IEEE Transactions on Mobile Computing最新文献

{"title":"Invisible, Yet Trusted: Blockchain-Empowered Privacy-Preserving Spatio-Temporal Task Matching in Crowdsourcing With Efficient Public Verification","authors":"Xu Yang;Saiyu Qi;Jiayu Zhou;Zheng He;Yong Qi","doi":"10.1109/TMC.2026.3654940","DOIUrl":"https://doi.org/10.1109/TMC.2026.3654940","url":null,"abstract":"Task matching is a core component of crowdsourcing systems, enabling efficient assignment of tasks to appropriate workers. However, this process often requires both task requesters and workers to disclose sensitive contextual information, such as spatial locations and temporal availability, raising serious privacy concerns. Although various cryptographic techniques and blockchain-based solutions have been proposed to preserve privacy and enhance trust, existing schemes still face three critical limitations: reliance on centralized key authorities, lack of support for fine-grained spatio-temporal constraints, and inefficient public verification mechanisms. To address these challenges, we propose PVSTMatch, a blockchain-empowered privacy-preserving spatio-temporal task matching scheme in crowdsourcing. PVSTMatch eliminates the need for trusted third parties through an authority-free authorization mechanism, supports secure spatio-temporal matching by designing a compact secure comparison method, and enables efficient public verifiability via a succinct verification mechanism. Furthermore, we introduce GE-PVSTMatch, a gas-efficient variant that significantly reduces on-chain storage costs. Security analysis and performance evaluation demonstrate that our schemes achieve strong bilateral privacy, verification correctness, and practical efficiency, making them well-suited for real-world decentralized crowdsourcing platforms.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9660-9675"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518554","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":"When Vehicle Crowd-Sensing Meets Complex Driving: Robust Illegal Roadside Parking Detections","authors":"Ruixue Huang;Chaocan Xiang;Yulan Guo","doi":"10.1109/TMC.2026.3665356","DOIUrl":"https://doi.org/10.1109/TMC.2026.3665356","url":null,"abstract":"Illegal roadside parking is highly common in major cities worldwide, leading to traffic congestion & accidents and undermining human safety. Traditional methods for detecting illegal parking rely heavily on active human effort and customized sensors, which are extremely cost-ineffective to cover large-scale cities. To this end, we explore utilizing massive on-road vehicles to collect parking violation-related data (including surrounding videos and driving state information), enabling large-scale and low-cost illegal parking detection. However, the dynamic and unpredictable motion of the sensing and target vehicles, coupled with intricate driving behaviors and traffic conditions, pose great challenges to achieving accurate detection. To address these challenges, we propose <inline-formula><tex-math>$i$</tex-math></inline-formula><i>Patrol+</i>, a novel <i>robust</i> illegal parking detection system empowered by vehicle crowdsensing, at the heart of which lies <i>a key extension of the Doppler effect from the traditional acoustic scenarios to the vehicle-mounted video scenarios in intricate driving conditions</i>. Specifically, following the methodology of the Doppler effect and leveraging camera imaging theory, we establish a new vehicle speed estimation model via frame-to-frame feature shifts. Furthermore, this model is augmented by a feature re-projection scheme and a driving behavior-aware bias rectification scheme to effectively alleviate the negative effect of intricate driving conditions. We implement <inline-formula><tex-math>$i$</tex-math></inline-formula><i>Patrol+</i> on smartphones mounted behind the vehicle windshields and conduct on-road experiments covering 233 <inline-formula><tex-math>$rm {km}$</tex-math></inline-formula> of roads in a 125 <inline-formula><tex-math>$rm {km}^{2}$</tex-math></inline-formula> urban area. The experimental results demonstrate that <inline-formula><tex-math>$i$</tex-math></inline-formula><i>Patrol+</i> identifies illegal parking events with a precision of 86% in intricate driving conditions (e.g., turns/u-turns, lane changes, traffic congestion, and waiting at traffic signals), outperforming five baselines by 17% on average.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10688-10705"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518556","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":"Adaptive Quantization and Differential Privacy Federated Learning Framework","authors":"Minh Hai Nguyen;Chi-Hieu Nguyen;Diep N. Nguyen;Dinh Thai Hoang;Mohammad Abu Alsheikh","doi":"10.1109/TMC.2026.3665886","DOIUrl":"https://doi.org/10.1109/TMC.2026.3665886","url":null,"abstract":"Federated Learning (FL) enables devices to collaboratively train machine learning models without sharing raw data, promoting privacy-preserving AI. However, practical deployment faces challenges in balancing the data privacy, communication overhead, and the training convergence rate. For instance, adding noise to local models to preserve privacy can increase the size of updates, exacerbating communication overhead and reducing the convergence rate, while coarse quantization reduces communication costs but can degrade model accuracy. This paper introduces a novel integration of diverse quantization schemes, including both uniform and adaptive quantization, synergistically paired with additive noise mechanisms, to optimally trade off the model/training precision/rate, communication overhead, and privacy protection. By adapting quantization levels based on training dynamics, including gradient variance and model convergence, our approach minimizes the learning error upper bound while ensuring theoretically quantified differential privacy and achieves significant savings in the number of communicated bits. To the best of our knowledge, this is the first work to integrate adaptive quantization with additive noise in FL. More importantly, we provide theoretical guarantees for differential privacy and convergence of the proposed framework and empirically evaluate its communication-privacy tradeoffs. Experimental results on popular datasets like MNIST, CIFAR demonstrate that our method enables the training of convolutional neural networks with less than 4-bit quantization, achieving privacy budgets as low as 1.0, while maintaining accuracy that approaches the standard, non-differentially private FedAvg algorithm.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10335-10350"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518845","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":"Smart Interrupted Routing Based on Multi-Head Attention Mask Mechanism-Driven MARL in Software-Defined UASNs","authors":"Zhenyu Wang;Chuan Lin;Guangjie Han;Shengchao Zhu;Ruoyuan Wu;Tongwei Zhang;Jialu Tian","doi":"10.1109/TMC.2026.3663303","DOIUrl":"https://doi.org/10.1109/TMC.2026.3663303","url":null,"abstract":"Routing-driven timely data collection in Underwater Acoustic Sensor Networks (UASNs) is crucial for marine environmental monitoring, disaster warning, and underwater resource exploration, etc. However, harsh underwater conditions, including high delays, limited bandwidth, and dynamic topologies, make efficient routing decisions challenging in UASNs. In this paper, we propose a smart interrupted routing scheme for UASNs to address dynamic underwater challenges. We first model underwater noise influences from real underwater routing features, e.g., turbulence and storms. We then propose a Software-Defined Networking (SDN)-based Interrupted Software-defined UASNs Reinforcement Learning (ISURL) framework, which ensures adaptive routing through dynamical failure handling (e.g., energy depletion of sensor nodes or link instability) and real-time interrupted recovery. Based on ISURL, we propose the MA-MAPPO algorithm, integrating multi-head attention mask mechanism with MAPPO to filter out infeasible actions and streamline training. Furthermore, to support interrupted data routing in UASNs, we introduce MA-MAPPO_i, MA-MAPPO with interrupted policy, to enable smart interrupted routing decisions in UASNs. The evaluations demonstrate that our proposed routing scheme achieves exact underwater data routing decisions with faster convergence speed and lower routing delays than existing approaches.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10166-10181"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518839","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":"Prototyping and Analyzing Mobile SoC Clusters as Modern Edge Servers","authors":"Li Zhang;Boqing Shi;Xiang Li;Ao Zhou;Xiao Ma;Shangguang Wang;Mengwei Xu","doi":"10.1109/TMC.2026.3657619","DOIUrl":"https://doi.org/10.1109/TMC.2026.3657619","url":null,"abstract":"The rapidly growing edge computing platforms, coupled with the still imperfect edge infrastructure, present an excellent opportunity for the emergence of new edge hardware. However, it remains unclear whether alternative architectures built from energy-efficient mobile System-on-Chips (SoCs) can meet the stringent performance, cost, and energy demands of modern edge workloads. In this paper, we propose a new type of edge server composed of 60 Qualcomm Snapdragon 865 mobile SoCs in a 2 U rack, referred to as SoC Cluster. We demonstrate its successful deployment on existing edge cloud platforms and its ability to natively serve mobile cloud gaming services. Despite the emergence of new hardware on edge platforms and its successful operation in serving mobile cloud gaming, our trace analysis revealed low hardware utilization and significant dynamic fluctuations in usage. To assess its broader applicability, we conducted the first measurement study of SoC Cluster to reveal its ability to run two popular and modern edge applications: deep learning inference and video transcoding. We developed a cross-platform benchmark suite to evaluate throughput, latency, power consumption, and application-specific metrics like video quality. We then directly compare SoC Cluster with a traditional edge server equipped with Intel CPUs and NVIDIA GPUs in terms of energy efficiency, space efficiency, and monetary cost. Results show that SoC Cluster exhibits up to 6.5× higher energy efficiency and 7.7× higher space efficiency. We also disclose its limitations in serving computation-intensive workloads such as large deep learning models. The outcomes provide insightful implications and offer practical direction for refining SoC Cluster toward broader deployment in edge scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10130-10147"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518840","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":"Edge-Assisted Multi-Vehicle Cooperative Perception: An Approach Based on Relative Pose Estimations","authors":"Yu Zhao;He Sun;Mingjun Xiao;Jie Wu;Junjie Shao;Jinbo Cai","doi":"10.1109/TMC.2026.3664614","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664614","url":null,"abstract":"Cooperative perception is a crucial technology to improve the safety and robustness of autonomous driving systems. Existing works mainly utilize a pre-constructed 3D map or roadside infrastructures to estimate poses for cooperative perception. However, the pre-constructed map should be updated frequently, and roadside infrastructures can only be employed on specific roads. To overcome these limitations, we exploit the inherent features of external shapes of vehicles for relative pose estimation and design an Edge-assisted multi-Vehicle cOoperative perception system, called EVO. EVO adopts a suit of approaches to ensure the accuracy and real-time performance of cooperative perception: (1) a novel approach is proposed to estimate relative poses without requiring any pre-constructed 3D map or roadside infrastructures; (2) a hierarchical registration module is adopted to speed up pose estimation; (3) a Regions of Interest (RoIs) prediction module based on Dlinear and a registrations reduction module are designed to reduce the computation latency. We evaluate EVO on a CARLA-based dataset and demonstrate that EVO can perform multi-vehicle cooperative perception in real-time and maintain centimeter-level accuracy in pose estimation. Further, EVO extends the maximum perception distance from 32-39 m to 38-67 m and achieves about 2.43× improvement in perception area.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10211-10224"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519024","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":"Breaking Cloud Dependencies: A Distributed Ledger Approach to IoT Device Usufruct Management","authors":"Chen Zeng;Xiaohui Peng;Fan Liang;Yifan Wang;Deke Guo","doi":"10.1109/TMC.2026.3655119","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655119","url":null,"abstract":"Prevailing cloud-centric Internet of Things (IoT) access control faces critical challenges in meeting evolving operational demands, particularly within IoT leasing ecosystems, where inherent vulnerabilities enable lessees to retain unauthorized post-lease access, and current systems fail to constrain lessor misconduct (e.g., double-leasing, illicit revocation). To address these limitations, we propose Menshen, a Distributed Ledger Technology (DLT)-based framework that breaks cloud dependencies through three key contributions: a resource-efficient Computable Trust Model (CTM) for peer-to-peer consensus, an authentication (authN) and authorization (authZ) mechanism for secure usufruct (leasehold right) management, and a Universal Computer Account (UCA) for user-controlled data storage. By leveraging a cryptographic data structure rather than energy-intensive mining for consensus, Menshen shifts the paradigm from infrastructure-centric to user-centric, thereby obviating the need for intermediate clouds to enhance operational flexibility, particularly in the enforcement of legally compliant usufruct. Evaluated on multiple IoT platforms within a Local Area Network (LAN), Menshen’s UCA executes micro-operations under 300 <inline-formula><tex-math>$mu s$</tex-math></inline-formula> on most platforms, demonstrating a 40% reduction in minimum latency and a 33% improvement in maximum throughput over a traditional cloud-based system.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9737-9753"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519208","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":"Mosaic: Towards Efficient and Low-Latency Multi-NeRF Complex Scene Cloud Rendering","authors":"Zhe Wang;Yifei Zhu;Hongming Xu;Meng Han;Linghe Kong","doi":"10.1109/TMC.2026.3667026","DOIUrl":"https://doi.org/10.1109/TMC.2026.3667026","url":null,"abstract":"Neural Radiance Fields (NeRF) offers superior reconstruction accuracy over traditional methods like point clouds. This is particularly advantageous for applications in retail, VR, and navigation. However, when representing a complex scene with multiple semantically meaningful objects, especially those containing high-frequency details, the conventional single NeRF approach struggles to accurately capture these intricate details. Furthermore, the state-of-the-art multi-NeRF solutions, though effectively capturing details by representing each object as a separate NeRF, are too resource-intensive to be run on mobile devices. Existing NeRF rendering acceleration studies focus on single NeRF data representation and its framework improvements, without resorting to the visibility information for further optimization and considering the practical multi-NeRF rendering scenario. In this paper, we present Mosaic, a multi-NeRF cloud rendering system that facilitates high-fidelity, and low-latency cloud rendering for complex scenes. We first design a multi-resolution controller to dynamically determine the resolution for each pixel based on its contextual details and view direction. It then incorporates a visibility-aware region pruner to effectively eliminate unneeded pixels to avoid being rendered. The extensive experiments on various scenes show that Mosaic can achieve high-fidelity rendering with 43% lower transmission cost and up to 70% smaller computation consumption when compared with other state-of-the-art benchmarks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10956-10968"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148516331","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":"Heterogeneity-Aware Semi-Supervised Federated Learning With Client-to-Client Collaboration","authors":"Yang Xu;Zhiwei Yao;Hongli Xu;Yunming Liao;Ying Zhu;Xitong Fu","doi":"10.1109/TMC.2026.3665045","DOIUrl":"https://doi.org/10.1109/TMC.2026.3665045","url":null,"abstract":"Federated learning (FL) enables multiple clients to collaboratively train models without exposing raw data. However, assuming all clients have fully labeled data is impractical for many real-world applications. To this end, we focus on semi-supervised FL (SSFL), where client data is partially labeled. Existing SSFL methods often ignore three inherent characteristics of FL: limited communication resources, data heterogeneity and system heterogeneity, which severely hinder convergence stability and efficiency. This paper proposes E-FedAC, a novel SSFL framework that tackles these challenges through alternating client-to-client (C2C) collaboration. Specifically, E-FedAC employs a two-stage clustering strategy in each training round while concurrently considering client computing and communication capabilities to mitigate the impact of intra-cluster stragglers. The first stage employs similarity clustering to group clients based on local data distribution, which allows similar clients to share knowledge and generate high-quality pseudo-labels for unlabeled data. In the second stage, clients are re-grouped using dissimilarity clustering to approximate the IID setting at the cluster level, thereby alleviating the bias induced by Non-IID data. E-FedAC utilizes a reinforcement learning algorithm to adaptively determine the average number of intra-cluster iterations across all clusters for both stages, balancing labeling assistance from similar clients and unbiased optimization from dissimilar clients. Additionally, E-FedAC dynamically optimizes local updating frequencies for each generated cluster based on this average and individual cluster capabilities, further reducing the waiting time across clusters. Extensive evaluations demonstrate that E-FedAC can provide up to 4.02× speedup and 61.74% savings in communication costs compared with existing benchmarks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10706-10723"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148516336","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":"Hybrid Orchestration of AI Services and Microservices in Cloud-Edge Collaboration","authors":"Kai Peng;Xudong Liu;Di Han;Yi Hu;Menglan Hu;Chao Cai;Zehui Xiong","doi":"10.1109/TMC.2026.3654236","DOIUrl":"https://doi.org/10.1109/TMC.2026.3654236","url":null,"abstract":"The rapid development of AI accelerates the implementation and delivery of AI applications in diverse fields. In cloud-edge collaboration, delivering a complete AI application relies on the robust coordination between AI-supporting microservices and AI services. However, most existing studies only coarsely considered monolithic AI service orchestration while neglecting microservice orchestration. Such coarse-grained orchestration severely impacts application performance. To enable diverse high-performance AI applications, fine-grained hybrid orchestration of AI services and microservices (HOAIM) is highly desirable, yet presents formidable challenges. Due to heterogeneous services, call dependencies, and service multiplexing, fine-grained hybrid orchestration modeling is highly non-trivial. Moreover, the tight coupling between deployment and routing results in a complex joint optimization problem. To address this, we first propose a heterogeneous service orchestration network that supports orchestration optimization and automated management. Then, based on queuing networks and multi-instance models, we conduct an accurate analysis of delay and load. Furthermore, to achieve efficient hybrid orchestration, we propose preference-driven resource allocation and instance computation algorithms, along with reinforcement learning with action masking and reward shaping. Finally, extensive trace-driven simulations demonstrate that our algorithms optimize average response delay by up to 41.83%, and achieve significant advantages in load balancing, response success rate, and resource efficiency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9382-9400"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517108","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}