IEEE Transactions on Mobile Computing最新文献

2024 Reviewers List

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-05 DOI: 10.1109/TMC.2025.3527174

引用次数: 0

Large-Scale Mechanism Design for Networks: Superimposability and Dynamic Implementation

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-02 DOI: 10.1109/TMC.2024.3499958

Meng Zhang;Deepanshu Vasal

{"title":"Large-Scale Mechanism Design for Networks: Superimposability and Dynamic Implementation","authors":"Meng Zhang;Deepanshu Vasal","doi":"10.1109/TMC.2024.3499958","DOIUrl":"https://doi.org/10.1109/TMC.2024.3499958","url":null,"abstract":"Network utility maximization (NUM) is a fundamental framework for optimizing next-generation networks. However, self-interested agents with private information pose challenges due to potential system manipulation. To address these challenges, the literature on economic mechanism design has emerged. Existing mechanisms are not suited for large-scale networks due to their complexity, high implementation costs, and difficulty to adapt to dynamic settings. This paper proposes a large-scale mechanism design framework that mitigates these limitations. As the number of agents <inline-formula><tex-math>$I$</tex-math></inline-formula> approaches infinity, their incentive to misreport decreases rapidly at a rate of <inline-formula><tex-math>$mathcal {O}(1/I^{2})$</tex-math></inline-formula>. We introduce a superimposable framework applicable to any NUM algorithm without modifications, reducing implementation costs. In the dynamic setting, the large-scale mechanism design framework introduces the decomposability of the problem, enabling agents to align their own interests with the objectives of the dynamic NUM problem. This alignment helps overcome the additional, more stringent incentive constraints encountered in dynamic settings. Extending our results to dynamic settings, we present the design of a Dynamic Large-Scale mechanism with desirable properties and the corresponding Dynamic Superimposable Large-Scale mechanism. Our numerical experiments validate the fact that our proposed schemes are approximately <inline-formula><tex-math>$I$</tex-math></inline-formula> times faster than the seminal VCG mechanism.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1278-1292"},"PeriodicalIF":7.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184148","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

Intelligence-Based Reinforcement Learning for Dynamic Resource Optimization in Edge Computing-Enabled Vehicular Networks

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-25 DOI: 10.1109/TMC.2024.3506161

Yuhang Wang;Ying He;F. Richard Yu;Kaishun Wu;Shanzhi Chen

{"title":"Intelligence-Based Reinforcement Learning for Dynamic Resource Optimization in Edge Computing-Enabled Vehicular Networks","authors":"Yuhang Wang;Ying He;F. Richard Yu;Kaishun Wu;Shanzhi Chen","doi":"10.1109/TMC.2024.3506161","DOIUrl":"https://doi.org/10.1109/TMC.2024.3506161","url":null,"abstract":"Intelligent transportation systems demand efficient resource allocation and task offloading to ensure low-latency, high-bandwidth vehicular services. The dynamic nature of vehicular environments, characterized by high mobility and extensive interactions among vehicles, necessitates considering time-varying statistical regularities, especially in scenarios with sharp variations. Despite the widespread use of traditional reinforcement learning for resource allocation, its limitations in generalization and interpretability are evident. To overcome these challenges, we propose an Intelligence-based Reinforcement Learning (IRL) algorithm. This algorithm utilizes active inference to infer the real world and maintain an internal model by minimizing free energy. Enhancing the efficiency of active inference, we incorporate prior knowledge as macro guidance, ensuring more accurate and efficient training. By constructing an intelligence-based model, we eliminate the need for designing reward functions, aligning better with human thinking, and providing a method to reflect the learning, information transmission and intelligence accumulation processes. This approach also allows for quantifying intelligence to a certain extent. Considering the dynamic and uncertain nature of vehicular scenarios, we apply the IRL algorithm to environments with constantly changing parameters. Extensive simulations confirm the effectiveness of IRL, significantly improving the generalization and interpretability of intelligent models in vehicular networks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2394-2406"},"PeriodicalIF":7.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361010","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

Biasing Federated Learning With a New Adversarial Graph Attention Network

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-15 DOI: 10.1109/TMC.2024.3499371

Kai Li;Jingjing Zheng;Wei Ni;Hailong Huang;Pietro Liò;Falko Dressler;Ozgur B. Akan

{"title":"Biasing Federated Learning With a New Adversarial Graph Attention Network","authors":"Kai Li;Jingjing Zheng;Wei Ni;Hailong Huang;Pietro Liò;Falko Dressler;Ozgur B. Akan","doi":"10.1109/TMC.2024.3499371","DOIUrl":"https://doi.org/10.1109/TMC.2024.3499371","url":null,"abstract":"Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an aim to bias the learning process across the FL. The proposed AGAT is developed to synthesize malicious, biasing model updates, where the minimum of Kullback-Leibler (KL) divergence between the user's model update and the global model is maximized. Due to a limited set of labeled input-output biasing data samples, a surrogate model is created, which presents the behavior of a complex malicious model update. Moreover, a graph autoencoder (GAE) is designed within the AGAT architecture, which is trained together with sub-gradient descent to reconstruct manipulatively the correlations of the model updates, and maximize the reconstruction loss while keeping the malicious, biasing model updates undetectable. The proposed AGAT attack is implemented in PyTorch, showing experimentally that AGAT successfully increases the minimum value of KL divergence of benign model updates by 60.9% and bypasses the detection of existing defense models. The source code of the AGAT attack is released on GitHub.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2407-2421"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361324","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

Harvesting Physical-Layer Randomness in Millimeter Wave Bands

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-15 DOI: 10.1109/TMC.2024.3499876

Ziqi Xu;Jingcheng Li;Yanjun Pan;Ming Li;Loukas Lazos

{"title":"Harvesting Physical-Layer Randomness in Millimeter Wave Bands","authors":"Ziqi Xu;Jingcheng Li;Yanjun Pan;Ming Li;Loukas Lazos","doi":"10.1109/TMC.2024.3499876","DOIUrl":"https://doi.org/10.1109/TMC.2024.3499876","url":null,"abstract":"The unpredictability of the wireless channel has been used as a natural source of randomness to build physical-layer security primitives for shared key generation, authentication, access control, proximity verification, and other security properties. Compared to pseudo-random generators, it has the potential to achieve information-theoretic security. In sub-6 GHz frequencies, the randomness is harvested from the small-scale fading effects of RF signal propagation in rich scattering environments. However, the RF propagation characteristics follow sparse models with clustered paths when devices operate in millimeter-wave (mmWave) bands (5G and Next-Generation networks, Wi-Fi in 60GHz). Millimeter-wave transmissions are typically directional to increase the gain and combat high signal attenuation, leading to stable and more predictable channels. In this paper, we first demonstrate that state-of-the-art methods relying on channel state information or received signal strength measurements fail to produce high randomness. Accounting for the unique features of mmWave propagation, we propose a novel randomness extraction mechanism that exploits the random timing of channel blockage to harvest random bits. Compared with the prior art in CSI-based and context-based randomness extraction, our protocol remains secure against <italic>passive and active Man-in-the-Middle adversaries co-located with the legitimate devices</i>. We demonstrate the security properties of our method in a 28 GHz mmWave testbed in an indoor setting.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2285-2300"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360989","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

Collaborative Task Offloading and Resource Allocation in Small-Cell MEC: A Multi-Agent PPO-Based Scheme

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3496536

Han Li;Ke Xiong;Yuping Lu;Wei Chen;Pingyi Fan;Khaled Ben Letaief

{"title":"Collaborative Task Offloading and Resource Allocation in Small-Cell MEC: A Multi-Agent PPO-Based Scheme","authors":"Han Li;Ke Xiong;Yuping Lu;Wei Chen;Pingyi Fan;Khaled Ben Letaief","doi":"10.1109/TMC.2024.3496536","DOIUrl":"https://doi.org/10.1109/TMC.2024.3496536","url":null,"abstract":"Small-cell mobile edge computing (SE-MEC) networks amalgamate the virtues of MEC and small-cell networks, enhancing data processing capabilities of user devices (UDs). Nevertheless, time-varying wireless channels, dynamic UD requirements, and severe interference among UDs make it difficult to fully exploit the limited network resources and stably provide computing services for UDs. Therefore, efficient task offloading and resource allocation (TORA) is essential. Moreover, since multiple small cells are deployed, decentralized TORA schemes are preferred in practice. Thus, this paper aims to design distributed adaptive TORA schemes for SE-MEC networks. In pursuit of an eco-friendly design, an optimization problem is formulated to minimize the total energy consumption (TEC) of UDs subject to delay constraints. To effectively deal with network's dynamic characteristics, the reinforce learning framework is applied, where the TEC minimization problem is first modeled as a partially observable Markov decision process (POMDP), and then an efficient multi-agent proximal policy optimization (MAPPO)-based scheme is presented to solve it. In the presented scheme, each small-cell base station (SBS) serves as an agent and is capable of making TORA decisions only with its own local information. To promote collaboration among multiple agents, a global reward function is designed. A state normalization mechanism is also introduced into the presented scheme for enhancing learning performance. Simulation results show that although the proposed MAPPO-based scheme works in a distributed manner, it achieves very similar performance to the centralized one. In addition, it is demonstrated that the state normalization mechanism has a significant effect on reducing TEC.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2346-2359"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361009","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

MagSpy: Revealing User Privacy Leakage via Magnetometer on Mobile Devices

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3495506

Yongjian Fu;Lanqing Yang;Hao Pan;Yi-Chao Chen;Guangtao Xue;Ju Ren

{"title":"MagSpy: Revealing User Privacy Leakage via Magnetometer on Mobile Devices","authors":"Yongjian Fu;Lanqing Yang;Hao Pan;Yi-Chao Chen;Guangtao Xue;Ju Ren","doi":"10.1109/TMC.2024.3495506","DOIUrl":"https://doi.org/10.1109/TMC.2024.3495506","url":null,"abstract":"Various characteristics of mobile applications (apps) and associated in-app services can reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to restrict access to data related to mobile app usage. This paper outlines a novel approach to extracting detailed app usage information by analyzing electromagnetic (EM) signals emitted from mobile devices during app-related tasks. The proposed system, MagSpy, recovers user privacy information from magnetometer readings that do not require access permissions. This EM leakage becomes complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. To address these challenges, MagSpy employs multiple techniques to extract and identify signals related to app usage. Specifically, the geomagnetic offset signal is canceled using accelerometer and gyroscope sensor data, and a Cascade-LSTM algorithm is used to classify apps and in-app services. MagSpy also uses CWT-based peak detection and a Random Forest classifier to detect PIN inputs. A prototype system was evaluated on over 50 popular mobile apps with 30 devices. Extensive evaluation results demonstrate the efficacy of MagSpy in identifying in-app services (96% accuracy), apps (93.5% accuracy), and extracting PIN input information (96% top-3 accuracy).","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2455-2469"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361322","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

REM: Enabling Real-Time Neural-Enhanced Video Streaming on Mobile Devices Using Macroblock-Aware Lookup Table

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3496443

Baili Chai;Di Wu;Jinyu Chen;Mengyu Yang;Zelong Wang;Miao Hu

{"title":"REM: Enabling Real-Time Neural-Enhanced Video Streaming on Mobile Devices Using Macroblock-Aware Lookup Table","authors":"Baili Chai;Di Wu;Jinyu Chen;Mengyu Yang;Zelong Wang;Miao Hu","doi":"10.1109/TMC.2024.3496443","DOIUrl":"https://doi.org/10.1109/TMC.2024.3496443","url":null,"abstract":"The demand for mobile video streaming has seen a substantial surge in recent years. However, current platforms heavily depend on network capacity to ensure the delivery of high-quality video streams. The emergence of neural-enhanced video streaming presents a promising solution to address this challenge by leveraging client-side computation, thereby reducing bandwidth consumption. Nonetheless, deploying advanced super-resolution (SR) models on mobile devices is hindered by the computational demands of existing SR models. In this paper, we propose REM, a novel neural-enhanced mobile video streaming framework. REM utilizes a customized lookup table to facilitate real-time neural-enhanced video streaming on mobile devices. Initially, we conduct a series of measurements to identify abundant macroblock redundancies across frames in a video stream. Subsequently, we introduce a dynamic macroblock selection algorithm that prioritizes important macroblocks for neural enhancement. The SR-enhanced results are stored in the lookup table and efficiently reused to meet real-time requirements and minimize resource overhead. By considering macroblock-level characteristics of the video frames, the lookup table enables efficient and fast processing. Additionally, we design a lightweight macroblock-aware SR module to expedite inference. Finally, we perform extensive experiments on various mobile devices. The results demonstrate that REM enhances overall processing throughput by up to 10.2 times and reduces power consumption by up to 58.6% compared to state-of-the-art methods. Consequently, this leads to a 38.06% improvement in the quality of experience for mobile users.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2085-2097"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360915","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

Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-Driven Approach

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-11 DOI: 10.1109/TMC.2024.3495719

Jingbo Liu;Jiacheng Chen;Zongxi Liu;Haibo Zhou

{"title":"Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-Driven Approach","authors":"Jingbo Liu;Jiacheng Chen;Zongxi Liu;Haibo Zhou","doi":"10.1109/TMC.2024.3495719","DOIUrl":"https://doi.org/10.1109/TMC.2024.3495719","url":null,"abstract":"To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes nearest neighbor interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel; 2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations; and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2437-2454"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361321","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

Mixed Numerology-Based Intelligent Resource Management in a Sliced 6G Space–Terrestrial Integrated Radio Access Network 分片 6G 天地一体化无线接入网中基于混合数字技术的智能资源管理

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-11-08 DOI: 10.1109/TMC.2024.3494842

Ning Hui;Qian Sun;Jie Zeng;Lin Tian;Yuanyuan Wang;Yiqing Zhou

{"title":"Mixed Numerology-Based Intelligent Resource Management in a Sliced 6G Space–Terrestrial Integrated Radio Access Network","authors":"Ning Hui;Qian Sun;Jie Zeng;Lin Tian;Yuanyuan Wang;Yiqing Zhou","doi":"10.1109/TMC.2024.3494842","DOIUrl":"https://doi.org/10.1109/TMC.2024.3494842","url":null,"abstract":"Although resource sharing and mixed numerology among slices are promising for improving wireless resource utilization, these techniques can compromise isolation performance and cause serious inter numerology interference (INI). Therefore, this paper studies wireless resource management in a mixed numerology-based sliced 6G space–terrestrial integrated radio access network (STI-RAN) with the aim of reducing INI and guaranteeing isolation performance while decreasing interference from Doppler frequency shifts caused by the high-speed movement of low-orbit satellites. First, an isolation performance indicator is defined to evaluate different isolation performances, and a universal spectral distance model is formulated to rewrite the INI power model. Next, the dynamic wireless resource management problem is formulated in a discrete form, yielding a scheme called Flex-<inline-formula><tex-math>$mu$</tex-math></inline-formula>, which is designed to reduce the INI and Doppler frequency shifts, guarantee isolation performance, and enhance the SINR. Finally, an intelligent multi-characteristic matrix coding-based social group optimization (MultiMatrix-SGO) algorithm is designed to solve the proposed NP-hard discrete optimization problem. Compared with existing schemes, the system utility is efficiently increased by up to 58.32%, the SINR can converge to 38.44 dB, and the isolation performance is guaranteed while the INI and Doppler frequency shifts are reduced.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1338-1356"},"PeriodicalIF":7.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184149","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