IEEE Transactions on Mobile Computing最新文献_第2页

Intelligent End-to-End Deterministic Scheduling Across Converged Networks 跨融合网络的智能端到端确定性调度

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-16 DOI: 10.1109/TMC.2025.3530486

Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen

{"title":"Intelligent End-to-End Deterministic Scheduling Across Converged Networks","authors":"Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen","doi":"10.1109/TMC.2025.3530486","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530486","url":null,"abstract":"Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2504-2518"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563922","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

Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning 了解参与者及其可穿戴设备在联邦学习中的影响

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-16 DOI: 10.1109/TMC.2025.3530818

Rahul Mishra;Hari Prabhat Gupta

{"title":"Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning","authors":"Rahul Mishra;Hari Prabhat Gupta","doi":"10.1109/TMC.2025.3530818","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530818","url":null,"abstract":"The popularity of wearable smart devices has increased due to their seamless monitoring of vital signs during daily activities. Federated learning leverages these devices along with participants’ smartphones to fine-tune pre-trained models. Moreover, calibrating the differences between wearables and smartphones in terms of sampling rates, orientations, activity correlation, battery power, and other factors is challenging. Thus, the paper introduces a participant and wearable selection cross-device federated learning approach. It leverages criteria such as the activity wearable(s) relationship, data quality, battery life, sampling rate, and so on to perform the wearable selection. The server evaluates and estimates the utility of each participant and selects those with higher utility in each communication round. We then figure out the optimal weighted contribution of each participant to perform robust aggregation. We also use knowledge distillation techniques to develop a high-performing and lightweight wearable model. Finally, we conduct simulation and real-world experiments on existing datasets and compare our approach with state-of-the-art. The result shows an improvement of <inline-formula><tex-math>$3!!-!!4%$</tex-math></inline-formula> in accuracy via fine-tuning from selected wearable data.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5003-5015"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918777","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

Open-Set Occluded Person Identification With mmWave Radar 毫米波雷达开集闭塞人员识别

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3529735

Tao Wang;Yang Zhao;Ming-Ching Chang;Jie Liu

{"title":"Open-Set Occluded Person Identification With mmWave Radar","authors":"Tao Wang;Yang Zhao;Ming-Ching Chang;Jie Liu","doi":"10.1109/TMC.2025.3529735","DOIUrl":"https://doi.org/10.1109/TMC.2025.3529735","url":null,"abstract":"Radio frequency sensors can penetrate non-metal objects and provide complementary information to vision sensors for person identification (PID) purposes. However, there is a lack of research on millimeter wave (mmWave) radar for PID under occlusions, particularly in addressing the open-set recognition problem. Thus, we propose an open-set occluded PID (OSO-PID) framework that can deal with various obstacle and occlusion scenarios with open-set recognition capability. We first introduce a new dataset, mmWave-ocPID, comprising mmWave radar measurements and RGB-depth images, collected from 23 human subjects. We next design a novel neural network, mm-PIDNet, for occluded person identification using mmWave radar measurements. mm-PIDNet incorporates a transformer encoder, a bidirectional long short-term memory module, and a novel supervised contrastive learning module to improve PID performance. For open-set recognition, we enhance the mmWave radar-based PID method by integrating supervised contrastive learning with the Weibull models, which can identify out-of-distribution samples. We perform extensive indoor experiments with a variety of obstacles and occlusion scenarios. Our experimental results show that mm-PIDNet achieves an F1-score of 0.93 on average, outperforming state-of-the-art methods by up to 13.41% for occluded cases. For open-set PID, the OSO-PID framework achieves an F1-score above 0.8 when the openness is less than 14.36%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5229-5244"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918647","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

ML-Track: Passive Human Tracking Using WiFi Multi-Link Round-Trip CSI and Particle Filter ML-Track：使用WiFi多链路往返CSI和粒子滤波的被动人体跟踪

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3529897

Fangzhan Shi;Wenda Li;Chong Tang;Yuan Fang;Paul V. Brennan;Kevin Chetty

{"title":"ML-Track: Passive Human Tracking Using WiFi Multi-Link Round-Trip CSI and Particle Filter","authors":"Fangzhan Shi;Wenda Li;Chong Tang;Yuan Fang;Paul V. Brennan;Kevin Chetty","doi":"10.1109/TMC.2025.3529897","DOIUrl":"https://doi.org/10.1109/TMC.2025.3529897","url":null,"abstract":"In this study, we present ML-Track, an innovative uncooperative passive tracking system leveraging WiFi communication signals between multiple devices. Our approach is realized with three pivotal techniques. First, we introduce a novel protocol termed multi-link round-trip CSI, which enables multi-link bistatic Doppler detection within a WiFi network. Second, a phase error cancellation method is developed, and we demonstrate a 0.92 rad reduction in error (0.96 to 0.04 rad) experimentally. Lastly, we propose a particle-filter-based back-end to track a moving human in the room passively without the need for the participant to carry any type of cooperative or active device. A prototype system is constructed using four Raspberry Pi CM4 units and subjected to real-world evaluations. Experimental results indicate a median error of approximately 0.23 m for tracking, which corresponds to a relative error of 5.8% based on the 4 m side length of the experimental field. Compared to existing studies, a distinct advantage of our system is it can run with non-MIMO (single-antenna) WiFi devices, making it particularly suitable for budget or low-profile WiFi hardware. This compatibility makes it an ideal fit for real-world Internet-of-Things (IoT) devices. Moreover, in terms of computational demands, our solution excels, delivering real-time performance on the Raspberry Pi CM4 while utilizing just 20% of its CPU capability and drawing a modest 2.5 watts of power.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5155-5172"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918730","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

SnapCFL: A Pre-Clustering-Based Clustered Federated Learning Framework for Data and System Heterogeneities SnapCFL：一个基于预聚类的数据和系统异构聚类联邦学习框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3529487

Yujun Cheng;Weiting Zhang;Zhewei Zhang;Jiawen Kang;Qi Xu;Shengjin Wang;Dusit Niyato

{"title":"SnapCFL: A Pre-Clustering-Based Clustered Federated Learning Framework for Data and System Heterogeneities","authors":"Yujun Cheng;Weiting Zhang;Zhewei Zhang;Jiawen Kang;Qi Xu;Shengjin Wang;Dusit Niyato","doi":"10.1109/TMC.2025.3529487","DOIUrl":"https://doi.org/10.1109/TMC.2025.3529487","url":null,"abstract":"Federated Learning (FL) has emerged as a promising framework to address data privacy concerns associated with mobile devices, in contrast to conventional Machine Learning (ML). However, traditional FL encounters significant challenges due to the heterogeneities among different clients. Clustered Federated Learning (CFL) has demonstrated effectiveness in mitigating the data heterogeneity challenge, which significantly limits a broader application of FL. Nevertheless, existing CFL approaches often tightly couple the clustering process with the main FL process, affecting the flexibility and performance of CFL. In this paper, we propose a pre-clustering-based CFL approach, named SnapCFL, which decouples the CFL process into pre-clustering and main FL stages, considering both the impact of heterogeneity on CFL accuracy and the framework's flexibility. The pre-clustering stage models the measurement of data similarity as a two-sample hypothesis testing problem to more accurately group clients and alleviate data heterogeneity. In the main FL stage, a constraint-based client selection method is employed to address the system heterogeneity problem. We conduct extensive experiments using popular datasets with various heterogeneity settings. The results demonstrate that SnapCFL achieves excellent performance in terms of accuracy and efficiency. Compared to five other state-of-the-art approaches, SnapCFL can improve model accuracy by 0.7%<inline-formula><tex-math>$sim$</tex-math></inline-formula>36.4%, and achieve the same level of accuracy with at least 0.08× the convergence time.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5214-5228"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918645","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

Efficient Subcarrier-Level OFDM Backscatter Communications 高效的子载波级OFDM反向散射通信

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3529265

Caihui Du;Jihong Yu;Zhenyu Yan;Ju Ren;Rongrong Zhang;Yun Li

{"title":"Efficient Subcarrier-Level OFDM Backscatter Communications","authors":"Caihui Du;Jihong Yu;Zhenyu Yan;Ju Ren;Rongrong Zhang;Yun Li","doi":"10.1109/TMC.2025.3529265","DOIUrl":"https://doi.org/10.1109/TMC.2025.3529265","url":null,"abstract":"Most of the existing OFDM backscatter systems adopt phase-modulated schemes to embed tag data, suffering from symbol-level modulation limitation, heavy synchronization accuracy reliance, and small tolerability to symbol time offset (STO) / carrier frequency (CFO) offset. We introduce SubScatter, the first subcarrier-level frequency-modulated OFDM backscatter which is able to tolerate bigger synchronization errors, STO, and CFO. The unique feature of SubScatter is our subcarrier shift keying (SSK) modulation. This method pushes the modulation granularity to the subcarrier by encoding and mapping tag data into different subcarrier patterns. We also design a tandem frequency shift (TFS) scheme that enables SSK with low cost and low power. Furthermore, we design SubScatter+ that shows these advantages while providing an even higher throughput without requiring more subcarrier patterns. We prototype and test SubScatter and SubScatter+, and the results show that our systems outperforms prior works in terms of effectiveness and robustness. Specifically, SubScatter has 743 kbps throughput that is 3.1 times and 14.9 times higher than RapidRider and MOXcatter, respectively. It also has a lower BER under noise and interferences which is over 6 times better than RapidRider or MOXcatter. Moreover, our proposed SubScatter+ could increase the throughput of SubScatter by 30%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5078-5093"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918799","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

DualRec: A Collaborative Training Framework for Device and Cloud Recommendation Models DualRec：设备和云推荐模型的协作训练框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3528967

Ye Zhang;Yongheng Deng;Sheng Yue;Qiushi Li;Ju Ren

{"title":"DualRec: A Collaborative Training Framework for Device and Cloud Recommendation Models","authors":"Ye Zhang;Yongheng Deng;Sheng Yue;Qiushi Li;Ju Ren","doi":"10.1109/TMC.2025.3528967","DOIUrl":"https://doi.org/10.1109/TMC.2025.3528967","url":null,"abstract":"Recommendation systems (RS) play a vital role in various domains. However, under recent data regulations like General Data Protection Regulation (GDPR), traditional RS that rely on collecting user's interaction data centrally face significant challenges. Federated learning (FL) enables collaborative model training among users while keeping their private data locally. Yet, the constrained resources of devices often limit the size of the learned model, resulting in suboptimal recommendation performance. To overcome the dilemma of data accessibility and model size, we propose DualRec, a novel collaborative training framework for device and cloud recommendation models. In DualRec, users train lightweight models on devices to harness their local private data, while a larger model is simultaneously trained on the cloud server to exploit its substantial resources. Devices and the cloud server collaboratively train their models, compensating for individual limitations of model size and data availability, enabling mutual empowerment and benefits. Specifically, we introduce an efficient aggregation mechanism for recommendation models to boost the collaborative training performance of device models. With the learned device models, we propose to generate pseudo user interaction data to train the server model. To enhance the training performance of the server model, we design an automated denoising mechanism to mitigate the negative impact of noisy samples in the generated pseudo dataset. Finally, the learned knowledge of the server model is distilled to device models for enhanced on-device recommendation performance. Extensive experiments demonstrate the superior performance of DualRec compared to state-of-the-art baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5202-5213"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918553","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

MADRL-Based Model Partitioning, Aggregation Control, and Resource Allocation for Cloud-Edge-Device Collaborative Split Federated Learning 基于madrl的云-边缘设备协同分离联邦学习模型划分、聚合控制和资源分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3530482

Wenhao Fan;Penghui Chen;Xiongfei Chun;Yuan’an Liu

{"title":"MADRL-Based Model Partitioning, Aggregation Control, and Resource Allocation for Cloud-Edge-Device Collaborative Split Federated Learning","authors":"Wenhao Fan;Penghui Chen;Xiongfei Chun;Yuan’an Liu","doi":"10.1109/TMC.2025.3530482","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530482","url":null,"abstract":"Split Federated Learning (SFL) has emerged as a promising paradigm to enhance FL by partitioning the Machine Learning (ML) model into parts and deploying them across clients and servers, effectively mitigating the workload on resource-constrained devices and preserving privacy. Compared to cloud-device-based and edge-device-based SFL, cloud-edge-device collaborative SFL offers both lower communication latency and wider network coverage. However, existing works adopt a uniform model partitioning strategy for different devices, ignoring the heterogeneous nature of device resources. This oversight leads to severe straggler problems, making the training process inefficient. Moreover, they do not consider joint optimization of model aggregation control and computing and communication resource allocation, and lack distributed algorithm design. To address these issues, we propose a joint resource management scheme for cloud-edge-device collaborative SFL to optimize the training latency and energy consumption of all devices. In our scheme, the partitioning strategy is optimized for each device based on resource heterogeneity. Meanwhile, we jointly optimize the aggregation frequency of ML models, computing resource allocation for all devices and edge servers, and transmit power allocation for all devices. We formulate a coordination game among all edge servers and then design a distributed optimization algorithm employing partially observable Multi-Agent Deep Reinforcement Learning (MADRL) with integrated numerical methods. Extensive experiments are conducted to validate the convergence of our algorithm and demonstrate the superiority of our scheme via evaluations under multiple scenarios and in comparison with four reference schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5324-5341"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918798","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

Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks 针对移动合谋窃听者：无人机辅助MEC网络的协同安全传输与计算

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3529929

Mingxiong Zhao;Zirui Wang;Kun Guo;Rongqian Zhang;Tony Q. S. Quek

{"title":"Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks","authors":"Mingxiong Zhao;Zirui Wang;Kun Guo;Rongqian Zhang;Tony Q. S. Quek","doi":"10.1109/TMC.2025.3529929","DOIUrl":"https://doi.org/10.1109/TMC.2025.3529929","url":null,"abstract":"In Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users’ tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5280-5297"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918774","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

AAV Swarm Cooperative Search Based on Scalable Multiagent Deep Reinforcement Learning With Digital Twin-Enabled Sim-to-Real Transfer 基于可扩展多智能体深度强化学习的AAV群协同搜索

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-15 DOI: 10.1109/TMC.2025.3530438

Pan Cao;Lei Lei;Gaoqing Shen;Shengsuo Cai;Xiaojiao Liu;Xiaochang Liu

{"title":"AAV Swarm Cooperative Search Based on Scalable Multiagent Deep Reinforcement Learning With Digital Twin-Enabled Sim-to-Real Transfer","authors":"Pan Cao;Lei Lei;Gaoqing Shen;Shengsuo Cai;Xiaojiao Liu;Xiaochang Liu","doi":"10.1109/TMC.2025.3530438","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530438","url":null,"abstract":"Cooperative target search (CTS) technology is highly desirable in various multi-autonomous aerial vehicle (AAV) applications. However, searching for unknown targets in a dynamic threatening environment is a challenging problem, especially for AAVs with limited sensing range and communication capabilities. Besides, traditional searching methods lack scalability and efficient collaboration among the AAV swarm in dynamic environments. In this work, a digital twin (DT)-enabled distributed CTS approach was presented for AAV swarms and achieving sim-to-real transfer. Specifically, a new scalable multi-agent reinforcement learning (MARL) based algorithm called SAMARL is adopted to improve effectiveness and adaptability, combining a multi-head attention mechanism. In SAMARL, a scalable observation space with graph representation and an environmental cognition map is designed to thoroughly consider the target search rate, area coverage, and safety assurance. Then, a DT-driven training framework is proposed to facilitate the continuous evolution of MARL models and address the tradeoff between training speed and environment fidelity. Furthermore, we innovatively develop a distributed AAV swarm digital twin cooperative target search validation system, including real flight control, communication simulation tools, and a 3D physics engine. Extensive simulations validate its superiority compared to state-of-the-art strategies. More importantly, we also conduct real-world flight experiments on different scale mission areas and AAV swarms, further demonstrating the generalization and scalability of trained models.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5173-5188"},"PeriodicalIF":7.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918772","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