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

Exploring the Robustness: Hierarchical Federated Learning Framework for Object Detection of UAV Cluster 鲁棒性探索：用于无人机集群目标检测的分层联邦学习框架

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3562812

Xingyu Li;Wenzhe Zhang;Linfeng Liu;Jia Xu

{"title":"Exploring the Robustness: Hierarchical Federated Learning Framework for Object Detection of UAV Cluster","authors":"Xingyu Li;Wenzhe Zhang;Linfeng Liu;Jia Xu","doi":"10.1109/TMC.2025.3562812","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562812","url":null,"abstract":"The deployment of Unmanned Aerial Vehicle (UAV) cluster is an available solution for object detection missions. In the harsh environment, UAV cluster could suffer from some significant threats (e.g., forest fire hazards, electromagnetic interference, and ground-to-air attacks), which could lead to the destruction of UAVs and loss of data. To this end, we propose a Hierarchical Federated Learning Framework for Object Detection (HFL-OD) to enhance the robustness of UAV cluster conducting object detection missions. In HFL-OD, UAVs are grouped through a Three-Dimensional (3D) graph coloring method, and an intragroup backup mechanism is provided to prevent the data loss caused by the destruction of UAVs. Besides, a dynamic server selection mechanism deals with the potential destruction of servers (cluster server and group servers) by adaptively reassigning the server roles. To further improve the robustness and mission efficiency of UAV cluster, a two-tier federated learning framework is introduced to make a proper trade-off between object detection accuracy and communication/computational overhead. This framework is built on the concept of hierarchical federated learning by implementing both intragroup parameter aggregation and global parameter aggregation. Extensive simulations and comparisons demonstrate the superior performance of our proposed HFL-OD, i.e., the robustness of UAV cluster conducting object detection missions can be significantly improved, and the communication/computational overhead is effectively reduced.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9489-9505"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036891","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

Semantic-Enhanced Representation Learning for Road Networks With Temporal Dynamics 具有时间动态的道路网络语义增强表示学习

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-21 DOI: 10.1109/TMC.2025.3562656

Yile Chen;Xiucheng Li;Gao Cong;Zhifeng Bao;Cheng Long

{"title":"Semantic-Enhanced Representation Learning for Road Networks With Temporal Dynamics","authors":"Yile Chen;Xiucheng Li;Gao Cong;Zhifeng Bao;Cheng Long","doi":"10.1109/TMC.2025.3562656","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562656","url":null,"abstract":"The widespread adoption of mobile devices and positioning technology has resulted in the generation of massive urban data, offering great opportunities to improve analytical abilities for urban infrastructure components. In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks. Specifically, we propose to encode two pivotal semantic characteristics intrinsic to road networks: traffic patterns and traveling semantics. To achieve this, we refine the skip-gram module by incorporating auxiliary objectives aimed at predicting the traffic context associated with a target road segment. Moreover, we leverage mobile trajectory data and design pre-training strategies based on Transformer to distill traveling semantics on road networks. DyToast further augments this framework by employing unified trigonometric functions characterized by their beneficial properties, enabling the capture of temporal evolution and dynamic nature of road networks more effectively. With these proposed techniques, we can obtain representations that encode multi-faceted aspects of knowledge within road networks, applicable across both road segment-based applications and trajectory-based applications. Extensive experiments on two real-world datasets across three tasks demonstrate that our proposed framework consistently outperforms the state-of-the-art baselines by a significant margin.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9413-9427"},"PeriodicalIF":9.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036879","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

Enhancing Edge-Cloud Collaboration With Blockchain-Assisted Digital Twin Intelligence Offloading Scheme 利用区块链辅助的数字孪生智能卸载方案增强边缘云协作

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-18 DOI: 10.1109/TMC.2025.3562189

Tianyu Li;Xingwei Wang;Rongfei Zeng;Liang Zhao;Ammar Hawbani;Yuxin Zhang;Min Huang

{"title":"Enhancing Edge-Cloud Collaboration With Blockchain-Assisted Digital Twin Intelligence Offloading Scheme","authors":"Tianyu Li;Xingwei Wang;Rongfei Zeng;Liang Zhao;Ammar Hawbani;Yuxin Zhang;Min Huang","doi":"10.1109/TMC.2025.3562189","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562189","url":null,"abstract":"Recently, Edge-Cloud Collaborative (ECC) has emerged as an efficient and promising technique to empower various computation-intensive applications in Digital Twin Network (DTN). The integration of ECC with JointCloud and DTN serves to bridge the gap between data analysis and physical states. In ECC, a reliable and optimal task offloading scheme is required to maximize resource utilization and provide satisfying services to End Users (EU). However, existing offloading schemes still face significant challenges, such as the instability and complexity of network topologies, the intricacies of massive data, and the lack of trust among EU. In this paper, we propose an <italic>enhancinG edge-clOud collaboraTion wiTh blockchain-assistEd digital twin intelligence offloadiNg scheme (GOTTEN) which transmits large-scale tasks generated by DTs to Edge Station (ES) or Cloud Station (CS) in dynamic DTN scenarios. We first formulate this resource allocation and task offloading problem and provide an appropriate initial solution which guarantees that tasks generated by DTs can be accurately mapped to physical entities, while optimizing block allocation and reducing the decision space of task offloading. Then, we employ the Lagrange Multiplier based Distributed Island model-enhanced Genetic Algorithm (LM-DIGA) to transform our formulated problem into a convex form and achieve an optimal resource allocation under a specific scheme. Additionally, our proposed architecture also leverages blockchain verification mechanisms to enhance system stability, strengthening privacy protection for DT data as well. Finally, extensive simulation results demonstrate that, compared with seven baselines, our proposed scheme achieves a 10 percent the total system delay and privacy overhead with regard to other schemes in ECC.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9619-9635"},"PeriodicalIF":9.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036760","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

Participant Recruitment of Vehicular Crowdsensing Along Freeways for Traffic Accident Detection 高速公路交通事故检测中车辆群体感知的参与者招募

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-18 DOI: 10.1109/TMC.2025.3562565

Qian Cao;Zhihui Li;Haitao Li;Shirui Zhou;Yunxiang Zhang

{"title":"Participant Recruitment of Vehicular Crowdsensing Along Freeways for Traffic Accident Detection","authors":"Qian Cao;Zhihui Li;Haitao Li;Shirui Zhou;Yunxiang Zhang","doi":"10.1109/TMC.2025.3562565","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562565","url":null,"abstract":"Vehicular crowdsensing provides a new approach for freeway traffic accident detection. However, the uncertainty on traffic accidents and Mobile Users (MUs) brings great challenges for participant recruitment in constructing the deterministic representation of sensing tasks and estimating the participants. To address the challenges, a participant recruitment method for freeway traffic accident detection is proposed. In the method, to deal with the non-deterministic sensing tasks and MUs, the temporal-spatial distribution of accident risk is estimated by optimal transport theory to represent sensing tasks, and the probability distributions of MUs’ trip distance and requested rewards are used to estimate MUs. Then the participant recruitment problem is converted into an optimal coverage problem for accident risk under the macro statistical characteristics of MUs. The participant recruitment model is established to determine the participants by maximizing the coverage rate of accident risk with the budget constraint. And a greedy heuristic strategy is used to solve the model. Simulation experiments are carried out to validate the proposed method. The results show the proposed method is effective and reliable in freeway traffic accident detection.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9650-9663"},"PeriodicalIF":9.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036205","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

Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding Salix-Leaf：寻找用于实际并行解码的信号簇的主脉

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-18 DOI: 10.1109/TMC.2025.3562590

Yajun Li;Jumin Zhao;Dengao Li;Hejun Wu;Shuang Xu;Ruiqin Bai

{"title":"Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding","authors":"Yajun Li;Jumin Zhao;Dengao Li;Hejun Wu;Shuang Xu;Ruiqin Bai","doi":"10.1109/TMC.2025.3562590","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562590","url":null,"abstract":"Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strength-differences (SSDs) among tags; or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called Salix-Leaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, Salix-Leaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9683-9694"},"PeriodicalIF":9.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051073","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

HeadMon$^{+}$+: Domain Adaptive Head Dynamic-Based Riding Maneuver Prediction HeadMon$^{+}$+：基于域自适应头部动态的骑行机动预测

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-17 DOI: 10.1109/TMC.2025.3562179

Zengyi Han;En Wang;Mohan Yu;Jie Wang;Yuuki Nishiyama;Kaoru Sezaki

{"title":"HeadMon$^{+}$+: Domain Adaptive Head Dynamic-Based Riding Maneuver Prediction","authors":"Zengyi Han;En Wang;Mohan Yu;Jie Wang;Yuuki Nishiyama;Kaoru Sezaki","doi":"10.1109/TMC.2025.3562179","DOIUrl":"https://doi.org/10.1109/TMC.2025.3562179","url":null,"abstract":"Micro-mobility has become a vital means of transportation in recent years, however, it has also resulted in a rise in traffic incidents. Timely tracking and predicting riders’ maneuvers hold the potential to ensure active protection and allow for sufficient time to avert accidents by issuing timely warnings and interventions. We contend that the rider's head dynamics can provide valuable information regarding their subsequent maneuvers. Riders’ traveling habits, however diverse, not to mention the rapidly varying riding environment. The above factors contribute to significant disruptions in the data source, and various micro-mobility forms further exacerbate the issue. We accordingly present HeadMon<inline-formula><tex-math>$^{+}$</tex-math></inline-formula>, which predicts the rider's subsequent maneuver by examining their head dynamics, and it can effectively adapt to various riding conditions and individuals. The system incorporates a deep learning framework with an advanced domain adversarial network. By single-time pre-training, HeadMon<inline-formula><tex-math>$^{+}$</tex-math></inline-formula> is capable of adapting to new data domains, including human subjects, and riding conditions for robust maneuver prediction. Based on our evaluation, we have found that the maneuver prediction of HeadMon<inline-formula><tex-math>$^{+}$</tex-math></inline-formula> has an overall precision of 94% with a prediction time gap of 4 seconds. HeadMon<inline-formula><tex-math>$^{+}$</tex-math></inline-formula>'s low cost and rapid response capability make it easily deployed and then contribute to enhancing safe riding.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"9570-9583"},"PeriodicalIF":9.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036921","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

Differential Private Data Stream Analytics in the Local and Shuffle Models 本地和Shuffle模型中的差异私有数据流分析

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-15 DOI: 10.1109/TMC.2025.3559621

Shaowei Wang;Jin Li;Yun Peng;Kongyang Chen;Wei Yang;Hui Jiang;Jin Li

{"title":"Differential Private Data Stream Analytics in the Local and Shuffle Models","authors":"Shaowei Wang;Jin Li;Yun Peng;Kongyang Chen;Wei Yang;Hui Jiang;Jin Li","doi":"10.1109/TMC.2025.3559621","DOIUrl":"https://doi.org/10.1109/TMC.2025.3559621","url":null,"abstract":"We study online data analytics with differential privacy (DP) in decentralized settings. Specifically, online data analytics with local DP protection is widely adopted in real-world applications. Despite numerous endeavors in this field, significant gaps in utility and functionality remain when compared to its offline counterpart. We present an optimal, streamable mechanism: <monospace>ExSub</monospace>, for local DP sparse vector estimation. The mechanism enables a range of online analytics on streaming binary vectors, including multi-dimensional binary, categorical, or set-valued data. By leveraging the negative correlation of occurrence events in the sparse vector, we attain an optimal error rate under local privacy constraints, only requiring streamable computations. To surpass the error barrier of local privacy, we also study <monospace>ExSub</monospace> randomizer in the newly emerging (single-message) shuffle model of DP, and provide nearly-tight privacy amplification bounds therein. Additionally, we leverage the online shuffle model that independently permutes users’ messages at each timestamp, to design a simplified randomization strategy that can approximately reach Gaussian accuracy in central DP. Through experiments with both synthetic and real-world datasets, <monospace>ExSub</monospace> mechanism in the local model have been shown to reduce error by 40%–60% compared to SOTA approaches. The <monospace>ExSub</monospace> in the shuffle model can further reduce over 85% error, and the online shuffle protocol reduces over 99.7% error.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6701-6717"},"PeriodicalIF":7.7,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219585","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

Joint Adaptation for Mobile 360-Degree Video Streaming and Enhancement 移动360度视频流的联合适配与增强

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-04-01 DOI: 10.1109/TMC.2025.3555322

Haotian Guo;Feng Wang;Wei Zhang;Yifei Zhu;Laizhong Cui;Jiangchuan Liu;Fei Richard Yu;Lei Zhang

{"title":"Joint Adaptation for Mobile 360-Degree Video Streaming and Enhancement","authors":"Haotian Guo;Feng Wang;Wei Zhang;Yifei Zhu;Laizhong Cui;Jiangchuan Liu;Fei Richard Yu;Lei Zhang","doi":"10.1109/TMC.2025.3555322","DOIUrl":"https://doi.org/10.1109/TMC.2025.3555322","url":null,"abstract":"Tile-based streaming and super resolution (SR) are two representative technologies adopted to improve bandwidth efficiency of 360° video streaming. The former allows selective downloading of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to enhance the received video to higher quality using advanced neural network models. In this work, we propose a Collaborated Streaming and Enhancement (CSE) adaptation framework for mobile 360° videos, which integrates super resolution with tile-based streaming to optimize the user experience with dynamic bandwidth and limited computing capability. To effectively enhance the tile-based video streaming through SR, we propose to adaptively group the tiles for quality enhancement adapting to the content similarity. We also identify and address several key design issues to integrate SR into tile-based video streaming including unified video quality assessment, computational complexity model for super resolution, and buffer analysis considering the interplay between transmission and enhancement. We further formulate the quality-of-experience (QoE) maximization problem for mobile 360° video streaming and propose a rate adaptation algorithm to make the best decisions for download and for enhancement based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which demonstrates stable performance with considerable QoE improvement, while enabling a trade-off between playback smoothness and video quality.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7726-7741"},"PeriodicalIF":7.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550158","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

UltraAdv: An Ultrasonic Adversarial Attack on Closed-Box Speech Recognition Systems UltraAdv：一种针对闭盒语音识别系统的超声波对抗性攻击

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-31 DOI: 10.1109/TMC.2025.3555680

Guoming Zhang;Xiaohui Ma;Huiting Zhang;Riccardo Spolaor;Yanni Yang;Xiaoyu Ji;Xiuzhen Cheng;Pengfei Hu

{"title":"UltraAdv: An Ultrasonic Adversarial Attack on Closed-Box Speech Recognition Systems","authors":"Guoming Zhang;Xiaohui Ma;Huiting Zhang;Riccardo Spolaor;Yanni Yang;Xiaoyu Ji;Xiuzhen Cheng;Pengfei Hu","doi":"10.1109/TMC.2025.3555680","DOIUrl":"https://doi.org/10.1109/TMC.2025.3555680","url":null,"abstract":"Attacks on speech recognition systems often use adversarial or inaudible commands. However, a challenge is that adversarial perturbations typically fall within the audible frequency range, making it difficult to achieve inaudibility. Additionally, the non-linear effects of loudspeakers often cause inaudible commands to become audible at higher power levels. Therefore, minimizing the power requirements of the attack is essential to maintain inaudibility. Another significant obstacle is the conversion of variable-length commands, especially longer ones, into shorter target commands. In this paper, we present UltraAdv, a method for generating long-range adversarial perturbations capable of compromising commands of arbitrary length in closed-box setting. By combining the ultrasonic signal with the normal one, rather than negating it as in DolphinAttack, we significantly improve the energy efficiency, thus enhancing its attack distance. We also propose a dynamically adjustable suppression-interference method based on automatic gain control to address the challenge of mismatched durations between long commands and target commands (length-independent). Experiments demonstrate that using a single perturbation, we achieve impressive success rates of 98.84% and 96.62% and 98.32% across a diverse set of 12,260 speeches on DeepSpeech, iFlytek, and Whisper. The attack range reaches up to 15 m, surpassing DolphinAttack's 5 m range at equivalent power.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7648-7662"},"PeriodicalIF":7.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550522","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

QoS-Oriented Joint Resource and Trajectory Optimization in NOMA-Enhanced AAV-MEC Systems 面向qos的noma增强型AAV-MEC系统联合资源与轨迹优化

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-30 DOI: 10.1109/TMC.2025.3575451

Huan Zhou;Yadong Lu;Geyong Min;Zhiwen Yu;Liang Wang;Yao Zhang;Bin Guo

{"title":"QoS-Oriented Joint Resource and Trajectory Optimization in NOMA-Enhanced AAV-MEC Systems","authors":"Huan Zhou;Yadong Lu;Geyong Min;Zhiwen Yu;Liang Wang;Yao Zhang;Bin Guo","doi":"10.1109/TMC.2025.3575451","DOIUrl":"https://doi.org/10.1109/TMC.2025.3575451","url":null,"abstract":"Autonomous Aerial Vehicle (AAV)-assisted Mobile Edge Computing (MEC) has received extensive attention because it provides resilient computation services for multiple Mobile Users (MUs). However, due to the increasing scale of offloaded tasks, the uncertain mobility of MUs, and the limited energy budget of AAV and MUs, it is extremely challenging to achieve satisfactory Quality-of-Service (QoS). Non-Orthogonal Multiple Access (NOMA), a promising technology to serve multiple MUs with limited communication resources, has great potential to be integrated with MEC. To this end, this paper proposes a QoS-oriented NOMA-enhanced AAV-MEC system, which aims to capture the potential gains of uplink NOMA and enable more MUs to benefit from edge computing servers in resource-constrained AAV-assisted MEC environments. This synergy reduces MUs’ uplink energy consumption but poses new challenges in resource allocation and AAV trajectory design. To address these challenges, we define a new metric called System Overhead Ratio (SOR) to reflect the system’s QoS, and then consider a joint optimization problem of resource allocation, transmission power control, and AAV trajectory design, with the goal of minimizing the SOR. Given the NP-hard nature of the optimization problem, we propose a Lyapunov and convex optimization-based Low-complexity Online Resource allocation and Trajectory optimization method (LORT) to solve it, and further analyze the convergence and complexity of LORT. Finally, extensive simulations show that the proposed method surpasses other benchmarks, reducing the SOR by approximately <inline-formula><tex-math>$10%$</tex-math></inline-formula>-<inline-formula><tex-math>$ 25%$</tex-math></inline-formula> under various scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10118-10134"},"PeriodicalIF":9.2,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021306","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