IEEE Transactions on Mobile Computing最新文献

{"title":"EPREAR:An Efficient Attribute-Based Proxy Re-Encryption Scheme With Fast Revocation for Data Sharing in AIoT","authors":"Xiaoxiao Li;Yong Xie;Cong Peng;Entao Luo;Xiong Li;Zhili Zhou","doi":"10.1109/TMC.2025.3573288","DOIUrl":"https://doi.org/10.1109/TMC.2025.3573288","url":null,"abstract":"The Artificial Intelligence of Things (AIoT) is driving human society from “information” to “intelligence”, and the information technology industry is undergoing tremendous changes. However, AIoT data faces security threats such as leakage and illegal access when assisted by third parties. Therefore, some scholars use attribute-based proxy re-encryption (ABPRE) for secure sharing of data. However, the existing ABPRE schemes suffer from high computational overhead and inefficient attribution revocation, which seriously hinders practical application. To solve these problems, in this paper, we propose an efficient attribute-based proxy re-encryption scheme with fast attribute revocation (EPREAR). We design a non-interactive zero-knowledge proof protocol based on blockchain to ensure the verifiability of the key during attribute revocation. Furthermore, we devise a boundless encryption and decryption mechanism to enable the system’s encryption and decryption with a fixed computation overhead, regardless of the size of the attribute set. And EPREAR possesses the ability to add infinite attributes without re-initializing the system. Finally, we perform theoretical and experimental analyses that show EPREAR has excellent computational performance. As a consequence, it has better application value in AIoT.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11005-11018"},"PeriodicalIF":9.2,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021305","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":"QoS-Aware Intelligence Information Sharing Requests Scheduling in IoV: CPO-Based Modeling and Solution","authors":"Yang Gao;Wenjun Wu;Ao Sun;Yang Sun;Teng Sun;Pengbo Si","doi":"10.1109/TMC.2025.3565898","DOIUrl":"https://doi.org/10.1109/TMC.2025.3565898","url":null,"abstract":"With the accelerated development of autonomous driving and large language model, blockchain-supported data interaction and artificial intelligence (AI)-assisted performance optimization is the current mainstream research in the Internet of Vehicles (IoV). However, the trial-and-error behavior of the AI algorithm during the training process is a threat to road safety. Therefore, this paper proposes a general constrained policy optimization (CPO)-based modeling and solution for high-dimensional constrained optimization problems. We focus on intelligent driving information sharing in blockchain-enhanced IoV and optimize the service rewards in the sharing requests scheduling problem while ensuring the frequency resource limitation, service quality constraint, and road safety constraint. The constrained state space (CSS) is innovatively proposed to abstract the environment mathematically with the definition of constraint hyperplanes and distance. Accordingly, the constrained Markov Decision process (CMDP) and the optimization problem are formulated. With the practical implementation of the CPO theory, the constrained sharing requests scheduling (CSRS) algorithm is proposed. Ablation experiments are deep reinforcement learning-based methods without using the CSS-based constraint modeling or without using the CPO-based constrained problem solving process. Results show the effectiveness of CSS and CSRS algorithm in improving the policy training efficiency, and the testing results shows excellent generalization ability.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9636-9649"},"PeriodicalIF":9.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036729","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":"AegisRAN: A Fair and Energy-Efficient Computing Resource Allocation Framework for vRANs","authors":"Ethan Sanchez Hidalgo;Jose A. Ayala-Romero;Josep Xavier Salvat Lozano;Andres Garcia-Saavedra;Xavier Costa Perez","doi":"10.1109/TMC.2025.3564116","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564116","url":null,"abstract":"The virtualization of Radio Access Networks (vRAN) is rapidly becoming a reality, driven by the increasing need for flexible, scalable, and cost-effective mobile network solutions. To mitigate energy efficiency concerns in vRAN deployments, two approaches are gaining attention: (<inline-formula><tex-math>$i$</tex-math></inline-formula>) sharing computing infrastructure among multiple virtualized base stations (vBSs); and (<inline-formula><tex-math>$ii$</tex-math></inline-formula>) relying upon general-purpose, low-cost CPUs. However, effectively realizing these approaches poses several challenges. In this paper, we first conduct a comprehensive experimental campaign on a vRAN platform to characterize the impact of computing and radio resource allocation on energy consumption and performance across various network contexts. This analysis reveals several key issues. First, determining the optimal allocation of computing resources is difficult because it depends on the context of each vBS (e.g., traffic load, channel quality) in a non-trivial and non-linear manner. Second, suboptimal resource assignment can lead to increased energy consumption or, even worse, degradation of users’ Quality of Service. Third, the high dimensionality of the solution space hinders the effectiveness of traditional optimization or learning methods. To tackle these challenges, we propose AegisRAN, a framework for optimizing computing resource allocation in vRAN. AegisRAN addresses the dual objective of minimizing energy consumption while maintaining high system reliability. Moreover, when computing resources are overbooked, our solution ensures a fair resource partition based on vBS performance. AegisRAN leverages a discrete soft actor-critic algorithm combined with several techniques, including multi-step decision-making, action masking, digital twin-based training, and a tailored reward signal that mitigates feedback sparsity. Our evaluations demonstrate that AegisRAN achieves near-optimal performance and offers high flexibility across diverse network contexts and varying numbers of vBSs, with up to 25% improvement in energy savings compared to baseline solutions in medium-scale scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9441-9457"},"PeriodicalIF":9.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036215","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":"Hierarchical Split Federated Learning: Convergence Analysis and System Optimization","authors":"Zheng Lin;Wei Wei;Zhe Chen;Chan-Tong Lam;Xianhao Chen;Yue Gao;Jun Luo","doi":"10.1109/TMC.2025.3565509","DOIUrl":"https://doi.org/10.1109/TMC.2025.3565509","url":null,"abstract":"As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, <italic>split federated learning</i> (SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention from the research community in recent years. Nevertheless, most prior works on SFL focus only on a two-tier architecture without harnessing multi-tier cloud-edge computing resources. In this paper, we intend to analyze and optimize the learning performance of SFL under multi-tier systems. Specifically, we propose the hierarchical SFL (HSFL) framework and derive its convergence bound. Based on the theoretical results, we formulate a joint optimization problem for model splitting (MS) and model aggregation (MA). To solve this rather hard problem, we then decompose it into MS and MA sub-problems that can be solved via an iterative descending algorithm. Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA in multi-tier systems and significantly outperform existing schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9352-9367"},"PeriodicalIF":9.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036839","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":"Sonicumos: An Enhanced Active Face Liveness Detection System via Ultrasonic and Video Signals","authors":"Yihao Wu;Peipei Jiang;Jianhao Cheng;Lingchen Zhao;Chao Shen;Cong Wang;Qian Wang","doi":"10.1109/TMC.2025.3565689","DOIUrl":"https://doi.org/10.1109/TMC.2025.3565689","url":null,"abstract":"<sc>Sonicumos</small> is an enhanced behavior-based face liveness detection system that combines ultrasonic and video signals to sense the 3D head gestures. As face authentication becomes increasingly prevalent, the need for a reliable liveness detection system is paramount. Traditional behavior-based liveness detection methods (e.g., eye-blinking, nodding, etc.), which are widely deployed in mission-critical scenarios like finance and banking applications today, are prone to advanced media-based facial forgery attacks. <sc>Sonicumos</small> aims to incorporate the traditional behavior-based method for active liveness detection without introducing extra user burden. By employing ultrasonic signals, <sc>Sonicumos</small> capitalizes on the head gestures, significantly raising the security bar. Our approach utilizes the frequency-modulated continuous-wave (FMCW) ultrasonic radar for robust 3D gesture recognition compatible with face authentication. We also propose a new dual-feature fusion network that integrates audio and video features at the feature level to increase detection accuracy and resilience against numerous attacks. Our prototype has been tested on seven off-the-shelf Android/iOS smartphones, achieving an overall detection accuracy of 95.83% at an equal error rate (EER) of 4.96% when dealing with 3D impersonation attacks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9883-9901"},"PeriodicalIF":9.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021316","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":"Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables","authors":"Taesik Gong;SiYoung Jang;Utku Günay Acer;Fahim Kawsar;Chulhong Min","doi":"10.1109/TMC.2025.3564314","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564314","url":null,"abstract":"The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrated into wearable devices, these accelerators open exciting opportunities, allowing various AI apps to run directly on the body. We present <italic>Synergy</i> that provides AI apps with best-effort performance via system-driven holistic collaboration over AI accelerator-equipped wearables. To achieve this, Synergy provides device-agnostic programming interfaces to AI apps, giving the system visibility and controllability over the app's resource use. Then, Synergy maximizes the inference throughput of concurrent AI models by creating various execution plans for each app considering AI accelerator availability and intelligently selecting the best set of execution plans. Synergy further improves throughput by leveraging parallelization opportunities over multiple computation units. Our evaluations with 7 baselines and 8 models demonstrate that, on average, Synergy achieves a 23.0× improvement in throughput, while reducing latency by 73.9% and power consumption by 15.8%, compared to the baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9319-9333"},"PeriodicalIF":9.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036933","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":"BAT: A Versatile Bipartite Attention-Based Approach for Comprehensive Truth Inference in Mobile Crowdsourcing","authors":"Jiacheng Liu;Feilong Tang;Hao Liu;Long Chen;Yichuan Yu;Yanmin Zhu;Jiadi Yu;Xiaofeng Hou;Pheng-Ann Heng","doi":"10.1109/TMC.2025.3563345","DOIUrl":"https://doi.org/10.1109/TMC.2025.3563345","url":null,"abstract":"The proliferation of smart mobile devices has catalyzed the growth of Mobile CrowdSourcing (MCS) as a distributed problem-solving paradigm. MCS platforms heavily rely on advanced truth inference techniques to extract reliable information from diverse and potentially noisy crowd-contributed data. Existing truth inference models often made simplified assumptions about workers or tasks, employing complex Bayesian models or stringent data aggregation methods. These approaches tend to be task-specific, primarily limited to categorical labeling, making adaptations to other mobile computing scenarios labor-intensive. To address these limitations, we introduce the Bipartite Attention-driven Truth (BAT), a versatile approach tailored for mobile computing environments. BAT utilizes an Attributed Bipartite Graph (ABG) to holistically model the MCS process, with workers and tasks as nodes connected by edges representing answer-specific attributes. The approach employs a bipartite graph neural network with an innovative attention mechanism to assess the importance of different answers. BAT extends beyond categorical tasks to support numerical ones by incorporating novel feature representations and model extensions. Theoretical analyses clarify the link between answer similarity and worker expertise. Extensive experiments using diverse real-world datasets demonstrate BAT's superior performance compared to state-of-the-art categorical and numerical truth inference models, highlighting its effectiveness in mobile computing scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9368-9382"},"PeriodicalIF":9.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036773","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":"Hierarchical Index Retrieval-Driven Wireless Network Intent Translation With LLM","authors":"Jingyu Wang;Lingqi Guo;Jianyu Wu;Caijun Yan;Haifeng Sun;Lei Zhang;Zirui Zhuang;Qi Qi;Jianxin Liao","doi":"10.1109/TMC.2025.3564937","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564937","url":null,"abstract":"Intent-Based Networking (IBN) represents an emerging network management concept that is designed to fulfill user service requirements through automation. At its core, IBN is capable of translating user intent into network policies, thereby enabling automated configuration and management. However, the application of IBN has been limited by challenges associated with automation and intelligence. The recent widespread adoption of Large Language Model (LLM) has partially mitigated these issues. Nonetheless, hardware heterogeneity and high dynamic networks remain significant challenges for IBN: <italic>(i)</i> Devices from different vendors are challenging to manage uniformly; <italic>(ii)</i> Aligning service demands with rapidly changing network status is difficult. To address these challenges, we propose LIT, a framework of <underline>L</u>LM-empowered <underline>I</u>ntent <underline>T</u>ranslation with manual guidance. LIT incorporates Retrieval-Augmented Generation (RAG) to reference hardware manuals and enhance the generation results of LLMs. To reduce noise from retrieval results, we optimized the general RAG process. Additionally, LIT introduces MoE (Mixture of Experts) to adjust parameter values according to network status by synthesizing results from multiple expert models. Experiments demonstrate that LIT alleviates the challenges faced by IBN, achieving a 57.5% improvement in F1 score compared to the baseline.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9837-9851"},"PeriodicalIF":9.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021238","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":"Compressed Private Aggregation for Scalable and Robust Federated Learning Over Massive Networks","authors":"Natalie Lang;Nir Shlezinger;Rafael G. L. D’Oliveira;Salim El Rouayheb","doi":"10.1109/TMC.2025.3564390","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564390","url":null,"abstract":"Federated learning (FL) is an emerging paradigm that allows a central server to train machine learning models using remote users’ data. Despite its growing popularity, Federated learning (FL) faces challenges in preserving the privacy of local datasets, its sensitivity to poisoning attacks by malicious users, and its communication overhead, especially in large-scale networks. These limitations are often individually mitigated by local differential privacy (LDP) mechanisms, robust aggregation, compression, and user selection techniques, which typically come at the cost of accuracy. In this work, we present <italic>compressed private aggregation (CPA)</i>, allowing massive deployments to simultaneously communicate at extremely low bit rates while achieving privacy, anonymity, and resilience to malicious users. CPA randomizes a codebook for compressing the data into a few bits using nested lattice quantizers, while ensuring anonymity and robustness, with a subsequent perturbation to hold LDP. CPA-aided FL is proven to converge in the same asymptotic rate as FL without privacy, compression, and robustness considerations, while satisfying both anonymity and LDP requirements. These analytical properties are empirically confirmed in a numerical study, where we demonstrate the performance gains of CPA compared with separate mechanisms for compression and privacy, as well as its robustness in mitigating the harmful effects of malicious users.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9934-9950"},"PeriodicalIF":9.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021371","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":"WiCG: In-Body Cardiac Motion Sensing Based on a Mix-Medium Wi-Fi Fresnel Zone Model","authors":"Pei Wang;Anlan Yu;Xujun Ma;Rong Zheng;Jingfu Dong;Zhaoxin Chang;Duo Zhang;Djamal Zeghlache;Daqing Zhang","doi":"10.1109/TMC.2025.3564843","DOIUrl":"https://doi.org/10.1109/TMC.2025.3564843","url":null,"abstract":"Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, highlighting the critical need for accurate and continuous heart health monitoring. Electrocardiograms (ECG), considered as the golden standard for diagnosing and monitoring heart-related conditions, offer precise measurements but require direct skin contact, limiting their practicality for long-term and everyday use. On the other hand, existing RF sensing techniques that analyze signals reflected off the skin struggle to distinguish micro cardiac motions of the heart due to weak motion amplitude and respiration interference at the chest wall. To overcome these limitations, we introduce WiCG, a novel contact-less cardiac motion monitoring system that employs 2.4 GHz Wi-Fi signals to penetrate the chest and detect subtle cardiac movements. A mix-medium Wi-Fi Fresnel zone model is developed to explain the enhanced phase sensitivity of in-body Wi-Fi signals, which is crucial for accurately detecting cardiac motions. By strategically positioning antennas near the heart, WiCG captures ventricular motions effectively. A novel cardiac Doppler method is proposed to suppress phase noise and interference from static paths and extract the time interval between the systole and diastole of the ventricular. Extensive experiments demonstrate that the proposed system can robustly estimate the R-R and Q-T intervals of human cardiac cycles across 21 subjects and different environments with an average accuracy of 99.22% and 92.8%, achieving performance comparable to ECG.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9524-9538"},"PeriodicalIF":9.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036884","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}