IEEE Transactions on Mobile Computing最新文献_第7页

mmFinger: Talk to Smart Devices With Finger Tapping Gesture mmFinger：用手指敲击手势与智能设备交谈

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3515044

Xuan Wang;Xuerong Zhao;Chao Feng;Dingyi Fang;Xiaojiang Chen

{"title":"mmFinger: Talk to Smart Devices With Finger Tapping Gesture","authors":"Xuan Wang;Xuerong Zhao;Chao Feng;Dingyi Fang;Xiaojiang Chen","doi":"10.1109/TMC.2024.3515044","DOIUrl":"https://doi.org/10.1109/TMC.2024.3515044","url":null,"abstract":"Contact-free finger gesture recognition unlocks plenty of applications in smart Human-Computer Interaction (HCI). However, existing solutions either require users to wear sensors on their fingers or use continuously monitored cameras, raising concerns regarding user comfort and privacy. In this paper, we propose mmFinger, an accurate and robust mmWave-based finger gesture recognition system that can extend the range of available custom commands. The core idea is that mmFinger leverages the finger tapping pattern as a basic gesture and encodes different number combinations of the basic gesture like Morse code. To enable reliable recognition across different locations and for various users, we carefully design a robust feature Dop-profile to effectively characterize finger movements. Furthermore, by leveraging the multi-views provided by multiple antennas of radar, we develop an adaptive weighted feature fusion network to enhance the system's robustness. Finally, we devise a novel sequence prediction network to enable the system to recognize new gestures without retraining. Comprehensive experiments demonstrate that mmFinger can achieve an average recognition accuracy of 92% for 36 predefined gestures and 88% for 5 new user-defined commands, and is robust against finger location and user diversity.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3537-3551"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777769","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

HyperRegion: Integrating Graph and Hypergraph Contrastive Learning for Region Embeddings 超区域：整合图与超图对比学习的区域嵌入

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3515154

Mingyu Deng;Chao Chen;Wanyi Zhang;Jie Zhao;Wei Yang;Suiming Guo;Huayan Pu;Jun Luo

{"title":"HyperRegion: Integrating Graph and Hypergraph Contrastive Learning for Region Embeddings","authors":"Mingyu Deng;Chao Chen;Wanyi Zhang;Jie Zhao;Wei Yang;Suiming Guo;Huayan Pu;Jun Luo","doi":"10.1109/TMC.2024.3515154","DOIUrl":"https://doi.org/10.1109/TMC.2024.3515154","url":null,"abstract":"Region representations (also called embeddings) are useful for various urban computing tasks. While graph-based region representation learning methods have shown outstanding performance, they encounter two major challenges: 1) the pervasive data noise and missing data can affect the quality of the constructed region graphs; and 2) high-order relationships (i.e., group-wise relationships) among regions are often insufficiently modeled and sometimes entirely overlooked. To this end, we propose HyperRegion, an unsupervised region representation learning framework that integrates graph and hypergraph contrastive learning to learn comprehensive region embeddings from multi-modal data. Built upon a region hybrid graph network, this framework models both pair-wise and group-wise dependencies involving POI semantics, mobility patterns, geographic neighbors, and visual semantics. To mitigate the impact of data noise and missing data, graph and hypergraph contrastive learning are performed in parallel, and a cross-module contrast is further introduced to facilitate information exchange and collaboration. Extensive experiments on real-world datasets across three downstream tasks demonstrate that HyperRegion outperforms all baselines, particularly improving check-in prediction by reducing MAE and RMSE by approximately 8.5% and 8.2%, respectively, and increasing <inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> by about 7%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3667-3684"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777923","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-Efficient DNN Inference With Early Exiting in Serverless Edge Computing 无服务器边缘计算中的资源高效DNN推理

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3514993

Xiaolin Guo;Fang Dong;Dian Shen;Zhaowu Huang;Jinghui Zhang

{"title":"Resource-Efficient DNN Inference With Early Exiting in Serverless Edge Computing","authors":"Xiaolin Guo;Fang Dong;Dian Shen;Zhaowu Huang;Jinghui Zhang","doi":"10.1109/TMC.2024.3514993","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514993","url":null,"abstract":"Serverless Edge Computing (SEC) has gained widespread adoption in improving resource utilization due to its triggered event-driven model. However, deploying deep neural network (DNN) inference services directly in SEC leads to resource inefficiencies, which stem from two key factors. First, existing methods adopt model-wise function encapsulation, which requires the entire DNN model to occupy memory throughout its execution lifecycle. This increases both memory footprint and occupancy time. Second, uniform DNN inference for diversity input leads to redundant computations and additional inference time. To this end, we propose REDI, a novel framework that leverages fine-grained block-wise function encapsulation and progressive inference to provide resource-efficient DNN inference while ensuring latency requirements. REDI enables the release of memory from already inferred shallow networks and allows each request to exit early based on input data complexity, eliminating redundant computations. To fully unleash the potential, REDI jointly considers resource heterogeneity, data diversity, and environment dynamics to investigate the block-wise function placement problem. We introduce an uncertainty-aware online learning-driven algorithm with bounded regret. Finally, we conduct extensive trace-driven experiments to evaluate our methods, demonstrating that REDI achieves a significant speedup of up to <inline-formula><tex-math>$6.52times$</tex-math></inline-formula> in terms of resource usage cost compared to state-of-the-art methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3650-3666"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777918","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

Graph-Based Joint Client Clustering and Resource Allocation for Wireless Distributed Learning: A New Hierarchical Federated Learning Framework With Non-IID Data 基于图的无线分布式学习联合客户端聚类和资源分配：一种新的非iid数据分层联邦学习框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3515037

Ercong Yu;Shanyun Liu;Qiang Li;Hongyang Chen;H. Vincent Poor;Shlomo Shamai

{"title":"Graph-Based Joint Client Clustering and Resource Allocation for Wireless Distributed Learning: A New Hierarchical Federated Learning Framework With Non-IID Data","authors":"Ercong Yu;Shanyun Liu;Qiang Li;Hongyang Chen;H. Vincent Poor;Shlomo Shamai","doi":"10.1109/TMC.2024.3515037","DOIUrl":"https://doi.org/10.1109/TMC.2024.3515037","url":null,"abstract":"Hierarchical federated learning (HFL) is a key technology enabling distributed learning with reduced communication overhead. However, practical HFL systems encounter two major challenges: limited resources and data heterogeneity. In particular, limited resources can result in intolerable system latency, while heterogeneous data across clients can significantly degrade model accuracy and convergence rates. To address these issues and fully leverage the potential of HFL, we propose a novel framework called graph-based joint client and resource orchestration. This framework addresses the challenges of practical networks through joint client clustering and resource allocation. First, we propose a learning process where edge servers employ hypernetworks to achieve edge aggregation. This method can generate personalized client models and extract data distributions without directly exposing data distributions. Then, to characterize the joint effects of limited resources and data heterogeneity, we propose a graph-based modeling method and formulate a joint optimization problem that aims to balance data distributions and minimize latency. Subsequently, we propose a graph neural network-based algorithm to tackle the formulated problem with low-complexity optimization. Numerical results demonstrate significant benefits over existing algorithms in terms of convergence latency, model accuracy, scalability, and adaptability to new distributions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3579-3596"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777779","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

Mobile Tile-Based 360$^circ$∘ Video Multicast With Cybersickness Alleviation 基于移动磁片的360°视频多播，减轻晕眩

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3514852

Chiao-Wen Lin;De-Nian Yang;Wanjiun Liao

{"title":"Mobile Tile-Based 360$^circ$∘ Video Multicast With Cybersickness Alleviation","authors":"Chiao-Wen Lin;De-Nian Yang;Wanjiun Liao","doi":"10.1109/TMC.2024.3514852","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514852","url":null,"abstract":"Virtual reality (VR) imaging is 360°, which requires a large bandwidth for video transmission. To address this challenge, tile-based streaming has been proposed to deliver only the focused part of the video instead of the entire one. However, the impact of cybersickness, akin to motion sickness, on tile selection in VR has not been explored. In this paper, we investigate Multi-user Tile Streaming with Cybersickness Control (MTSCC) in an adaptive 360<inline-formula><tex-math>$^circ$</tex-math></inline-formula> video streaming system with multicast and cybersickness alleviation. We propose a novel <inline-formula><tex-math>$m^{2}$</tex-math></inline-formula>-competitive online algorithm that utilizes Individual Sickness Indicator (ISI) and Bitrate Restriction Indicator (BRI) to evaluate user cybersickness tendency and network bandwidth efficiency. Moreover, we introduce the Video Loss Indicator (VLI) and Quality Variance Indicator (QVI) to assess video quality loss and quality difference between tiles. We also propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward (video quality) and cost (cybersickness) estimation. The algorithm learns the weighting factor of each user's cost to slow down cybersickness accumulation for users with high cybersickness tendencies. We prove that the algorithm converges to an optimal solution over time. According to simulation with real network settings, our proposed algorithms outperform baselines in terms of video quality and cybersickness accumulation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3423-3440"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583228","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

PIM-LLN: Protocol Independent Multicast for Low-Power and Lossy Networks PIM-LLN：低功耗和有损网络的协议无关组播

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3514942

Issam Eddine Lakhlef;Badis Djamaa;Mustapha Reda Senouci;Abbas Bradai;Yahia Mohamed Cherif

{"title":"PIM-LLN: Protocol Independent Multicast for Low-Power and Lossy Networks","authors":"Issam Eddine Lakhlef;Badis Djamaa;Mustapha Reda Senouci;Abbas Bradai;Yahia Mohamed Cherif","doi":"10.1109/TMC.2024.3514942","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514942","url":null,"abstract":"In resource-constrained Internet of Things (IoT) environments like Low-power and Lossy Networks (LLNs), efficient communication protocols are essential. In this context, IP multicast protocols play a crucial role, facilitating the transmission of data packets from a single source to multiple recipients, thereby conserving bandwidth, power, and time for numerous LLN applications, such as over-the-air programming, information dissemination, and device configuration. Despite their usefulness, existing multicast solutions face several challenges, including scalability, energy efficiency, and reliability. To tackle such issues, this paper introduces Protocol Independent Multicast for LLNs (PIM-LLN). PIM-LLN employs a multicast distribution tree anchored at the border router, a multi-path data dissemination mechanism, and an efficient retransmission technique to route streams exclusively to regions with group members reducing energy consumption and bandwidth usage while improving response times and reliability. Through comprehensive simulations and public testbed experiments, we meticulously assess PIM-LLN’s performance, benchmarking it against state-of-the-art solutions under different scenarios. Our findings underscore the scalability, reliability, reduced latency, and efficient resource utilization of PIM-LLN in terms of memory, bandwidth, and energy. Notably, PIM-LLN, as compared to state-of-the-art solutions, achieves a similar level of reliability while reducing overhead by up to 50%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3809-3825"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777865","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

Self-Supervised Learning for Complex Activity Recognition Through Motif Identification Learning 基于基序识别学习的复杂活动识别的自监督学习

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3514736

Qingxin Xia;Jaime Morales;Yongzhi Huang;Takahiro Hara;Kaishun Wu;Hirotomo Oshima;Masamitsu Fukuda;Yasuo Namioka;Takuya Maekawa

{"title":"Self-Supervised Learning for Complex Activity Recognition Through Motif Identification Learning","authors":"Qingxin Xia;Jaime Morales;Yongzhi Huang;Takahiro Hara;Kaishun Wu;Hirotomo Oshima;Masamitsu Fukuda;Yasuo Namioka;Takuya Maekawa","doi":"10.1109/TMC.2024.3514736","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514736","url":null,"abstract":"Owing to the cost of collecting labeled sensor data, self-supervised learning (SSL) methods for human activity recognition (HAR) that effectively use unlabeled data for pretraining have attracted attention. However, applying prior SSL to COMPLEX activities in real industrial settings poses challenges. Despite the consistency of work procedures, varying circumstances, such as different sizes of packages and contents in a packing process, introduce significant variability within the same activity class. In this study, we focus on sensor data corresponding to characteristic and necessary actions (sensor data motifs) in a specific activity such as a stretching packing tape action in an assembling a box activity, and propose to train a neural network in self-supervised learning so that it identifies occurrences of the characteristic actions, i.e., Motif Identification Learning (MoIL). The feature extractor in the network is subsequently employed in the downstream activity recognition task, enabling accurate recognition of activities containing these characteristic actions, even with limited labeled training data. The MoIL approach was evaluated on real-world industrial activity data, encompassing the state-of-the-art SSL tasks with an improvement of up to 23.85% under limited training labels.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3779-3793"},"PeriodicalIF":7.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776242","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

MIQA: An Application Agent for Immersive Content Delivery Over Millimeter Waves MIQA：毫米波上沉浸式内容交付的应用代理

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-11 DOI: 10.1109/TMC.2024.3514973

Zongshen Wu;Chin-Ya Huang;Parameswaran Ramanathan

引用次数: 0

Differentially Private Weighted Graphs Publication Under Continuous Monitoring 连续监控下的差分私有加权图发布

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-09 DOI: 10.1109/TMC.2024.3514153

Wen Xu;Zhetao Li;Haolin Liu;Yunjun Gao;Xiaofei Liao;Kenli Li

{"title":"Differentially Private Weighted Graphs Publication Under Continuous Monitoring","authors":"Wen Xu;Zhetao Li;Haolin Liu;Yunjun Gao;Xiaofei Liao;Kenli Li","doi":"10.1109/TMC.2024.3514153","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514153","url":null,"abstract":"Graph data analysis has been used in various real-world applications to improve services or scientific research, which, however, may expose sensitive personal information. Differential privacy (DP) has become the gold standard for publishing graph data while still protecting personal privacy. However, most existing studies over differentially private graph data publication mainly focus on static unweighted graphs. As interactions between entities in real systems are often dynamically changing and associated with weights, it is desirable to consider the more general scenario of continuous weighted graph publication under DP in the temporal dimension. Therefore, we investigate the problem of publishing weighted graphs satisfying DP under continuous monitoring. Specifically, we consider a server that continuously monitors user data and publishes a sequence of weighted graph snapshots. We propose SwgDP, a novel framework that leverages historical graph data to guide current snapshot generation. SwgDP consists of four key components: node adaptive sampling, dynamic weight optimization, prediction-based community detection and weighted graph generation. We demonstrate that SwgDP satisfies DP, and comprehensive experiments on four real-world datasets and four commonly used graph metrics show that SwgDP can effectively synthesize weighted graph at any time step.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3735-3749"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777765","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

FinerSense: A Fine-Grained Respiration Sensing System Based on Precise Separation of Wi-Fi Signals FinerSense：基于Wi-Fi信号精确分离的细粒度呼吸传感系统

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-09 DOI: 10.1109/TMC.2024.3514311

Wenchao Song;Zhu Wang;Yifan Guo;Zhuo Sun;Zhihui Ren;Chao Chen;Bin Guo;Zhiwen Yu;Xingshe Zhou;Daqing Zhang

{"title":"FinerSense: A Fine-Grained Respiration Sensing System Based on Precise Separation of Wi-Fi Signals","authors":"Wenchao Song;Zhu Wang;Yifan Guo;Zhuo Sun;Zhihui Ren;Chao Chen;Bin Guo;Zhiwen Yu;Xingshe Zhou;Daqing Zhang","doi":"10.1109/TMC.2024.3514311","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514311","url":null,"abstract":"This study introduces a novel approach for preventing overexertion in home fitness through fine-grained detection of respiratory parameters. To overcome the robustness limitation associated with using a composite signal for wireless sensing, we introduce an optimization-based signal separation model. This model effectively disentangles composite signals into static and dynamic components, while preserving the intricate details of target movements or activities. Specifically, by constructing a reference signal derived from the dominant static component, we eliminate time-varying phase shifts and leverage the invariant property of the dynamic component’s amplitude for precise separation. A system called <italic>FinerSense is developed, which is able to accurately and robustly detect fine-grained respiratory parameters such as respiration rate, depth, and inhalation-to-exhalation ratio with accuracy rates exceeding 97%, 95%, and 91%, respectively. Extensive experiments show that the developed system outperforms state-of-the-art baselines significantly, empowering users to optimize exercise intensity and duration while mitigating the risk of overexertion. We believe that this work is able to facilitate the seamless transition of wireless sensing systems from laboratory prototypes to practical and user-friendly applications.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3703-3718"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777960","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