tasks. These methods often treat SDC and downstream tasks as independent modules, resulting in suboptimal outcomes. In this paper, we investigate the impact of SDC on the SMCS paradigm, both qualitatively and quantitatively. We establish the upper bound of performance achievable when applying SDC in SMCS under different levels of sensing data sparsity. Based on these studies and findings, we propose a practical and flexible framework called <monospace>SDC-EVA</monospace>, <monospace>S</monospace>ensing <monospace>D</monospace>ata <monospace>C</monospace>ompletion <monospace>EVA</monospace>luation framework. This framework allows for applying different SDC methods in SMCS, considering factors such as computing complexity, storage space, and associated costs. Our proposed framework allows researchers to assess the necessity and feasibility of integrating SDC into SMCS systems before designing and deploying them in real-world scenarios. This assessment can be tailored to specific data sparsity and contextual information. To validate the effectiveness of our proposed evaluation framework, we conduct experiments in various real-world scenarios involving different combinations of SDC and downstream tasks. The results demonstrate the superiority of our framework in improving the performance of SMCS. By presenting these findings, we aim to contribute to developing SMCS techniques and provide valuable insights for researchers and practitioners.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5094-5105"},"PeriodicalIF":7.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918797","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":"Enabling Ultralow-Latency Services With Ubiquitous Mobility by Means of a Compact Network Architecture","authors":"Guiliang Cai;Qiang Wu;Ran Wang;Lianyi Zhi;Xiaoming Fu;Hongke Zhang","doi":"10.1109/TMC.2025.3526971","DOIUrl":"https://doi.org/10.1109/TMC.2025.3526971","url":null,"abstract":"With the rapid development of emerging services such as cellular vehicle-to-everything and immersive video service, network connections have further evolved from tangible physical connections to intangible virtual connections such as content, services, and computing resources, and the application scenarios have become more abundant. The mobile ultra-service, which is characterized by ultra-low latency, ultra-high reliability, and ubiquitous mobility, is becoming one of the most representative traffic types. However, the existing mobile network architecture has not evolved sufficiently to meet the specific requirements of these mobile ultra-services, the mobility anchors introduce unnecessary node and link latency, leaving space for further optimization. A compact network architecture (ComArch) is proposed in this paper for ultralow-latency services with ubiquitous mobility. ComArch is designed with a mapping control plane and a generalized forwarding plane to collaboratively implement packet forwarding in mobile scenarios. The generalized forwarding plane handles packet forwarding, while the mapping control plane manages terminals’ identifier and locator mapping entries. The node latency introduced by mobility anchors is eliminated, and an efficient routing scheme is proposed to find the optimal mandatory nodes in the forwarding path, thereby reducing unnecessary link latency. Experimental results show that ComArch can effectively reduce end-to-end delay while saving resources.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"4858-4873"},"PeriodicalIF":7.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925323","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":"nCTX: A Neural Network-Powered Lossless Compressive Transmission Using Shared Information","authors":"Wooseung Nam;Sungyong Lee;Joohyun Lee;Kyunghan Lee","doi":"10.1109/TMC.2025.3530950","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530950","url":null,"abstract":"In this work, we explore the possibility of a new delivery method for lossless data, namely compressive transmission. It aims at minimizing the transmission data volume at runtime by exploiting the tailored information shared between the sender and the receiver. There are two approaches to leverage shared information for compression: 1) using a DNN-based codec as a proxy for shared information and 2) applying redundancy elimination using deduplication. However, these approaches have not been studied in depth to utilize the trade-off between the compression rate and the amount of shared information. Compared to these approaches, compressive transmission is unique as it fully leverages the abundance of information available on both sides, which is chosen and placed purposely. To bring the concept to reality, we propose nCTX, a neural network-powered Compressive Transmission System that adaptively exploits a generative model and matching blocks. nCTX extracts the optimal semantic data from the input data, exploiting shared information to closely imitate the original and compensate it with the offset (i.e., difference). Extensive evaluations in mobile platforms confirm that nCTX reduces the transmission volume significantly by 25.8% and 23.3% compared to FLIF and RC, the state-of-the-art image codecs, respectively, in comparable or shorter computation times.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5386-5399"},"PeriodicalIF":7.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929725","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":"Intelligent End-to-End Deterministic Scheduling Across Converged Networks","authors":"Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen","doi":"10.1109/TMC.2025.3530486","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530486","url":null,"abstract":"Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2504-2518"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563922","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":"Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning","authors":"Rahul Mishra;Hari Prabhat Gupta","doi":"10.1109/TMC.2025.3530818","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530818","url":null,"abstract":"The popularity of wearable smart devices has increased due to their seamless monitoring of vital signs during daily activities. Federated learning leverages these devices along with participants’ smartphones to fine-tune pre-trained models. Moreover, calibrating the differences between wearables and smartphones in terms of sampling rates, orientations, activity correlation, battery power, and other factors is challenging. Thus, the paper introduces a participant and wearable selection cross-device federated learning approach. It leverages criteria such as the activity wearable(s) relationship, data quality, battery life, sampling rate, and so on to perform the wearable selection. The server evaluates and estimates the utility of each participant and selects those with higher utility in each communication round. We then figure out the optimal weighted contribution of each participant to perform robust aggregation. We also use knowledge distillation techniques to develop a high-performing and lightweight wearable model. Finally, we conduct simulation and real-world experiments on existing datasets and compare our approach with state-of-the-art. The result shows an improvement of <inline-formula><tex-math>$3!!-!!4%$</tex-math></inline-formula> in accuracy via fine-tuning from selected wearable data.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5003-5015"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918777","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":"LaserKey: Eavesdropping Keyboard Typing Leveraging Vibrational Emanations via Laser Sensing","authors":"Chengwen Luo;Zhuoqing Xie;Yuhan Huang;Gecheng Chen;Haiyi Yao;Jin Zhang;Long Cheng;Weitao Xu;Jianqiang Li","doi":"10.1109/TMC.2025.3529919","DOIUrl":"https://doi.org/10.1109/TMC.2025.3529919","url":null,"abstract":"Reconstructing keyboard input through side-channel attacks has posed significant threats to user security. While conventional keystroke eavesdropping attacks have demonstrated effectiveness using side channels such as acoustic signals, they are usually shorter in range and can be significantly affected by environmental noises. In this paper, we propose <italic>LaserKey</i>, a novel keystroke eavesdropping technique that leverages the long-range and noise-resistant nature of lasers to achieve a more stealthy side-channel attack. We utilize laser sensors to accurately capture the subtle vibrations induced on laptop screens by keystrokes, and innovatively design a laser-driven deep learning-based keystroke recognition model with the inputs being the Mel-frequency Cepstral Coefficien (MFCC), Time Difference of Arrival (TDoA), and amplitude features extracted from such vibration signals. Through systematic experiments, we demonstrate that <italic>LaserKey</i> achieves a 92.2% single-key recognition accuracy. By combining multiple single-key recognition capabilities based on this, we then realize the end-to-end word-level recognition. Moreover, to mitigate the recognition errors caused by the changes in keystroke positions, we introduce a meta-learning based domain generalization approach for achieving robust laser position calibration. Results show that <italic>LaserKey</i> achieves as low as 3% character error rate (CER) for word-level recognition, proving its effectiveness for long-range and high-accuracy keystroke eavesdropping, and highlighting the necessity for countermeasures in the future.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"4829-4844"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925287","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":"Distributed MAC for RIS-Assisted Multiuser Networks: CSMA/CA Protocol Design and Statistical Optimization","authors":"Zhou Zhang;Saman Atapattu;Yizhu Wang;Marco Di Renzo","doi":"10.1109/TMC.2025.3530851","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530851","url":null,"abstract":"This research focuses on the challenges of distributed Medium Access Control (MAC) protocols involving Reconfigurable Intelligent Surfaces (RISs), which are still in early development. The study explores optimal channel access for multiple source-destination pairs in distributed networks with the assistance of multiple RISs. Three key issues are addressed: joint scheme for channel contention, RISs’ channel state information (CSI) acquisition, and RIS-assisted channel access; tradeoff between overhead and effective data transmission; and low-complexity distributed network operation. To achieve maximum network throughput, the paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance based on statistical optimization. The proposed MAC strategy's optimality in terms of average network throughput is rigorously derived. Closed-form expressions for threshold functions of rewards for making decisions are derived, and an easy-to-implement distributed channel access algorithm is provided with online complexity <inline-formula><tex-math>$mathcal {O}(2^{L})$</tex-math></inline-formula>, where <inline-formula><tex-math>$L$</tex-math></inline-formula> denotes the number of distributed RISs. The proposed MAC strategy is then refined, and a low-complexity distributed algorithm is developed with complexity <inline-formula><tex-math>$mathcal {O}(L)$</tex-math></inline-formula>. Numerical simulations verify the theoretical results, demonstrating the efficiency of the proposed strategy. This work introduces innovative solutions and analytical frameworks for the distributed MAC problem with RIS assistance, significantly advancing existing research.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"4698-4715"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925190","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}