IEEE Transactions on Mobile Computing最新文献_第3页

On User Scheduling for Fixed Wireless Access via Channel Statistics 基于信道统计的固定无线接入用户调度研究

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-01 DOI: 10.1109/TMC.2024.3524565

Xin Guan;Zhixing Chen;Yibin Kang;Qi Yan;Qingjiang Shi

{"title":"On User Scheduling for Fixed Wireless Access via Channel Statistics","authors":"Xin Guan;Zhixing Chen;Yibin Kang;Qi Yan;Qingjiang Shi","doi":"10.1109/TMC.2024.3524565","DOIUrl":"https://doi.org/10.1109/TMC.2024.3524565","url":null,"abstract":"Conventional multi-user scheduling in cellular networks are required to make a decision every transmission time interval (TTI) of at most several milliseconds. Only quite simple schemes can be implemented under the stringent time constraint, resulting in far-from-optimum performance. In this paper, we focus on the case of scheduling multiple users in a fixed wireless access (FWA) network with stable channel characteristics. We propose a scheduling approach by which a high-quality scheduling decision based on statistical channel state information (CSI) is made across all TTIs instead of making simple TTI-level decisions. The proposed design is essentially a mixed-integer non-smooth non-convex stochastic problem. We first replace the indicator functions in the formulation by smooth sigmoid functions to tackle nonsmoothness. By leveraging deterministic equivalents (D.E.), we then convert the original stochastic problem into an approximated deterministic one, followed by linear relaxation of the integer constraints. However, the converted problem is still nonconvex due to implicit equation constraints formerly introduced by D.E. Therefore, we employ implicit optimization technique to compute the gradient explicitly, with which we further propose an algorithm design based on a modified version of Frank–Wolfe method. Numerical results verify the effectiveness of our proposed scheme.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4040-4052"},"PeriodicalIF":7.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783256","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

NeuroBalancer: Balancing System Frequencies With Punctual Laziness for Timely and Energy-Efficient DNN Inferences 神经平衡：平衡系统频率与准时懒惰的及时和节能DNN推理

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-01 DOI: 10.1109/TMC.2024.3524628

Kyungmin Bin;Seyeon Kim;Sangtae Ha;Song Chong;Kyunghan Lee

{"title":"NeuroBalancer: Balancing System Frequencies With Punctual Laziness for Timely and Energy-Efficient DNN Inferences","authors":"Kyungmin Bin;Seyeon Kim;Sangtae Ha;Song Chong;Kyunghan Lee","doi":"10.1109/TMC.2024.3524628","DOIUrl":"https://doi.org/10.1109/TMC.2024.3524628","url":null,"abstract":"On-device deep neural network (DNN) inference is often desirable for user experience and privacy. Existing solutions have fully utilized resources to minimize inference latency. However, they result in severe energy inefficiency by completing DNN inference much earlier than the required service interval. It poses a new challenge of how to make DNN inferences in a punctual and energy-efficient manner. To tackle this challenge, we propose a new resource allocation strategy for DNN processing, namely <italic>punctual laziness that disperses its workload as efficiently as possible over time within its strict delay constraint. This strategy is particularly beneficial for neural workloads since a DNN comprises a set of popular operators whose latency and energy consumption are predictable. Through this understanding, we propose NeuroBalancer, an operator-aware core and memory frequency scaling framework that balances those frequencies as efficiently as possible while making timely inferences. We implement and evaluate NeuroBalancer on off-the-shelf Android devices with various state-of-the-art DNN models. Our results show that NeuroBalancer successfully meets a given inference latency requirements while saving energy consumption up to 43.9% and 21.1% compared to the Android’s default governor and up to 42.1% and 18.6% compared to SysScale, the state-of-the-art mobile governor on CPU and GPU, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4339-4354"},"PeriodicalIF":7.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786329","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

Intelligent Task Offloading and Resource Allocation in Knowledge Defined Edge Computing Networks 知识定义边缘计算网络中的智能任务卸载与资源分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-27 DOI: 10.1109/TMC.2024.3522253

Chuangchuang Zhang;Qiang He;Fuliang Li;Keping Yu

{"title":"Intelligent Task Offloading and Resource Allocation in Knowledge Defined Edge Computing Networks","authors":"Chuangchuang Zhang;Qiang He;Fuliang Li;Keping Yu","doi":"10.1109/TMC.2024.3522253","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522253","url":null,"abstract":"As an emerging architecture, edge computing enables resource limited terminal devices to offload their computation tasks to edge servers in the vicinity, to efficiently reduce delay and energy consumption. However, the continuous expansion of network scale and rapid growth of network traffic in recent years have brought huge challenges to task offloading and resource allocation. To tackle the challenges, by integrating Knowledge Defined Networking (KDN) and edge computing technologies, we design a novel Knowledge defined Edge Computing (KEC) architecture, to achieve intelligent resource allocation and task offloading in dynamic large-scale edge computing networks. We formulate the task offloading and resource allocation optimization problem, to minimize delay and energy consumption, by considering resource requirements and controller deployment. To solve it, we present an intelligent Resource Allocation based Task Offloading (TORA) mechanism, where a Multi-Agent SD3 based resource allocation (MASD3) algorithm is devised to perform efficient resource allocation. To adapt to the rapid expansion of network scale, we design a resource Allocation based Controller Deployment and task offloading Decision (DACD) algorithm, to perform the optimal controller deployment and task offloading. Extensive simulation experiments demonstrate the effectiveness and efficiency of our proposed solution, and TORA mechanism outperforms comparison mechanisms on delay and energy consumption.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4312-4325"},"PeriodicalIF":7.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786335","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

Resource Allocation for the Uplink of a Multi-User Massive MIMO System 多用户大规模MIMO系统上行链路的资源分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-26 DOI: 10.1109/TMC.2024.3522207

Haseen Rahman;Catherine Rosenberg

{"title":"Resource Allocation for the Uplink of a Multi-User Massive MIMO System","authors":"Haseen Rahman;Catherine Rosenberg","doi":"10.1109/TMC.2024.3522207","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522207","url":null,"abstract":"We study the uplink resource management of a multi-user multiple-input-multiple-output single cell for Zero-Forcing receive combining transmission. We consider jointly power allocation, user selection and modulation and coding scheme selection over multiple subchannels. Our contributions are twofold: we first propose a quasi-optimal offline algorithm that provides a target performance and then design and validate an efficient online proportional fair algorithm that performs the above steps. Due to user power constraints, the offline optimization is conducted jointly for all subchannels within a time slot, a computationally intensive task, prompting the proposal of a greedy offline algorithm that we validate in two ways: 1) for a small number of users, by solving the general problem to quasi-optimality and 2) for a larger number of users, by solving again to quasi-optimality a transformed version of the general problem when the channels are assumed flat. From the offline study, we find that, given the right user selection, equal power allocation can be employed without much degradation in performance. We also see that the number of channels allocated to users varies widely depending upon their channel gains. Using these insights, we propose our efficient real-time online algorithm that has runtime competitiveness with a state-of-the-art benchmark.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4326-4338"},"PeriodicalIF":7.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786385","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

Laser-Powered UAV Trajectory and Charging Optimization for Sustainable Data-Gathering in the Internet of Things 面向物联网可持续数据采集的激光无人机轨迹与充电优化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-26 DOI: 10.1109/TMC.2024.3523281

Yue-Shiuan Liau;Y.-W. Peter Hong;Jang-Ping Sheu

{"title":"Laser-Powered UAV Trajectory and Charging Optimization for Sustainable Data-Gathering in the Internet of Things","authors":"Yue-Shiuan Liau;Y.-W. Peter Hong;Jang-Ping Sheu","doi":"10.1109/TMC.2024.3523281","DOIUrl":"https://doi.org/10.1109/TMC.2024.3523281","url":null,"abstract":"This work examines the trajectory design and energy charging strategy of a data-gathering unmanned aerial vehicle (UAV). The UAV utilizes laser charging from high-altitude platforms (HAPs) to replenish its battery, enabling sustained travel across multiple data-gathering points. The trajectory is determined by a sequence of hovering positions at which the UAV stays to perform both data collection and energy charging. The UAV's hovering positions affect both the sensors’ transmission rates and the laser-charging efficiency. To minimize the total task completion time, it is necessary to choose hovering positions that consider both data upload and energy charging times. In this work, we first propose the Minimum Completion Time Trajectory and Charging Optimization (MinTime-TCO) algorithm, where the hovering positions and charging energies are optimized in turn using a block coordinate descent approach. Given the UAV's hovering positions, we propose the Minimum Charge Rate Search (MCRS) algorithm to optimize the charging energies at these positions. We show that MCRS is optimal in terms of minimizing the total task completion time. Then, given the charging energies, we propose the Hovering Position Optimization (HPO) algorithm, employing successive convex approximation to address the non-convexity of the optimization problem. We also propose a low-complexity alternative based on dynamic programming to further reduce computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms against several baseline strategies.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4278-4295"},"PeriodicalIF":7.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-Enabled Multi-Agent Reinforcement Learning 基于动态切换的多智能体强化学习的多auv协同水下多目标跟踪

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3521889

Shengbo Wang;Chuan Lin;Guangjie Han;Shengchao Zhu;Zhixian Li;Zhenyu Wang;Yunpeng Ma

{"title":"Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-Enabled Multi-Agent Reinforcement Learning","authors":"Shengbo Wang;Chuan Lin;Guangjie Han;Shengchao Zhu;Zhixian Li;Zhenyu Wang;Yunpeng Ma","doi":"10.1109/TMC.2024.3521889","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521889","url":null,"abstract":"In recent years, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration or underwater tracking, etc. In this paper, we propose a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account. We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process. Then, based on software-defined networking (SDN), we regard the AUV swarm as a underwater ad-hoc network and propose a hierarchical software-defined multi-AUV reinforcement learning (HSARL) architecture. Based on the proposed HSARL architecture, we propose the “Dynamic-Switching” mechanism, it includes “Dynamic-Switching Attention” and “Dynamic-Switching Resampling” mechanisms which accelerate the HSARL algorithm's convergence speed and effectively prevents it from getting stuck in a local optimum state. Additionally, we introduce the reward reshaping mechanism for further accelerating the convergence speed of the proposed HSARL algorithm in early phase. Finally, based on a proposed AUV classification method, we propose a cooperative tracking algorithm called <bold>Dynamic-<bold>Switching-<bold>Based <bold>MARL (DSBM)-driven tracking algorithm. Evaluation results demonstrate that our proposed DSBM tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of various important metrics.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4296-4311"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786382","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

Reliability-Optimal UAV-Assisted Mobile Edge Computing: Joint Resource Allocation, Data Transmission Scheduling and Motion Control 可靠性优化的无人机辅助移动边缘计算：联合资源分配、数据传输调度和运动控制

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3521934

Jianshan Zhou;Mingqian Wang;Daxin Tian;Kaige Qu;Guixian Qu;Xuting Duan;Xuemin Shen

{"title":"Reliability-Optimal UAV-Assisted Mobile Edge Computing: Joint Resource Allocation, Data Transmission Scheduling and Motion Control","authors":"Jianshan Zhou;Mingqian Wang;Daxin Tian;Kaige Qu;Guixian Qu;Xuting Duan;Xuemin Shen","doi":"10.1109/TMC.2024.3521934","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521934","url":null,"abstract":"Uncrewed aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC) within space-air-ground integrated networks. They serve as aerial cloudlets, enabling task processing in close proximity to ground users. While numerous joint trajectory design and resource allocation schemes aim to enhance energy efficiency or computation rate, few focus on improving system reliability, which is often challenged by stochastic channels and node mobility. This paper presents a stochastic modeling perspective to derive a system reliability expression. Our reliability formulation incorporates the impacts of stochastic Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) air-to-ground communication channels, application data load, available bandwidth, offloading time, and transmission power. This comprehensive approach leads to a reliability-oriented joint optimization model that considers not only resource allocation and user data transmission scheduling but also the motion of UAVs. To solve this problem, we propose a low-complexity algorithm. By utilizing augmented Lagrangian multipliers, the algorithm transforms nonlinear constraints into a tractable formulation, enabling the utilization of legacy unconstrained optimization techniques. We provide a proof of convergence for this algorithm. Through simulations, we demonstrate that our proposed method guarantees convergence within finite iterations and improves the average communication reliability in comparison with several other joint optimization schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4217-4234"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783281","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

Noise-Robust Federated Learning With Model Heterogeneous Clients 模型异构客户端的噪声鲁棒联邦学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3522573

Xiuwen Fang;Mang Ye

{"title":"Noise-Robust Federated Learning With Model Heterogeneous Clients","authors":"Xiuwen Fang;Mang Ye","doi":"10.1109/TMC.2024.3522573","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522573","url":null,"abstract":"Federated Learning (FL) enables multiple devices to collaboratively train models without sharing their raw data. Considering that clients may prefer to design their own models independently, model heterogeneous FL has emerged. Additionally, due to the annotation uncertainty, the collected data usually contain unavoidable and varying noise, which cannot be effectively addressed by existing FL algorithms. This paper presents a novel solution that simultaneously handles model heterogeneity and label noise in a single framework. It is featured in three aspects: (1) For the communication between heterogeneous models, we directly align the model feedback by utilizing the easily-accessible public data, which does not require additional global models or relevant data for collaboration. (2) For internal label noise in each client, we design a dynamic label refinement strategy to mitigate the negative effects. (3) For challenging noisy feedback from other participants, we design an enhanced client confidence re-weighting scheme, which adaptively assigns corresponding weights to each client in the collaborative learning stage. Extensive experiments validate the effectiveness of our approach in mitigating the negative effects of various noise rates and types under both model homogeneous and heterogeneous FL settings.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4053-4071"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783238","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

FairSTG: Countering Performance Heterogeneity via Collaborative Sample-Level Optimization FairSTG：通过协作样本级优化来对抗性能异质性

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-25 DOI: 10.1109/TMC.2024.3522476

Gengyu Lin;Zhengyang Zhou;Qihe Huang;Kuo Yang;Shifen Cheng;Yang Wang

{"title":"FairSTG: Countering Performance Heterogeneity via Collaborative Sample-Level Optimization","authors":"Gengyu Lin;Zhengyang Zhou;Qihe Huang;Kuo Yang;Shifen Cheng;Yang Wang","doi":"10.1109/TMC.2024.3522476","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522476","url":null,"abstract":"Spatiotemporal learning plays a crucial role in mobile computing techniques to empower smart cites. While existing research has made great efforts to achieve accurate predictions on the overall dataset, they still neglect the significant performance heterogeneity across samples. In this work, we designate the performance heterogeneity as the reason for unfair spatiotemporal learning, which not only degrades the practical functions of models, but also brings serious potential risks to real-world urban applications. To fix this gap, we propose a model-independent Fairness-aware framework for SpatioTemporal Graph learning (FairSTG), which inherits the idea of exploiting advantages of well-learned samples to challenging ones with collaborative mix-up. Specifically, FairSTG consists of a spatiotemporal feature extractor for model initialization, a collaborative representation enhancement for knowledge transfer between well-learned samples and challenging ones, and fairness objectives for immediately suppressing sample-level performance heterogeneity. Experiments on four spatiotemporal datasets demonstrate that our FairSTG significantly improves the fairness quality while maintaining comparable forecasting accuracy. Case studies show FairSTG can counter both spatial and temporal performance heterogeneity by our sample-level retrieval and compensation, and our work can potentially alleviate the risks on spatiotemporal resource allocation for underrepresented urban regions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4153-4168"},"PeriodicalIF":7.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783274","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

Two-Factor Authentication Based on Acoustic Fingerprinting in Modulation Domain 基于调制域声指纹的双因素认证

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-24 DOI: 10.1109/TMC.2024.3522077

Yanzhi Ren;Tingyuan Yang;Yufei Zhou;Hongbo Liu;Jiadi Yu;Haomiao Yang;Hongwei Li

{"title":"Two-Factor Authentication Based on Acoustic Fingerprinting in Modulation Domain","authors":"Yanzhi Ren;Tingyuan Yang;Yufei Zhou;Hongbo Liu;Jiadi Yu;Haomiao Yang;Hongwei Li","doi":"10.1109/TMC.2024.3522077","DOIUrl":"https://doi.org/10.1109/TMC.2024.3522077","url":null,"abstract":"The two-factor authentication (2FA) has been increasingly used with the popularity of mobile devices. Currently, many existing 2FA schemes extract the devices’ acoustic fingerprints as the second factor. Nevertheless, they mainly consider deriving fingerprints from the raw acoustic waveforms for authentication, which are susceptible to the fingerprint variations caused by the environmental noise or the varying distance between devices. To address these vulnerabilities, we propose a robust system utilizing the distortions of modulated signals, which are incurred by the acoustic elements of mobile devices, as the proof for 2FA. Specifically, our system first designs a channel delay estimation scheme to accurately estimate the propagation delay from the speaker to the microphone by deriving the phase change of the received sinusoidal signal. To perform a robust authentication, we design a new acoustic fingerprinting scheme to remove the impacts of the varying distance and environmental noise from the demodulated PSK signals for fingerprint extraction. Moreover, our device authentication component designs a transfer learning-based scheme to capture the subtle differences in devices’ fingerprints for accurate device authentication. To the best of our knowledge, this is the first 2FA system that could extract acoustic fingerprints in modulation domain and can effectively withstand the impacts of channel distortions. We also confirm the accuracy and security of our system through extensive user experiments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4235-4247"},"PeriodicalIF":7.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783240","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