Pub Date : 2024-08-21DOI: 10.1109/TMC.2024.3447026
Xueliang Li;Shicong Hong;Junyang Chen;Junkai Ji;Chengwen Luo;Guihai Yan;Zhibin Yu;Jianqiang Li
Energy-efficiency is one of the most important design criteria for mobile systems, such as smartphones and tablets. But current mobile systems always over-provision resources to satisfy users. The root cause is that, we have no knowledge on how much of system performance/energy will exactly satisfy users. Psychophysics defines the quantified link between physical stimuli and human-perceived stimuli. So, we will leverage psychophysics to study the quantified correlation between computer architecture resources (i.e., physical stimuli) and user satisfaction (i.e., human-perceived stimuli). We then exploit such correlation to precisely apportion resources to operate tasks and accurately satisfy users. Benefiting from our precisely-defined user satisfaction criteria and well-designed algorithms, we can reduce energy consumption of computer architectures by up to 42.9% without harming user experience. To the best of our knowledge, we for the first time theoretically and accurately model such substantial correlation. Our work opens a new research domain for fundamentally improving mobiles’ energy-efficiency.
{"title":"Satisfying Energy-Efficiency Constraints for Mobile Systems","authors":"Xueliang Li;Shicong Hong;Junyang Chen;Junkai Ji;Chengwen Luo;Guihai Yan;Zhibin Yu;Jianqiang Li","doi":"10.1109/TMC.2024.3447026","DOIUrl":"10.1109/TMC.2024.3447026","url":null,"abstract":"Energy-efficiency is one of the most important design criteria for mobile systems, such as smartphones and tablets. But current mobile systems always over-provision resources to satisfy users. The root cause is that, we have no knowledge on how much of system performance/energy will exactly satisfy users. Psychophysics defines the quantified link between physical stimuli and human-perceived stimuli. So, we will leverage psychophysics to study the quantified correlation between computer architecture resources (i.e., physical stimuli) and user satisfaction (i.e., human-perceived stimuli). We then exploit such correlation to precisely apportion resources to operate tasks and accurately satisfy users. Benefiting from our precisely-defined user satisfaction criteria and well-designed algorithms, we can reduce energy consumption of computer architectures by up to 42.9% without harming user experience. To the best of our knowledge, we for the first time theoretically and accurately model such substantial correlation. Our work opens a new research domain for fundamentally improving mobiles’ energy-efficiency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180595","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}
Pub Date : 2024-08-21DOI: 10.1109/TMC.2024.3447000
Ziru Niu;Hai Dong;A. K. Qin;Tao Gu
Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the unequal training contributions among clients have made FL vulnerable, as clients with heavily biased datasets can easily compromise FL by sending malicious or heavily biased parameter updates. Furthermore, the resource shortage issue of the network also becomes a bottleneck. Due to overwhelming computation overheads generated by training deep-learning models on edge devices, and significant communication overheads for transmitting deep-learning models across the network, enormous amounts of resources are consumed in the FL process. This encompasses computation resources like energy and communication resources like bandwidth. To comprehensively address these challenges, in this paper, we present FLrce, an efficient FL framework with a r elationship-based c lient selection and e arly-stopping strategy. FLrce accelerates the FL process by selecting clients with more significant effects, enabling the global model to converge to a high accuracy in fewer rounds. FLrce also leverages an early stopping mechanism that terminates FL in advance to save communication and computation resources. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.
{"title":"FLrce: Resource-Efficient Federated Learning With Early-Stopping Strategy","authors":"Ziru Niu;Hai Dong;A. K. Qin;Tao Gu","doi":"10.1109/TMC.2024.3447000","DOIUrl":"10.1109/TMC.2024.3447000","url":null,"abstract":"Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the unequal training contributions among clients have made FL vulnerable, as clients with heavily biased datasets can easily compromise FL by sending malicious or heavily biased parameter updates. Furthermore, the resource shortage issue of the network also becomes a bottleneck. Due to overwhelming computation overheads generated by training deep-learning models on edge devices, and significant communication overheads for transmitting deep-learning models across the network, enormous amounts of resources are consumed in the FL process. This encompasses computation resources like energy and communication resources like bandwidth. To comprehensively address these challenges, in this paper, we present FLrce, an efficient FL framework with a \u0000<bold>r</b>\u0000elationship-based \u0000<bold>c</b>\u0000lient selection and \u0000<bold>e</b>\u0000arly-stopping strategy. FLrce accelerates the FL process by selecting clients with more significant effects, enabling the global model to converge to a high accuracy in fewer rounds. FLrce also leverages an early stopping mechanism that terminates FL in advance to save communication and computation resources. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180591","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 Long Range Point Cloud Registration in Vehicular Networks via Muti-hop Relays","authors":"Zhenxi Wang, Hongzi Zhu, Yunxiang Cai, Quan Liu, Shan Chang, Liang Zhang, Minyi Guo","doi":"10.1109/tmc.2024.3446828","DOIUrl":"https://doi.org/10.1109/tmc.2024.3446828","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180597","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}
Pub Date : 2024-08-21DOI: 10.1109/tmc.2024.3446819
Geng Sun, Yixian Wang, Zemin Sun, Qingqing Wu, Jiawen Kang, Dusit Niyato, Victor C. M. Leung
{"title":"Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing","authors":"Geng Sun, Yixian Wang, Zemin Sun, Qingqing Wu, Jiawen Kang, Dusit Niyato, Victor C. M. Leung","doi":"10.1109/tmc.2024.3446819","DOIUrl":"https://doi.org/10.1109/tmc.2024.3446819","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180589","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":"Lyapunov-Guided Offloading Optimization based on Soft Actor-Critic for ISAC-Aided Internet of Vehicles","authors":"Yonghui Liang, Huijun Tang, Huaming Wu, Yixiao Wang, Pengfei Jiao","doi":"10.1109/tmc.2024.3445350","DOIUrl":"https://doi.org/10.1109/tmc.2024.3445350","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180601","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}
Pub Date : 2024-08-19DOI: 10.1109/TMC.2024.3445411
Fan Dang;Xinqi Jin;Qi-An Fu;Lingkun Li;Guanyan Peng;Xinlei Chen;Kebin Liu;Yunhao Liu
Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, existing fingerprinting-based streaming systems are not widely used since they fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, an accurate and lightweight temporal synchronization scheme, a polarized and randomized SVD watermarking scheme, and a collusion-resistant fingerprinting scheme. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing methods, the proposed three schemes improve the re-identification accuracy by 4-49x, the watermark extraction accuracy by 2.25x at most and 1.5x on average, and the recall rate of catching colluders by 26%.
{"title":"StreamingTag: A Scalable Piracy Tracking Solution for Mobile Streaming Services","authors":"Fan Dang;Xinqi Jin;Qi-An Fu;Lingkun Li;Guanyan Peng;Xinlei Chen;Kebin Liu;Yunhao Liu","doi":"10.1109/TMC.2024.3445411","DOIUrl":"10.1109/TMC.2024.3445411","url":null,"abstract":"Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, existing fingerprinting-based streaming systems are not widely used since they fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, an accurate and lightweight temporal synchronization scheme, a polarized and randomized SVD watermarking scheme, and a collusion-resistant fingerprinting scheme. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing methods, the proposed three schemes improve the re-identification accuracy by 4-49x, the watermark extraction accuracy by 2.25x at most and 1.5x on average, and the recall rate of catching colluders by 26%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180599","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}
Pub Date : 2024-08-19DOI: 10.1109/TMC.2024.3445583
Tianlong Zhou;Tianyi Shi;Hongye Gao;Weixiong Rao
In this paper, we study the collaborative detection problem in a multi-agent environment. By exploiting onboard range-bearing sensors, mobile agents make sequential control decisions such as moving directions to gather information of movable targets. To estimate target states, i.e., target location and velocity, the classic works such as Kalman Filter (KF) and Extended Kalman Filter (EKF) impractically assume that the underlying state space model is fully known, and some recent learning-based works, i.e., KalmanNet, estimate target states alone but without estimation uncertainty, and cannot make robust control decision. To tackle such issues, we first propose a neural network-based state estimator, namely T W o-phase K AL ma n Filter with U ncertainty quan T ification (WALNUT), to explicitly give both target states and estimation uncertainty. The developed multi-agent reinforcement learning (MARL) model then takes the learned target states and uncertainty as input and makes robust actions to track movable targets. Our extensive experiments demonstrate that our work outperforms the state-of-the-art by higher tracking ability and lower localization error.
{"title":"Learning to Optimize State Estimation in Multi-Agent Reinforcement Learning-Based Collaborative Detection","authors":"Tianlong Zhou;Tianyi Shi;Hongye Gao;Weixiong Rao","doi":"10.1109/TMC.2024.3445583","DOIUrl":"10.1109/TMC.2024.3445583","url":null,"abstract":"In this paper, we study the collaborative detection problem in a multi-agent environment. By exploiting onboard range-bearing sensors, mobile agents make sequential control decisions such as moving directions to gather information of movable targets. To estimate target states, i.e., target location and velocity, the classic works such as Kalman Filter (KF) and Extended Kalman Filter (EKF) impractically assume that the underlying state space model is fully known, and some recent learning-based works, i.e., KalmanNet, estimate target states alone but without estimation uncertainty, and cannot make robust control decision. To tackle such issues, we first propose a neural network-based state estimator, namely T\u0000<underline>W</u>\u0000o-phase K\u0000<underline>AL</u>\u0000ma\u0000<underline>n</u>\u0000 Filter with \u0000<underline>U</u>\u0000ncertainty quan\u0000<underline>T</u>\u0000ification (WALNUT), to explicitly give both target states and estimation uncertainty. The developed multi-agent reinforcement learning (MARL) model then takes the learned target states and uncertainty as input and makes robust actions to track movable targets. Our extensive experiments demonstrate that our work outperforms the state-of-the-art by higher tracking ability and lower localization error.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180600","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}
Pub Date : 2024-08-19DOI: 10.1109/TMC.2024.3439588
Yatong Wang;Zhongyi Wen;Yunjie Li;Bin Cao
Decentralized federated learning (DFL) has emerged as a conducive paradigm, facilitating a distributed privacy-preserving data collaboration mode in mobile edge computing (MEC) systems to bolster the expansion of artificial intelligence applications. Nevertheless, the dynamic wireless environment and the heterogeneity among collaborating nodes, characterized by skewed datasets and uneven capabilities, present substantial challenges for efficient DFL model training in MEC systems. Consequently, the design of an efficient collaboration strategy becomes essential to facilitate practical distributed knowledge sharing and cost reduction for MEC. In this paper, we propose an adaptive decentralized federated learning framework that enables heterogeneous nodes to learn tailored collaboration strategies, thereby maximizing the efficiency of the DFL training process in collaborative MEC systems. Specifically, we present an effective option critic-based collaboration strategy learning (OCSL) mechanism by decomposing the collaboration strategy model into two sub-strategies: local training strategy and resource scheduling strategy. In addressing inherent issues such as large-scale action space and overestimation in collaboration strategy learning, we introduce the option framework and a dual critic network-based approximation method within the OCSL design. We theoretically prove that the learned collaboration strategy achieves the Nash equilibrium. Extensive numerical results demonstrate the effectiveness of the proposed method in comparison with existing baselines.
{"title":"Learn to Collaborate in MEC: An Adaptive Decentralized Federated Learning Framework","authors":"Yatong Wang;Zhongyi Wen;Yunjie Li;Bin Cao","doi":"10.1109/TMC.2024.3439588","DOIUrl":"https://doi.org/10.1109/TMC.2024.3439588","url":null,"abstract":"Decentralized federated learning (DFL) has emerged as a conducive paradigm, facilitating a distributed privacy-preserving data collaboration mode in mobile edge computing (MEC) systems to bolster the expansion of artificial intelligence applications. Nevertheless, the dynamic wireless environment and the heterogeneity among collaborating nodes, characterized by skewed datasets and uneven capabilities, present substantial challenges for efficient DFL model training in MEC systems. Consequently, the design of an efficient collaboration strategy becomes essential to facilitate practical distributed knowledge sharing and cost reduction for MEC. In this paper, we propose an adaptive decentralized federated learning framework that enables heterogeneous nodes to learn tailored collaboration strategies, thereby maximizing the efficiency of the DFL training process in collaborative MEC systems. Specifically, we present an effective option critic-based collaboration strategy learning (OCSL) mechanism by decomposing the collaboration strategy model into two sub-strategies: local training strategy and resource scheduling strategy. In addressing inherent issues such as large-scale action space and overestimation in collaboration strategy learning, we introduce the option framework and a dual critic network-based approximation method within the OCSL design. We theoretically prove that the learned collaboration strategy achieves the Nash equilibrium. Extensive numerical results demonstrate the effectiveness of the proposed method in comparison with existing baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595897","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}
Pub Date : 2024-08-19DOI: 10.1109/TMC.2024.3445507
Mingda Han;Huanqi Yang;Mingda Jia;Weitao Xu;Yanni Yang;Zhijian Huang;Jun Luo;Xiuzhen Cheng;Pengfei Hu
Video surveillance systems play a crucial role in ensuring public safety and security by capturing and monitoring critical events in various areas. However, traditional surveillance cameras face limitations when it comes to malicious physical damage or obscuring by offenders. To overcome this limitation, we propose m$^{2}$2 Vision , which is the first millimeter-wave (mmWave)-based video reconstruction system designed to enhance existing video surveillance cameras. m$^{2}$2 Vision utilizes mmWave to sense the profile and motion signature of the target, integrating it with previously acquired visual data about the environment and the target's appearance, thereby facilitating the reconstruction of surveillance video. Specifically, our proposed system incorporates a dual-stage mmWave signal denoising algorithm to efficiently eliminate the noise and multiple-input multiple-output virtual antenna enhanced heatmap generation (MVAE-HG) method to obtain fine-grained mmWave heatmaps responsive to the target's profile and motion information. Moreover, we design the mm2Video generative network that first employs a multi-modal fusion module to fuse the mmWave and pre-acquired visual data, then use a conditional generative adversarial network (cGAN)-based video reconstruction module for surveillance video reconstruction. We conducted comprehensive experiments on m$^{2}$2 Vision using a commercial mmWave radar and four surveillance cameras across various environments, with the participation of seven individuals. Evaluation results show that m$^{2}$2 Vision can achieve an average structural similarity index measure (SSIM) of 0.93, demonstrating its effectiveness and potential.
{"title":"Seeing the Invisible: Recovering Surveillance Video With COTS mmWave Radar","authors":"Mingda Han;Huanqi Yang;Mingda Jia;Weitao Xu;Yanni Yang;Zhijian Huang;Jun Luo;Xiuzhen Cheng;Pengfei Hu","doi":"10.1109/TMC.2024.3445507","DOIUrl":"https://doi.org/10.1109/TMC.2024.3445507","url":null,"abstract":"Video surveillance systems play a crucial role in ensuring public safety and security by capturing and monitoring critical events in various areas. However, traditional surveillance cameras face limitations when it comes to malicious physical damage or obscuring by offenders. To overcome this limitation, we propose \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000, which is the first millimeter-wave (mmWave)-based video reconstruction system designed to enhance existing video surveillance cameras. \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000 utilizes mmWave to sense the profile and motion signature of the target, integrating it with previously acquired visual data about the environment and the target's appearance, thereby facilitating the reconstruction of surveillance video. Specifically, our proposed system incorporates a dual-stage mmWave signal denoising algorithm to efficiently eliminate the noise and multiple-input multiple-output virtual antenna enhanced heatmap generation (MVAE-HG) method to obtain fine-grained mmWave heatmaps responsive to the target's profile and motion information. Moreover, we design the mm2Video generative network that first employs a multi-modal fusion module to fuse the mmWave and pre-acquired visual data, then use a conditional generative adversarial network (cGAN)-based video reconstruction module for surveillance video reconstruction. We conducted comprehensive experiments on \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000 using a commercial mmWave radar and four surveillance cameras across various environments, with the participation of seven individuals. Evaluation results show that \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000 can achieve an average structural similarity index measure (SSIM) of 0.93, demonstrating its effectiveness and potential.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598670","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}
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