IEEE Transactions on Mobile Computing最新文献

{"title":"Task-Oriented Video Compressive Streaming for Real-Time Semantic Segmentation","authors":"Xuedou Xiao;Yingying Zuo;Mingxuan Yan;Wei Wang;Jianhua He;Qian Zhang","doi":"10.1109/TMC.2024.3446185","DOIUrl":"10.1109/TMC.2024.3446185","url":null,"abstract":"Real-time semantic segmentation (SS) is a major task for various vision-based applications such as self-driving. Due to the limited computing resources and stringent performance requirements, streaming videos from camera-embedded mobile devices to edge servers for SS is a promising approach. While there are increasing efforts on task-oriented video compression, most SS-applicable algorithms apply more uniform compression, as the sensitive regions are less obvious and concentrated. Such processing results in low compression performance and significantly limits the capacity of edge servers supporting real-time SS. In this paper, we propose STAC, a novel task-oriented DNN-driven video compressive streaming algorithm tailed for SS, to strike accuracy-bitrate balance and adapt to time-varying bandwidth. It exploits DNN's gradients as sensitivity metrics for fine-grained spatial adaptive compression and includes a temporal adaptive scheme that integrates spatial adaptation with predictive coding. Furthermore, we design a new bandwidth-aware neural network, serving as a compatible configuration tuner to fit time-varying bandwidth and content. STAC is evaluated in a system with a commodity mobile device and an edge server with real-world network traces. Experiments show that STAC can save up to 63.7–75.2% of bandwidth or improve accuracy by 3.1–9.5% compared to state-of-the-art algorithms, while capable of adapting to time-varying bandwidth.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14396-14413"},"PeriodicalIF":7.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks","authors":"Minwoo Kim;Jonggyu Jang;Youngchol Choi;Hyun Jong Yang","doi":"10.1109/TMC.2024.3458185","DOIUrl":"10.1109/TMC.2024.3458185","url":null,"abstract":"The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)—namely, minimizing service latency. Additionally, the use of MEC systems poses an additional problem arising from limited battery resources of MDs. This paper tackles the pressing challenge of latency-aware distributed task offloading optimization, where user association (UA), resource allocation (RA), full-task offloading, and battery of mobile devices (MDs) are jointly considered. In existing studies, joint optimization of overall task offloading and UA is seldom considered due to the complexity of combinatorial optimization problems, and in cases where it is considered, linear objective functions such as power consumption are adopted. Revolutionizing the realm of MEC, our objective includes all major components contributing to users’ quality of experience, including latency and energy consumption. To achieve this, we first formulate an NP-hard combinatorial problem, where the objective function comprises three elements: communication latency, computation latency, and battery usage. We derive a closed-form RA solution of the problem; next, we provide a distributed pricing-based UA solution. We simulate the proposed algorithm for various resource-intensive tasks. Our numerical results show that the proposed method Pareto-dominates baseline methods. More specifically, the results demonstrate that the proposed method can outperform baseline methods by \u0000<italic>1.62 times shorter latency</i>\u0000 with \u0000<italic>41.2% less energy consumption</i>\u0000.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15149-15166"},"PeriodicalIF":7.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180548","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":"Democratizing Federated WiFi-Based Human Activity Recognition Using Hypothesis Transfer","authors":"Bing Li;Wei Cui;Le Zhang;Qi Yang;Min Wu;Joey Tianyi Zhou","doi":"10.1109/TMC.2024.3457788","DOIUrl":"10.1109/TMC.2024.3457788","url":null,"abstract":"Human activity recognition (HAR) is a crucial task in IoT systems with applications ranging from surveillance and intruder detection to home automation and more. Recently, non-invasive HAR utilizing WiFi signals has gained considerable attention due to advancements in ubiquitous WiFi technologies. However, recent studies have revealed significant privacy risks associated with WiFi signals, raising concerns about bio-information leakage. To address these concerns, the decentralized paradigm, particularly federated learning (FL), has emerged as a promising approach for training HAR models while preserving data privacy. Nevertheless, FL models may struggle in end-user environments due to substantial domain discrepancies between the source training data and the target end-user environment. This discrepancy arises from the sensitivity of WiFi signals to environmental changes, resulting in notable domain shifts. As a consequence, FL-based HAR approaches often face challenges when deployed in real-world WiFi environments. Albeit there are pioneer attempts on federated domain adaptation, they typically require non-trivial communication and computation cost, which is prohibitively expensive especially considering edge-based hardware equipment of end-user environment. In this paper, we propose a model to democratize the WiFi-based HAR system by enhancing recognition accuracy in unannotated end-user environments while prioritizing data privacy. Our model leverages the hypothesis transfer and a lightweight hypothesis ensemble to mitigate negative transfer. We prove a tighter theoretical upper bound compared to existing multi-source federated domain adaptation models. Extensive experiments shows our model improves the average accuracy by approximately 10 absolute percentage points in both cross-person and cross-environment settings comparing several state-of-the-art baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15132-15148"},"PeriodicalIF":7.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180550","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":"Joint Client-and-Sample Selection for Federated Learning via Bi-Level Optimization","authors":"Anran Li;Guangjing Wang;Ming Hu;Jianfei Sun;Lan Zhang;Luu Anh Tuan;Han Yu","doi":"10.1109/TMC.2024.3455331","DOIUrl":"10.1109/TMC.2024.3455331","url":null,"abstract":"Federated Learning (FL) enables massive local data owners to collaboratively train a deep learning model without disclosing their private data. The importance of local data samples from various data owners to FL models varies widely. This is exacerbated by the presence of noisy data that exhibit large losses similar to important (hard) samples. Currently, there lacks an FL approach that can effectively distinguish hard samples (which are beneficial) from noisy samples (which are harmful). To bridge this gap, we propose the joint Federated Meta-Weighting based Client and Sample Selection (FedMW-CSS) approach to simultaneously mitigate label noise and hard sample selection. It is a bilevel optimization approach for FL client-and-sample selection and global model construction to achieve hard sample-aware noise-robust learning in a privacy preserving manner. It performs meta-learning based online approximation to iteratively update global FL models, select the most positively influential samples and deal with training data noise. To utilize both the instance-level information and class-level information for better performance improvements, FedMW-CSS efficiently learns a class-level weight by manipulating gradients at the class level, e.g., it performs a gradient descent step on class-level weights, which only relies on intermediate gradients. Theoretically, we analyze the privacy guarantees and convergence of FedMW-CSS. Extensive experiments comparison against eight state-of-the-art baselines on six real-world datasets in the presence of data noise and heterogeneity shows that FedMW-CSS achieves up to 28.5% higher test accuracy, while saving communication and computation costs by at least 49.3% and 1.2%, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15196-15209"},"PeriodicalIF":7.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180553","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":"GUGEN: Global User Graph Enhanced Network for Next POI Recommendation","authors":"Changqi Zuo;Xu Zhang;Liang Yan;Zuyu Zhang","doi":"10.1109/TMC.2024.3455107","DOIUrl":"10.1109/TMC.2024.3455107","url":null,"abstract":"Learning the next Point-of-Interest (POI) is a highly context-dependent human movement behavior prediction task, which has gained increasing attention with the consideration of massive spatial-temporal trajectories data or check-in data. The spatial dependency, temporal dependency, sequential dependency and social network dependency are widely considered pivotal to predict the users’ next location in the near future. However, most existing models fail to consider the influence of other users’ movement patterns and the correlation with the POIs the user has visited. Therefore, we propose a Global User Graph Enhanced Network (GUGEN) for the next POI recommendation from a global and a user perspectives. First, a trajectory learning network is designed to model the users’ short-term preference. Second, a geographical learning module is designed to model the global and user context information. From the global perspective, two graphs are designed to represent the global POI features and the geographical relationships of all POIs. From the user perspective, a user graph is constructed to describe each users’ historical POI information. We evaluated the proposed model on three real-world datasets. The experimental evaluations demonstrate that the proposed GUGEN method outperforms the state-of-the-art approaches for the next POI recommendation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14975-14986"},"PeriodicalIF":7.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180563","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":"FedFMSL: Federated Learning of Foundation Models With Sparsely Activated LoRA","authors":"Panlong Wu;Kangshuo Li;Ting Wang;Yanjie Dong;Victor C. M. Leung;Fangxin Wang","doi":"10.1109/TMC.2024.3454634","DOIUrl":"10.1109/TMC.2024.3454634","url":null,"abstract":"Foundation models (FMs) have shown great success in natural language processing, computer vision, and multimodal tasks. FMs have a large number of model parameters, thus requiring a substantial amount of data to help optimize the model during the training. Federated learning has revolutionized machine learning by enabling collaborative learning from decentralized data while still preserving clients’ data privacy. Despite the great benefits foundation models can have empowered by federated learning, their bulky model parameters cause severe communication challenges for modern networks and computation challenges especially for edge devices. Moreover, the data distribution of different clients can be different thus inducing statistical challenges. In this paper, we propose a novel two-stage federated learning algorithm called FedFMSL. A global expert is trained in the first stage and a local expert is trained in the second stage to provide better personalization. We construct a Mixture of Foundation Models (\u0000<monospace>MoFM</monospace>\u0000) with these two experts and design a gate neural network with an inserted gate adapter that joins the aggregation every communication round in the second stage. To further adapt to edge computing scenarios with limited computational resources, we design a novel Sparsely Activated LoRA (\u0000<monospace>SAL</monospace>\u0000) algorithm that freezes the pre-trained foundation model parameters inserts low-rank adaptation matrices into transformer blocks, and activates them progressively during the training. We employ extensive experiments to verify the effectiveness of FedFMSL, results show that FedFMSL outperforms other SOTA baselines by up to 59.19% in default settings while tuning less than 0.3% parameters of the foundation model.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15167-15181"},"PeriodicalIF":7.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180554","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":"Modeling Spatio-Temporal Mobility Across Data Silos via Personalized Federated Learning","authors":"Yudong Zhang;Xu Wang;Pengkun Wang;Binwu Wang;Zhengyang Zhou;Yang Wang","doi":"10.1109/TMC.2024.3453657","DOIUrl":"10.1109/TMC.2024.3453657","url":null,"abstract":"Spatio-temporal mobility modeling plays a pivotal role in the advancement of mobile computing. Nowadays, data is frequently held by various distributed silos, which are isolated from each other and confront limitations on data sharing. Given this, there have been some attempts to introduce federated learning into spatio-temporal mobility modeling. Meanwhile, the distributional heterogeneity inherent in the spatio-temporal data also puts forward requirements for model personalization. However, the existing methods tackle personalization in a model-centric manner and fail to explore the data characteristics in various data silos, thus ignoring the fact that the fundamental cause of insufficient personalization in the model is the heterogeneous distribution of data. In this paper, we propose a novel distribution-oriented personalized \u0000<underline>Fed</u>\u0000erated learning framework for \u0000<underline>Cro</u>\u0000ss-silo \u0000<underline>S</u>\u0000patio-\u0000<underline>T</u>\u0000emporal mobility modeling (named \u0000<italic>FedCroST</i>\u0000), that leverages learnable spatio-temporal prompts to implicitly represent the local data distribution patterns of data silos and guide the local models to learn the personalized information. Specifically, we focus on the potential characteristics within temporal distribution and devise a conditional diffusion module to generate temporal prompts that serve as guidance for the evolution of the time series. Simultaneously, we emphasize the structure distribution inherent in node neighborhoods and propose adaptive spatial structure partition to construct the spatial prompts, augmenting the spatial information representation. Furthermore, we introduce a denoising autoencoder to effectively harness the learned multi-view spatio-temporal features and obtain personalized representations adapted to local tasks. Our proposal highlights the significance of latent spatio-temporal data distributions in enabling personalized federated spatio-temporal learning, providing new insights into modeling spatio-temporal mobility in data silo scenarios. Extensive experiments conducted on real-world datasets demonstrate that FedCroST outperforms the advanced baselines by a large margin in diverse cross-silo spatio-temporal mobility modeling tasks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15289-15306"},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180555","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":"A Federated Online Restless Bandit Framework for Cooperative Resource Allocation","authors":"Jingwen Tong;Xinran Li;Liqun Fu;Jun Zhang;Khaled B. Letaief","doi":"10.1109/TMC.2024.3453250","DOIUrl":"10.1109/TMC.2024.3453250","url":null,"abstract":"Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dynamics of MRPs are known prior, which makes the RMAB problem solvable from an optimization perspective. Nevertheless, an efficient learning-based solution for RMABs with unknown system dynamics remains an open problem. In this paper, we fill this gap by investigating a cooperative resource allocation problem with unknown system dynamics of MRPs. This problem can be modeled as a multi-agent online RMAB problem, where multiple agents collaboratively learn the system dynamics while maximizing their accumulated rewards. We devise a federated online RMAB framework to mitigate the communication overhead and data privacy issue by adopting the federated learning paradigm. Based on this framework, we put forth a Federated Thompson Sampling-enabled Whittle Index (FedTSWI) algorithm to solve this multi-agent online RMAB problem. The FedTSWI algorithm enjoys a high communication and computation efficiency, and a privacy guarantee. Moreover, we derive a regret upper bound for the FedTSWI algorithm. Finally, we demonstrate the effectiveness of the proposed algorithm on the case of online multi-user multi-channel access. Numerical results show that the proposed algorithm achieves a fast convergence rate of \u0000<inline-formula><tex-math>$mathcal {O}(sqrt{Tlog (T)})$</tex-math></inline-formula>\u0000 and better performance compared with baselines. More importantly, its sample complexity reduces sublinearly with the number of agents.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15274-15288"},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180556","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":"DTTCNet: Time-to-Collision Estimation With Autonomous Emergency Braking Using Multi-Scale Transformer Network","authors":"Xiaoqiang Teng;Shibiao Xu;Deke Guo;Yulan Guo;Weiliang Meng;Xiaopeng Zhang","doi":"10.1109/TMC.2024.3454122","DOIUrl":"10.1109/TMC.2024.3454122","url":null,"abstract":"The rapid advancement of autonomous driving technologies has brought the significance of Autonomous Emergency Braking (AEB) systems, which are paramount in mitigating collision risk and elevating road safety by preemptively applying brakes when a potential collision is detected. Within the core mechanisms of AEB systems, the Time-to-Collision (TTC) estimation plays a pivotal role, in quantitatively determining the criticality and timing for initiating braking interventions. However, existing TTC estimation approaches exhibit sensitivity to diverse driving scenarios, compromising the performance of AEB systems, especially in instantaneous situations. To address these issues, this paper presents DTTCNet, a novel supervised deep learning model for TTC estimation that leverages multi-scale transformer architectures and multi-task losses, thereby enhancing precision and boosting system performance. The DTTCNet first extracts spatiotemporal features from raw sensor data and utilizes a supervised training strategy. The multi-scale transformer architecture effectively captures variations across different scales, while the multi-task loss function optimizes the network training performance. Our experimental results on a challenging dataset demonstrate that DTTCNet achieves approximately 20% performance improvements over existing methods in terms of accuracy. This signifies a promising approach to augmenting the safety of autonomous driving systems with the integration of aftermarket mobile devices (e.g., Mobileye and Bosch products).","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14903-14917"},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180562","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":"Real-World Large-Scale Cellular Localization for Pickup Position Recommendation at Black-Hole","authors":"Ruipeng Gao;Shuli Zhu;Lingkun Li;Xuyu Wang;Yuqin Jiang;Naiqiang Tan;Hua Chai;Peng Qi;Jiqiang Liu;Dan Tao","doi":"10.1109/TMC.2024.3453596","DOIUrl":"10.1109/TMC.2024.3453596","url":null,"abstract":"Indoor localization availability is still sporadic in industry, especially at the black-hole, i.e., there only exist cellular signals, no GPS or WiFi signals. Based on our 2-year observations at the DiDi ride-hailing platform in China, there are \u0000<inline-formula><tex-math>$ 68,text{k}$</tex-math></inline-formula>\u0000 orders everyday created at black-hole. In this paper, we present \u0000<i>TransparentLoc</i>\u0000, a large-scale cellular localization system for pickup position recommendation of the DiDi platform. Specifically, we design a CNN model for real-time localization based on a crowdsourcing fingerprint set constructed by outdoor trajectories and abnormal cell tower detection. Then we leverage a DeepFM model to recommend an optimal pickup position for passengers. We share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, temporal variations, and abnormal cell towers in terms of four major service metrics, i.e., pickup position error, over-30-meters ratio, cancel ratio, and call ratio. The large-scale evaluations show that our system achieves a \u0000<inline-formula><tex-math>$ 0.54,text{m}$</tex-math></inline-formula>\u0000 lower median pickup position error compared to the iOS built-in cellular localization system, regardless of environmental changes, smartphone brands/models, time, and cellular providers. Additionally, the over-30-meters ratio, cancel ratio, and call ratio have significant reductions of 0.88%, 0.88%, and 5.13%, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15114-15131"},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180592","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}