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

MuSAC: Toward Communication-Free Sensory Data Acquisition 迈向无通信的传感数据采集

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-13 DOI: 10.1109/TMC.2026.3664511

Sijie Ji;Yiwei Wang;Lixiang Lian

{"title":"MuSAC: Toward Communication-Free Sensory Data Acquisition","authors":"Sijie Ji;Yiwei Wang;Lixiang Lian","doi":"10.1109/TMC.2026.3664511","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664511","url":null,"abstract":"Sensing and communication are at the core of the Internet of Things, which usually function independently. For example, a smartphone can communicate over Wi-Fi or cellular networks while continuously acquiring sensory data from the environment through various sensors. This paper presents a novel framework, MuSAC (Mutualistic Sensing and Communication), which seamlessly integrates the collection of sensory data with existing communication systems, without adding any additional communication overhead (i.e., communication-free). The framework leverages the mutualistic relationship between specific communication data and sensory data to effectively crowdsource heterogeneous sensory data without harming communication performance in practical distributed systems. To embed massive sensory data into the current transmission of communication data, MuSAC presents novel neural networks to distill universal features from the raw data for compression at the sender side and then extract invariant features on the server side. By doing so, MuSAC eliminates additional communication overhead for sensory data collection while also mitigating privacy concerns and data heterogeneity in crowdsensing. To evaluate the performance of MuSAC, we first conduct system-level simulations in a distributed environment by embedding public human activity sensing datasets into cellular massive MIMO communication processes. The results demonstrate that MuSAC effectively supports heterogeneous sensory data collection over existing wireless communication links at zero additional communication overhead. We further implement a functional prototype of the MuSAC system using off-the-shelf devices, where real-world sensory data are seamlessly integrated into WiFi transmissions. This practical implementation demonstrates the viability of communication-free sensory data acquisition in real-world scenarios, paving the way for broader applications of MuSAC.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10420-10437"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518843","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

Forward Secure Identity-Based Encryption With Equality Test for Privacy-Preserving Friendship Recommendation on Social Platforms 基于前向安全身份加密的社交平台好友推荐等式检验

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-13 DOI: 10.1109/TMC.2026.3664502

Wenjie Yang;Yu Cai;Futai Zhang;Anjia Yang

{"title":"Forward Secure Identity-Based Encryption With Equality Test for Privacy-Preserving Friendship Recommendation on Social Platforms","authors":"Wenjie Yang;Yu Cai;Futai Zhang;Anjia Yang","doi":"10.1109/TMC.2026.3664502","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664502","url":null,"abstract":"On social platforms, the friendship recommendation models deployed suggest potential friends based on the similarities among users. As privacy concerns rise, more users choose to encrypt their attributes before requesting recommendation services. Identity-based encryption with equality test (IBEET) supports encrypted data comparison under different users’ identities (public keys) and avoids the need for costly certificate management. This makes it highly suitable for implementing privacy-preserving friendship recommendation on social platforms. Nevertheless, most existing IBEET schemes lack the ability to control the validity period of trapdoors used for testing. Historical trapdoors may be maliciously utilized to test encrypted data generated in the future. To address this issue, we propose a forward secure identity-based encryption with equality test for friendship recommendation. Our construction enables an authorized tester to provide privacy-preserving recommendations based on encrypted data among different users. Furthermore, to control the tester’s testing capability, time slots are encoded by the 0/1-encoding mechanism and embedded into both ciphertexts and trapdoors. This ensures that only the authorized tester possessing valid trapdoors can perform tests on specific ciphertexts. Under the bilinear Diffie-Hellman assumption in the random oracle model, we prove that the proposed IBEET achieves OW-ID-CCA security when the attacker has access to the trapdoor and IND-ID-CCA security when the attacker does not. Experiment simulation demonstrates that our construction outperforms existing ones in overall performance, particularly in testing that requires frequent execution for recommendation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10818-10832"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518947","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

KDB-Gaze: Keypoint-Guided Dual-Branch Learning for Gaze Estimation KDB-Gaze：用于注视估计的关键点引导双分支学习

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-20 DOI: 10.1109/TMC.2026.3655740

Xiaodong Wang;Xiaowei Bai;Liang Xie;Qining Wang;Hao Su;Erwei Yin

{"title":"KDB-Gaze: Keypoint-Guided Dual-Branch Learning for Gaze Estimation","authors":"Xiaodong Wang;Xiaowei Bai;Liang Xie;Qining Wang;Hao Su;Erwei Yin","doi":"10.1109/TMC.2026.3655740","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655740","url":null,"abstract":"Near-eye gaze estimation has been a core technology for natural interaction in smart head-mounted devices. Existing gaze estimation approaches have the following limitations: (1) Data-driven methods learn nonlinear mappings directly from images to gaze direction but ignore the physiological characteristics of the iris–pupil system. (2) Model-driven methods rely on hand-designed modeling and perform poorly in complex, noisy scenes. (3) Hybrid-driven methods attempt to combine both paradigms but are restricted by simplified model designs. To address these issues, we propose KDB-Gaze, a <bold>Keypoint-Guided <bold>Dual-<bold>Branch learning for near-eye <bold>Gaze estimation. First, a backbone and a 3-Pass path aggregation feature pyramid network effectively capture multi-scale ocular features. Then, two parallel branches refine the features: an implicit spatial modeling branch employs deformable DETR to capture nonlinear features, while an explicit topological constraint branch uses a graph attention network to provide stable physiological guidance. Finally, the dual-branch features are fused and optimized jointly with a multi-task loss for precise prediction. Extensive experiments on the TEyeD, LPW, and NVGaze datasets verify the state-of-the-art performance of the proposed method.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10030-10044"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148515691","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

Hybrid Computing of Decentralized Applications in Edge Web 3.0: A Scheduling Strategy via Decision Transformer 边缘Web 3.0中分散应用的混合计算：一种基于决策转换器的调度策略

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-19 DOI: 10.1109/TMC.2026.3666350

Zhongqi Miao;Xichun Cai;Lixing Chen;Yang Bai;Hongfu Liu;Zhe Qu;Pan Zhou;Xinping Guan

{"title":"Hybrid Computing of Decentralized Applications in Edge Web 3.0: A Scheduling Strategy via Decision Transformer","authors":"Zhongqi Miao;Xichun Cai;Lixing Chen;Yang Bai;Hongfu Liu;Zhe Qu;Pan Zhou;Xinping Guan","doi":"10.1109/TMC.2026.3666350","DOIUrl":"https://doi.org/10.1109/TMC.2026.3666350","url":null,"abstract":"Web 3.0 and edge computing offer a synergistic foundation for secure and efficient distributed service platforms, enabling the deployment of decentralized applications (DApps). However, optimizing execution mode decisions for interdependent task patterns remains a significant challenge due to high computational overhead and blockchain consensus latency. This paper proposes an elastic hybrid computing architecture for Web 3.0-Enabled Edge Computing Systems (<sc>Edge Web 3.0) that integrates on-chain and off-chain execution to enhance computational efficiency and mitigate blockchain overhead. DApp contracts are modeled as Directed Acyclic Graphs (DAGs) and partitioned into dependent task patterns, enabling a structured decomposition of execution. To optimize execution modes while minimizing gas costs and service delays, we formulate a DApp task scheduling problem and introduce the <underline>Decision <underline>Transformer-based <underline>Dependent <underline>Scheduling (DTDS) algorithm. DTDS leverages a transformer network with masked self-attention to effectively capture dependencies within the DApp DAG. The decision transformer is further refined with regularization terms to address constraints including service latency, gas consumption, and computing capacity. We establish an <sc>Edge Web 3.0 testbed utilizing Goerli, zkSync, and Ethereum, and evaluate the performance of DTDS. Experimental results demonstrate that the proposed method outperforms state-of-the-art benchmarks, offering a scalable and efficient solution for DApp execution in <sc>Edge Web 3.0 environments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10910-10923"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148516332","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

GroupNL: Low-Resource and Robust CNN Design Over Cloud and Device GroupNL：基于云和设备的低资源和鲁棒CNN设计

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-20 DOI: 10.1109/TMC.2026.3655770

Chuntao Ding;Jianhang Xie;Junna Zhang;Salman Raza;Shangguang Wang;Jiannong Cao

{"title":"GroupNL: Low-Resource and Robust CNN Design Over Cloud and Device","authors":"Chuntao Ding;Jianhang Xie;Junna Zhang;Salman Raza;Shangguang Wang;Jiannong Cao","doi":"10.1109/TMC.2026.3655770","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655770","url":null,"abstract":"Deploying Convolutional Neural Network (CNN) models on ubiquitous Internet of Things (IoT) devices in a cloud-assisted manner to provide users with a variety of high-quality services has become mainstream. Most existing studies speed up model cloud training/on-device inference by reducing the number of convolution (Conv) parameters and floating-point operations (FLOPs). However, they usually employ two or more lightweight operations (e.g., depthwise Conv, <inline-formula><tex-math>$1times 1$</tex-math></inline-formula> cheap Conv) to replace a Conv, which can still affect the model's speedup even with fewer parameters and FLOPs. To this end, we propose the Grouped NonLinear transformation generation method (GroupNL), leveraging data-agnostic, hyperparameters-fixed, and lightweight Nonlinear Transformation Functions (NLFs) to generate diversified feature maps on demand via grouping, thereby reducing resource consumption while improving the robustness of CNNs. First, in a GroupNL Conv layer, a small set of feature maps, i.e., seed feature maps, are generated based on the seed Conv operation. Then, we split seed feature maps into several groups, each with a set of different NLFs, to generate the required number of diversified feature maps with tensor manipulation operators and nonlinear processing in a lightweight manner without additional Conv operations. We further introduce a sparse GroupNL Conv to speed up by reasonably designing the seed Conv groups between the number of input channels and seed feature maps. Experiments conducted on benchmarks and on-device resource measurements demonstrate that the GroupNL Conv is an impressive alternative to Conv layers in baseline models. Specifically, on Icons-50 dataset, the accuracy of GroupNL-ResNet-18 is 2.86% higher than ResNet-18; on ImageNet-C dataset, the accuracy of GroupNL-EfficientNet-ES achieves about 1.1% higher than EfficientNet-ES. In addition, we verified the efficiency of GroupNL-based models in terms of cloud training and on-device inference.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9534-9548"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148516334","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

Cooperative Content Caching in Vehicular Edge Computing Networks: A Two-Stage Deep Reinforcement Learning Approach 车辆边缘计算网络中的协同内容缓存：一种两阶段深度强化学习方法

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-13 DOI: 10.1109/TMC.2026.3664597

Hongbo Jiang;Jianghao Guo;Zhu Xiao;Jiali Yang;Kehua Yang;Geyong Min

{"title":"Cooperative Content Caching in Vehicular Edge Computing Networks: A Two-Stage Deep Reinforcement Learning Approach","authors":"Hongbo Jiang;Jianghao Guo;Zhu Xiao;Jiali Yang;Kehua Yang;Geyong Min","doi":"10.1109/TMC.2026.3664597","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664597","url":null,"abstract":"In vehicular edge computing (VEC) networks, by implementing content caching and V2X connectivity, road side unit (RSU) and nearby vehicles can serve as platforms for rapid data retrieval to address mobile traffic explosion. However, due to the dynamic and multi-constrained environment consisting of heterogeneous vehicles and RSU, it is challenging to meticulously plan cooperative caching policies. Additionally, due to the diversity of contents and the mobility of vehicles, the caching policy space is massive, which can be fatal for vehicles with limited computing and energy. In this paper, we formulate cooperative content caching in VEC networks as Markov decision process (MDP), configuring caching policies for vehicles and RSU. Our aim is to minimize Lyapunov drift and long-term delay. To address the massive caching policies, we propose a two-stage deep reinforcement learning (TS-DRL) algorithm. In the first stage, an improved ant colony algorithm is used to generate unilateral suggestions and construct action space to avoid the curse of dimensionality. In the second stage, we combine the Noisy Net and Double Deep Q-Learning Network to avoid overestimating value and efficient exploration problem. Simulation results show that TS-DRL outperforms advanced algorithms in terms of delay and cache hit rate.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10557-10576"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517669","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

NearSense: Exploring NearLink for New-Generation Wireless Sensing NearSense：探索新一代无线传感的NearLink

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-16 DOI: 10.1109/TMC.2026.3655026

Zijun Han;Xuanzhi Wang;Yang Li;Dan Wu;Hongliu Yang;Wanru Ning;Zhiyun Yao;Wenwei Li;Xingqing Cheng;Zixiang Ma;Daqing Zhang

{"title":"NearSense: Exploring NearLink for New-Generation Wireless Sensing","authors":"Zijun Han;Xuanzhi Wang;Yang Li;Dan Wu;Hongliu Yang;Wanru Ning;Zhiyun Yao;Wenwei Li;Xingqing Cheng;Zixiang Ma;Daqing Zhang","doi":"10.1109/TMC.2026.3655026","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655026","url":null,"abstract":"Recent years have witnessed considerable efforts in repurposing ubiquitous wireless communication signals for non-contact sensing. <inline-formula><tex-math>$mathbf{NearLink}$</tex-math></inline-formula> is a new-generation short-range wireless communication protocol, which is designed to address the high-quality network connectivity requirements of low power, low latency and high reliability. Given these notable advantages, NearLink has great potential for widespread application in various Internet of Things (IoT) areas. However, NearLink-based wireless sensing has not yet been explored. To bridge this gap, this work explores for the first time the sensing potential and opportunities of NearLink. Specifically, we systematically investigate the sensing capability of NearLink through answering two key questions: (1) How can NearLink’s communication-oriented signals be adapted for sensing tasks? (2) How can sensing performance be enhanced under multipath interference and low-power constraints? We prototype the NearLink sensing system-NearSense, and take the respiration detection as a case study to demonstrate its effectiveness. Extensive experiments demonstrate that NearSense can achieve an average detection rate of 98% and a false alarm rate below 1.5% in case of various real-life challenging interference. We believe this work opens up new directions for the new-generation wireless sensing towards high-quality network connections.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9863-9877"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517941","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

Dynamic and Heterogeneous Network Slicing for Vehicular Edge Computing Based on Two-Timescale Reinforcement Learning 基于双时间尺度强化学习的车辆边缘计算动态异构网络切片

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-17 DOI: 10.1109/TMC.2026.3665671

Xulong Li;Wencan Mao;Xinyi Xu;Yaxi Liu;Wei Huangfu;Keping Long;Yu Xiao;Yusheng Ji

{"title":"Dynamic and Heterogeneous Network Slicing for Vehicular Edge Computing Based on Two-Timescale Reinforcement Learning","authors":"Xulong Li;Wencan Mao;Xinyi Xu;Yaxi Liu;Wei Huangfu;Keping Long;Yu Xiao;Yusheng Ji","doi":"10.1109/TMC.2026.3665671","DOIUrl":"https://doi.org/10.1109/TMC.2026.3665671","url":null,"abstract":"Vehicular Edge Computing (VEC) is an essential part of the Internet of Vehicles (IoV) due to its low latency by moving the computational resources close to the edge. Although the introduction of network slicing into VEC improves resource utilization through dynamic resource allocation based on real-time demands and priorities, it increases the deployment and operational costs. In view of this, this paper envisions a resource allocation strategy for VEC based on network slicing technique, in which the tasks involved are not only dynamic but also heterogeneous. To minimize the system cost (including resource consumption and computation, network slice maintenance and reconfiguration costs), this paper proposes CST-RL, a confidence-based self-adjusting two-timescale reinforcement learning algorithm. This solution performs resource allocation and activation scheduling for network slices on a large timescale, while allocating slices to heterogeneous tasks on a short timescale to meet dynamic demands. In addition, we innovatively utilize critic in reinforcement learning to predict and compare the expected benefits of network slices with versus without reconfiguration. We introduce the Random Network Distillation (RND) technique to assess the confidence level of these benefits, thus providing guidance for network slices to automatically decide whether and when to undergo reconfiguration. Finally, we demonstrate the effectiveness and superiority of CST-RL through simulations. Results show that CST-RL yields 27.77% lower system cost compared to the scheme without network slicing and 15.15% lower system cost compared to performing constant network slicing configuration, with guaranteed Quality-of-Service.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10939-10955"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518215","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

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks 基于大语言模型的数字双边缘计算网络任务卸载与资源分配

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-16 DOI: 10.1109/TMC.2026.3664866

Qiong Wu;Yu Xie;Pingyi Fan;Dong Qin;Kezhi Wang;Nan Cheng;Khaled B. Letaief

引用次数: 0

GOTA-NU: Global Task Allocation With Non-Deterministic Tasks and Unknown Users to Enhance Freeway Traffic Accident Detection GOTA-NU：具有不确定性任务和未知用户的全局任务分配增强高速公路交通事故检测

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-12 DOI: 10.1109/TMC.2026.3664386

Qian Cao;Zhihui Li;Haitao Li;Shirui Zhou;Yali Zhao;Jinghao Xie

{"title":"GOTA-NU: Global Task Allocation With Non-Deterministic Tasks and Unknown Users to Enhance Freeway Traffic Accident Detection","authors":"Qian Cao;Zhihui Li;Haitao Li;Shirui Zhou;Yali Zhao;Jinghao Xie","doi":"10.1109/TMC.2026.3664386","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664386","url":null,"abstract":"Vehicular crowdsensing could enhance the traffic accident detection performance on freeways to the existing infrastructures by global task allocation. However, the uncertainty of traffic accidents and unknown of individual users bring difficulties for the task allocation. To address the problem, a global optimization method of task allocation with non-deterministic tasks and unknown users (GOTA-NU) is proposed. In the method, to reduce the influence of accident uncertainty, the accident risk is used to represent the sensing tasks of traffic accidents, and sensing task representation model is constructed by estimating the intrinsic temporal-spatial distribution of accident risk according to the wavelet transform and optimal transport theory. Meanwhile, to determine the users in future, a user estimation model is established according to macro statistical characteristics of traffic flow. Then the task allocation problem is transformed into a coverage problem for accident risk. A task allocation model is constructed by minimizing the user incentive cost with coverage level constraints of accident risk. And a greedy algorithm is proposed to solve it. To validate the proposed method, sensitivity experiments, robustness experiments, and comparison experiments are carried out on an open data source. The results show that the proposed method is efficient and reliable under different traffic conditions. The proposed method provides a reference for the long-term task allocation with non-deterministic tasks and unknown users.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10302-10316"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518192","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