IEEE Transactions on Mobile Computing最新文献_第5页

Minimizing Age of Processed Information Over Unreliable Wireless Network Channels 在不可靠的无线网络信道上最小化处理信息的时间

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-20 DOI: 10.1109/TMC.2024.3520913

Wasin Meesena;Chanikarn Nikunram;Stephen John Turner;Sucha Supittayapornpong

{"title":"Minimizing Age of Processed Information Over Unreliable Wireless Network Channels","authors":"Wasin Meesena;Chanikarn Nikunram;Stephen John Turner;Sucha Supittayapornpong","doi":"10.1109/TMC.2024.3520913","DOIUrl":"https://doi.org/10.1109/TMC.2024.3520913","url":null,"abstract":"The freshness of real-time status processing of time-sensitive information is crucial for many applications, including flight control, image processing, and autonomous vehicles. In this paper, unprocessed information is sent from sensors to a base station over a shared, unreliable wireless network. The base station has a set of dedicated non-preemptive processors with constant processing times to process information from each sensor. The age of processed information is the time elapsed since the generation of the packet that the processor most recently processed. Our objective is to minimize the expected weighted sum of this age over an infinite time horizon. Here, the challenge is the coupling between a scheduling problem under unreliable communications and the processing times. We first break the coupling by tracking the age of information during processing and derive a lower performance bound of the objective. We then design a stationary randomized policy and a Max-Weight policy for two queueing disciplines: no queues and single-packet queues to achieve our objective. We prove that these policies achieve performance within a factor of two from the optimal. In addition, we prove queues are useful to the stationary randomized policies in highly unreliable or large network settings. Our analytical results are further validated by numerical experiments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3567-3578"},"PeriodicalIF":7.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777919","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

Membership Inference Attacks Against Incremental Learning in IoT Devices 针对物联网设备增量学习的成员推理攻击

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-20 DOI: 10.1109/TMC.2024.3521216

Xianglong Zhang;Huanle Zhang;Guoming Zhang;Yanni Yang;Feng Li;Lisheng Fan;Zhijian Huang;Xiuzhen Cheng;Pengfei Hu

{"title":"Membership Inference Attacks Against Incremental Learning in IoT Devices","authors":"Xianglong Zhang;Huanle Zhang;Guoming Zhang;Yanni Yang;Feng Li;Lisheng Fan;Zhijian Huang;Xiuzhen Cheng;Pengfei Hu","doi":"10.1109/TMC.2024.3521216","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521216","url":null,"abstract":"Internet of Things (IoT) devices are frequently deployed in highly dynamic environments and need to continuously learn new classes from data streams. Incremental Learning (IL) has gained popularity in IoT as it enables devices to learn new classes efficiently without retraining model entirely. IL involves fine-tuning the model using two sources of data: a small amount of representative samples from the original training dataset and samples from the new classes. However, both data sources are vulnerable to Membership Inference Attack (MIA). Fortunately, the existing MIAs result in poor performance against IL, because they ignore features such as the similarity between old and new models at the old classification layer. This paper presents the first MIA against IL, capable of determining not only whether a sample was used for training/fine-tuning but also distinguishing whether it belongs to the representative dataset or the new classes (unique in IL). Extensive experiments validate the effectiveness of our attack across four real-world datasets. Our attack achieves an average attack success rate of 74.03% in the white-box setting (model structure and parameters are known) and 70.08% in the black-box setting. Importantly, our attack is not sensitive to the IL hyper-parameters (e.g., distillation temperature), confirming its accurate, robust, and practical.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4006-4021"},"PeriodicalIF":7.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783273","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

Underwater Multiple AUV Cooperative Target Tracking Based on Minimal Reward Participation-Embedded MARL 基于最小奖励参与-嵌入式MARL的水下多AUV协同目标跟踪

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-20 DOI: 10.1109/TMC.2024.3521028

Shengchao Zhu;Guangjie Han;Chuan Lin;Fan Zhang

{"title":"Underwater Multiple AUV Cooperative Target Tracking Based on Minimal Reward Participation-Embedded MARL","authors":"Shengchao Zhu;Guangjie Han;Chuan Lin;Fan Zhang","doi":"10.1109/TMC.2024.3521028","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521028","url":null,"abstract":"Recently, the rapid advancement of Multi-Agent Reinforcement Learning (MARL) has introduced a new paradigm for intelligent underwater target tracking within Autonomous Underwater Vehicle (AUV) cluster networks, enabling these networks to intelligently collaborate in target tracking. However, the limited scalability of MARL poses significant challenges to the performance of AUV cluster networks in tracking tasks. Specifically, MARL models trained on a fixed agents lose their effectiveness when the agent count changes, underscoring the critical need to enhance MARL’s scalability to accommodate an arbitrary number of agents. This paper addresses the pressing issue of MARL’s scalability in the context of AUV cluster network-based target tracking. Specifically, we propose an Elastic Software-Defined Multi-Agent Reinforcement Learning (ESD-MARL) architecture to enhance the scalability of AUV cluster networks. Moreover, we propose an Incremental Multi-Agent Reinforcement Learning algorithm based on Minimal Reward Participation (IMARL-MRP) that allows for the expansion of the agents without retraining. By integrating the ESD-MARL with the IMARL-MRP, we propose an elastic underwater target tracking scheme, achieving high-performance target tracking with enhanced scalability. Evaluation results demonstrate that the proposed approach effectively enhances the scalability of MARL, enabling the arbitrary expansion of the AUV cluster network, thus supporting scalable and efficient underwater target tracking.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4169-4182"},"PeriodicalIF":7.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783280","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

Reinforcement Learning-Based Dual-Identity Double Auction in Personalized Federated Learning 个性化联邦学习中基于强化学习的双身份双拍卖

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-20 DOI: 10.1109/TMC.2024.3521304

Juan Li;Zishang Chen;Tianzi Zang;Tong Liu;Jie Wu;Yanmin Zhu

{"title":"Reinforcement Learning-Based Dual-Identity Double Auction in Personalized Federated Learning","authors":"Juan Li;Zishang Chen;Tianzi Zang;Tong Liu;Jie Wu;Yanmin Zhu","doi":"10.1109/TMC.2024.3521304","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521304","url":null,"abstract":"Federated learning participants have two identities: model trainers and model users. As model users, participants care most about the performance of the final model on their own distributions, which is called Personal Model Performance (PMP). This makes training a single global model to accommodate all participants impractical because the data distributions of participants are heterogeneous. As model trainers, due to high training costs, participants are reluctant to contribute models if incentives are not enough. With the combination of the above two reasons, we propose a dual-identity double auction as an incentive mechanism in personalized federated learning, allowing directional selection between model users and model trainers, both of which are served by FL participants. Within the double auction framework, we devise a reinforcement learning-based model selection method. This method selects a set of models for each buyer to bid on. The bought models are aggregated to be a personalized model to achieve higher PMP. Additionally, we implement a transaction partition-based approach for determining clearing prices and winning pairs. We address the challenge of the unavailability of private yet essential data distribution information, the coupled influence of model selection and auction results on PMP, and more utility improvement ways of multi-demand dual-identity participants. Finally, our double auction optimizes the PMP of all participants and ensures the truthfulness of multi-demand dual-identity participants, which is harder compared with single-demand single-identity participants.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"4086-4103"},"PeriodicalIF":7.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783326","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

WiCamera: Vortex Electromagnetic Wave-Based WiFi Imaging WiCamera：涡流电磁波WiFi成像

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-18 DOI: 10.1109/TMC.2024.3519623

Leiyang Xu;Xiaolong Zheng;Xinrun Du;Liang Liu;Huadong Ma

{"title":"WiCamera: Vortex Electromagnetic Wave-Based WiFi Imaging","authors":"Leiyang Xu;Xiaolong Zheng;Xinrun Du;Liang Liu;Huadong Ma","doi":"10.1109/TMC.2024.3519623","DOIUrl":"https://doi.org/10.1109/TMC.2024.3519623","url":null,"abstract":"Current WiFi imaging approaches focus on monitoring dynamic targets to facilitate easy object distinction and capture rich signal reflections for image construction. In static object imaging, massive antenna array or emulated antenna array is often necessary. We propose WiCamera, a novel WiFi imaging prototype that utilizes vortex electromagnetic waves (VEMWs) to monitor stationary human postures using commodity WiFi, by generating human silhouettes with only <inline-formula><tex-math>$3 times 3$</tex-math></inline-formula> MIMO. VEMWs possess a helical wavefront with different phase variations, enabling the imaging of stationary objects through different OAM (Orbital Angular Momentum) modes with time-division multiplexing. WiCamera emits three OAM modes waves from WiFi devices and utilizes their phase variations for imaging. By ray tracing the received signals to a target image plane, WiCamera generates a wavefront image. A generative adversarial network (GAN)-based model is further utilized to refine the wavefront image and create a high-resolution human silhouette. The system's output images are evaluated using metrics such as structural similarity index measure (SSIM) and Szymkiewicz-Simpson coefficient (SSC), comparing them to ground truth images captured by cameras. The evaluation shows that WiCamera performs consistently well in various environments and with different users, with an SSIM reaching up to 0.89 and an SSC reaching up to 0.93.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3633-3649"},"PeriodicalIF":7.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777867","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

Semi-Distributed Network Fault Diagnosis Based on Digital Twin Network in Highly Dynamic Heterogeneous Networks 高动态异构网络中基于数字孪生网络的半分布式网络故障诊断

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-18 DOI: 10.1109/TMC.2024.3519576

Fengxiao Tang;Linfeng Luo;Zhiqi Guo;Yangfan Li;Ming Zhao;Nei Kato

{"title":"Semi-Distributed Network Fault Diagnosis Based on Digital Twin Network in Highly Dynamic Heterogeneous Networks","authors":"Fengxiao Tang;Linfeng Luo;Zhiqi Guo;Yangfan Li;Ming Zhao;Nei Kato","doi":"10.1109/TMC.2024.3519576","DOIUrl":"https://doi.org/10.1109/TMC.2024.3519576","url":null,"abstract":"Highly dynamic heterogeneous networks (HDHNs), characterized by high node mobility and heterogeneity, frequently experience complex and recurrent network faults. Conventional centralized fault diagnosis methods demand real-time collection of extensive network-wide data, while distributed approaches often exhibit limited fault detection capabilities. Additionally, machine learning-based fault diagnosis methods are challenged by the scarcity of labeled fault samples required for training. To address these limitations, this study proposes a semi-distributed network fault diagnosis architecture based on a digital twin network (DTN). The proposed architecture facilitates the extraction of a comprehensive labeled fault dataset that closely replicates real-world network conditions. Using this dataset, we perform centralized training of an enhanced anomaly detection model, FTS-LSTM, to infer fault types at the node level. To overcome the drawbacks of both centralized and distributed approaches, we further introduce a semi-distributed fault diagnosis algorithm (SDFD) that integrates fault types and severity levels identified by nodes to infer overall network faults. The proposed fault diagnosis scheme is validated on a semi-physical DTN simulation platform, demonstrating its effectiveness in realistic scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3979-3992"},"PeriodicalIF":7.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783324","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

Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach 近场点光束聚焦：一种关联感知迁移学习方法

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-18 DOI: 10.1109/TMC.2024.3519382

Mohammad Amir Fallah;Mehdi Monemi;Mehdi Rasti;Matti Latva-aho

{"title":"Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach","authors":"Mohammad Amir Fallah;Mehdi Monemi;Mehdi Rasti;Matti Latva-aho","doi":"10.1109/TMC.2024.3519382","DOIUrl":"https://doi.org/10.1109/TMC.2024.3519382","url":null,"abstract":"Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely large-scale programmable metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the desired focal point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSI-independent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the phase distribution image (PDI) of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing quasi-liquid layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3935-3949"},"PeriodicalIF":7.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783186","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

Learning-Driven Swarm Intelligence: Enabling Deterministic Flows Scheduling in LEO Satellite Networks 学习驱动的群智能：实现LEO卫星网络的确定性流调度

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-16 DOI: 10.1109/TMC.2024.3517618

Zunliang Wang;Haipeng Yao;Tianle Mai;Zhipei Li;C. L. Philip Chen

{"title":"Learning-Driven Swarm Intelligence: Enabling Deterministic Flows Scheduling in LEO Satellite Networks","authors":"Zunliang Wang;Haipeng Yao;Tianle Mai;Zhipei Li;C. L. Philip Chen","doi":"10.1109/TMC.2024.3517618","DOIUrl":"https://doi.org/10.1109/TMC.2024.3517618","url":null,"abstract":"Over the past decade, low-Earth-orbit (LEO) satellite networks have emerged as a critical infrastructure in communication systems, providing wide coverage, high reliability, and global connectivity. Recently, the development of 6G technologies has challenged the LEO satellite networks to guarantee deterministic scheduling for time-sensitive services. However, traditional deterministic networking techniques fall short for LEO satellite networks. First, these techniques impose strict time constraints, but in LEO satellite networks, delay and jitter typically range in the tens of milliseconds, which exceed these limits and render them infeasible. Second, the dynamic topologies of LEO satellite networks challenge the inflexible scheduling strategies generated by these techniques, leading to sub-optimal performance and potential strategy failures. To tackle the first problem, we propose a Cycle Specified Queuing and Forwarding (CSQF) based deterministic flows scheduling mechanism. It relaxes strict time constraints by employing cyclic multi-queue scheduling, enabling more flexible and reliable long-distance transmission. For the second problem, we propose a learning-based swarm intelligence method for deterministic flows scheduling in dynamic LEO satellite networks. It includes an algorithm that combines a Dynamic Graph Convolutional Network (DGCN) with an Adaptive Ant Colony Optimization (ACO) algorithm, referred to as the DGCN-ACO algorithm. The DGCN captures the dynamic feature of the network and generates the heuristic information. The Adaptive ACO utilizes the heuristic information and considers each flow's attribute to generate multi-path scheduling strategies for each deterministic flow, as well as updates the DGCN. The experiment results demonstrate the effectiveness of our proposed algorithm.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3962-3978"},"PeriodicalIF":7.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783255","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

Determining Task Assignments for Candidate Workers Based on Trajectory Prediction 基于轨迹预测的候选工人任务分配确定

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-16 DOI: 10.1109/TMC.2024.3518534

Yahong Li;Yingjie Wang;Gang Li;Xiangrong Tong;Zhipeng Cai

{"title":"Determining Task Assignments for Candidate Workers Based on Trajectory Prediction","authors":"Yahong Li;Yingjie Wang;Gang Li;Xiangrong Tong;Zhipeng Cai","doi":"10.1109/TMC.2024.3518534","DOIUrl":"https://doi.org/10.1109/TMC.2024.3518534","url":null,"abstract":"With the rise of sensor-equipped mobile devices, Mobile Crowd Sensing (MCS) has emerged as an efficient method for information gathering. In smart city environmental sensing, workers can acquire data by merely being within the sensing area. Currently, most studies select opportunistic workers based on the workers’ prior preferences and ignore the effect of movement trajectories on potential opportunistic workers. This may result in the selected opportunistic workers being less-than-ideal, or even ignoring the failure of some tasks to be accomplished, thus resulting in a waste of resources. Therefore, this paper proposes a Recruitment Framework for judging Opportunistic Workers based on Movement Trajectories (RFOW-MT), a two-phase framework for worker recruitment. In the offline phase, combining the neural network model Long Short-Term Memory (LSTM) and Geohash algorithm, an algorithm to detect the set of candidate opportunistic workers is proposed, solving the problems of location privacy and search efficiency. In the online phase, in order to maximize the task spatial coverage under the task budget constraint, a task allocation algorithm based on geographic location packed grouping is proposed. Finally, RFOW-MT outperforms other methods in terms of task spatial coverage and runtime as verified by experiments on real datasets.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3890-3902"},"PeriodicalIF":7.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783277","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

Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks 支持 MEC 的星空地一体化网络中的量子辅助在线任务卸载和资源分配

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-16 DOI: 10.1109/TMC.2024.3519060

Yu Zhang;Yanmin Gong;Lei Fan;Yu Wang;Zhu Han;Yuanxiong Guo

{"title":"Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks","authors":"Yu Zhang;Yanmin Gong;Lei Fan;Yu Wang;Zhu Han;Yuanxiong Guo","doi":"10.1109/TMC.2024.3519060","DOIUrl":"https://doi.org/10.1109/TMC.2024.3519060","url":null,"abstract":"In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders’ decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3878-3889"},"PeriodicalIF":7.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777866","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