IEEE Transactions on Mobile Computing最新文献_第10页

Toward Integrated Sensing and Communication: Interference-Resistance Design for WiFi Sensing 面向传感与通信的集成：WiFi传感的抗干扰设计

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-16 DOI: 10.1109/TMC.2025.3570752

Junmei Yao;Chaoyang Liu;Sheng Luo;Lu Wang;Tingting Zhang;Kaishun Wu

{"title":"Toward Integrated Sensing and Communication: Interference-Resistance Design for WiFi Sensing","authors":"Junmei Yao;Chaoyang Liu;Sheng Luo;Lu Wang;Tingting Zhang;Kaishun Wu","doi":"10.1109/TMC.2025.3570752","DOIUrl":"https://doi.org/10.1109/TMC.2025.3570752","url":null,"abstract":"WiFi has been widely used for local area networking of devices and Internet access in the past two decades. Many researchers exploit WiFi signals for target sensing through analyzing the Channel State Information (CSI) of signals affected by the target movement. With the development of 6G Integrated Sensing and Communication (ISAC), some researchers further consider using communication data packets for WiFi sensing. However, all the current works do not analyze the impact of ubiquitous interference on WiFi sensing performance. In this paper, we propose IRSensing, an interference-resistance design to improve the CSI quality under interference in the ISAC scenario, aiming to improve the WiFi sensing performance. IRSensing exploits the overall WiFi packet for CSI optimization. It first measures the interference level of each subcarrier based on variance analysis, then proposes a CSI optimization method based on maximal ratio combining to improve the CSI quality. It finally proposes a practical CSI enhancement process to adapt to complex interference situations in actual networks. We implement IRSensing on a hardware testbed and evaluate its performance under different settings. Experiment results show that it can significantly decrease the activity detection error rate by up to 80% and improve the classification accuracy by up to 15<inline-formula><tex-math>$%$</tex-math></inline-formula>.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10807-10822"},"PeriodicalIF":9.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036738","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

Multi-Variate Time Series Prediction of Traffic and Users for Dynamic RRH-BBU Mapping in C-RAN C-RAN中RRH-BBU动态映射的流量和用户多变量时间序列预测

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-16 DOI: 10.1109/TMC.2025.3570851

Fan Wu;Shanshan Wang;Jieyu Zhou;Haoye Pan;Conghao Zhou;Wang Yang;Feng Lyu;Yaoxue Zhang

{"title":"Multi-Variate Time Series Prediction of Traffic and Users for Dynamic RRH-BBU Mapping in C-RAN","authors":"Fan Wu;Shanshan Wang;Jieyu Zhou;Haoye Pan;Conghao Zhou;Wang Yang;Feng Lyu;Yaoxue Zhang","doi":"10.1109/TMC.2025.3570851","DOIUrl":"https://doi.org/10.1109/TMC.2025.3570851","url":null,"abstract":"Cellular operators face significant challenges in cutting operating expenses while maintaining the quality of service (QoS) for users due to growing network traffic and dynamic user connections. These challenges are addressed by the cloud radio access network (C-RAN) architecture, which includes a centralized pool of baseband units (BBUs) and distributes them from remote radio heads (RRHs). The key to improving C-RAN performance is to dynamically allocate large-scale RRHs to different BBUs in real time. In this paper, we propose a user behavior-aware RRH-BBU mapping framework to improve the performance of large-scale C-RANs by predicting RRH traffic and users in advance. First, we propose a Multivariate RRH time series Prediction Model (MRPM) that captures the spatio-temporal patterns in the data to predict the traffic volume and the number of users of RRHs, which represent key indicators of RRH connection states. Second, we formulate the RRH-BBU mapping as a Markov decision process problem to optimize cost and QoS by considering BBU utilization, BBU energy consumption, RRH migration frequency, and BBU load balancing. Third, we propose a prediction-based RRH-BBU mapping scheme (PB-RBM) to find the optimal RRH-BBU mapping strategy by leveraging the prediction information of MRPM. In the PB-RBM algorithm, we employ an A3C algorithm to learn the mapping policy and group the RRHs based on a defined popularity metric to reduce the state and action space of the reinforcement learning algorithm. Finally, extensive experiments are conducted on a real-world dataset, and our algorithm is compared with several matching algorithms, such as ACKTR, heuristic, etc., to demonstrate its superiority, especially reducing 17.5% in RMSE compared to the best-performing baseline.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10557-10572"},"PeriodicalIF":9.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036865","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 Perception Against Data Fabrication Attacks in Vehicular Networks 协同感知对抗车载网络数据伪造攻击

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-16 DOI: 10.1109/TMC.2025.3571013

Zhiping Lin;Liang Xiao;Hongyi Chen;Zefang Lv

{"title":"Collaborative Perception Against Data Fabrication Attacks in Vehicular Networks","authors":"Zhiping Lin;Liang Xiao;Hongyi Chen;Zefang Lv","doi":"10.1109/TMC.2025.3571013","DOIUrl":"https://doi.org/10.1109/TMC.2025.3571013","url":null,"abstract":"Collaborative perception in vehicular networks enables the connected autonomous vehicle (CAV) to gather sensing data, such as feature maps of light detection and ranging (LiDAR) point clouds, from neighboring CAVs to achieve higher perception accuracy, which has performance degradation against data fabrication attacks that share falsified sensing data with random probability. In this paper, we exploit the spatial consistency check to detect the potentially manipulated regions in LiDAR point clouds and measure the inconsistency degree of the received sensing data based on the number of conflict regions, which is the basis for determining the falsified sensing data if the inconsistency degree exceeds the threshold of the hypothesis test. The reinforcement learning (RL)-based collaborative vehicular perception scheme against data fabrication attacks is further proposed to choose CAVs based on the inconsistency degrees, the data quality measured by the confidence scores, the channel gains and the CAV reputations, which enhances the utility as the weighted sum of perception accuracy, speed and minimum latency requirement for data transmission. In addition, the multi-layer perceptron-based neural networks extract the perception features of sensing data from historical experiences, such as the data quality of received feature maps, as well as compress the RL state that linearly increases with the network scales and the spatial granularity of LiDAR point clouds for faster learning. Experimental results based on 10 CAVs equipped with LiDAR sensors and NVIDIA computational units to detect 20 vehicles against data fabrication attacks show that our proposed scheme outperforms the benchmarks in terms of perception accuracy and speed.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10654-10667"},"PeriodicalIF":9.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036878","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

Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity 抗拜占庭攻击和数据异构的联邦学习

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-16 DOI: 10.1109/TMC.2025.3571058

Shiyuan Zuo;Xingrun Yan;Rongfei Fan;Han Hu;Hangguan Shan;Tony Q. S. Quek;Puning Zhao

{"title":"Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity","authors":"Shiyuan Zuo;Xingrun Yan;Rongfei Fan;Han Hu;Hangguan Shan;Tony Q. S. Quek;Puning Zhao","doi":"10.1109/TMC.2025.3571058","DOIUrl":"https://doi.org/10.1109/TMC.2025.3571058","url":null,"abstract":"This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), which uses the geometric median for aggregation and allows flexible round number for local updates. Unlike most existing resilient approaches, which base their convergence analysis on strongly-convex loss functions or homogeneously distributed datasets, this work conducts convergence analysis for both strongly-convex and non-convex loss functions over heterogeneous datasets. The theoretical analysis indicates that as long as the fraction of the data from malicious users is less than half, RAGA can achieve convergence at a rate of <inline-formula><tex-math>$mathcal {O}({1}/{T^{2/3- delta }})$</tex-math></inline-formula> for non-convex loss functions, where <inline-formula><tex-math>$T$</tex-math></inline-formula> is the iteration number and <inline-formula><tex-math>$delta in (0, 2/3)$</tex-math></inline-formula>. For strongly-convex loss functions, the convergence rate is linear. Furthermore, the stationary point or global optimal solution is shown to be attainable as data heterogeneity diminishes. Experimental results validate the robustness of RAGA against Byzantine attacks and demonstrate its superior convergence performance compared to baselines under varying intensities of Byzantine attacks on heterogeneous datasets.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10729-10742"},"PeriodicalIF":9.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036728","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

AOA Sensor Placement for Anchor-Assisted Target Localization in GNSS-Denied Environment: Formulation, Bounds and Optimization 无gnss环境下锚定辅助目标定位的AOA传感器定位：公式、边界和优化

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-16 DOI: 10.1109/TMC.2025.3570768

Sheng Xu;Linlong Wu;Xianliang Li;Xinyu Wu;Tiantian Xu

{"title":"AOA Sensor Placement for Anchor-Assisted Target Localization in GNSS-Denied Environment: Formulation, Bounds and Optimization","authors":"Sheng Xu;Linlong Wu;Xianliang Li;Xinyu Wu;Tiantian Xu","doi":"10.1109/TMC.2025.3570768","DOIUrl":"https://doi.org/10.1109/TMC.2025.3570768","url":null,"abstract":"Target localization technology is widely applied in various applications, such as rescue missions, robot navigation, and the Internet of Things. However, in some scenarios, the positions of sensors are unknown due to the load limitation of the sensor carriers and environmental interferences, resulting in the instability of the global navigation satellite system (GNSS). This paper focuses on optimal angle-of-arrival (AOA) sensor placement using multiple position-unknown sensors for target localization accuracy improvement. To guarantee the uniqueness of the target coordinate, at least two anchors are needed. The anchors are some static benchmark objects in the environment with priori known positions. Firstly, a new optimization problem for AOA target localization accuracy improvement incorporating position-unknown sensors and anchors is formulated. Secondly, the optimal theoretical localization accuracies of the unknown sensors and target are derived by minimizing the trace of the Cramér-Rao lower bounds (CRLBs). Thirdly, a mixture optimization method, including a geometrical initialization and the new proposed simultaneous perturbation stochastic approximation and adaptive momentum estimation (SPSA-Adam) algebraic algorithm, is developed. Then, the correctness of the new theoretical findings and the effectiveness of the proposed sensor placement optimization method are verified by simulation examples.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10792-10806"},"PeriodicalIF":9.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036838","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

Satellite Edge Intelligence: DRL-Based Resource Management for Task Inference in LEO-Based Satellite-Ground Collaborative Networks 卫星边缘智能：基于drl的卫星-地面协同网络任务推理资源管理

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-16 DOI: 10.1109/TMC.2025.3570799

Wenhao Fan;Qingcheng Meng;Guan Wang;Hengwei Bian;Yabin Liu;Yuan’an Liu

{"title":"Satellite Edge Intelligence: DRL-Based Resource Management for Task Inference in LEO-Based Satellite-Ground Collaborative Networks","authors":"Wenhao Fan;Qingcheng Meng;Guan Wang;Hengwei Bian;Yabin Liu;Yuan’an Liu","doi":"10.1109/TMC.2025.3570799","DOIUrl":"https://doi.org/10.1109/TMC.2025.3570799","url":null,"abstract":"Distinguished from terrestrial edge intelligence, satellite edge intelligence has unique characteristics, including the rapid mobility of satellites, limitations in computing and energy resources, and differences in the artificial intelligence models deployed on user devices, satellites, and ground cloud servers. In this paper, we propose a Deep Reinforcement Learning (DRL)-based resource management scheme for task inference in Low Earth Orbit (LEO)-based satellite-ground collaborative networks. In our approach, the task of a user can be inferred by the user device itself, the edge server of the current satellite via user-to-satellite transmission, the edge server of a neighboring satellite via satellite-to-satellite transmission, or a ground cloud server via satellite-to-cloud transmission. Our scheme jointly optimizes task offloading, computing resource allocation, and communication resource allocation to minimize the total system cost, which encompasses trade-offs among the task inference delays for all tasks, the energy consumption of system, and the task inference accuracies for all tasks, while ensuring that the transmit power budgets of all satellites and the satellite coverage time constraints for each user are met. A DRL-based algorithm combining the Softmax Deep Double Deterministic Policy Gradients (SD3) algorithm and two numerical methods is designed to solve the optimization problem efficiently. We prove the convergence of our algorithm and demonstrate the superiority of our scheme by performing extensive simulations in 4 scenarios with 4 reference schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10710-10728"},"PeriodicalIF":9.2,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036759","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

Delay-Sensitive Goods Delivery and In-Situ Sensing Using a Multi-Task Drone 使用多任务无人机的延迟敏感货物交付和原位传感

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-15 DOI: 10.1109/TMC.2025.3570437

Bin Liu;Wei Ni;Ren Ping Liu;Y. Jay Guo;Hongbo Zhu

{"title":"Delay-Sensitive Goods Delivery and In-Situ Sensing Using a Multi-Task Drone","authors":"Bin Liu;Wei Ni;Ren Ping Liu;Y. Jay Guo;Hongbo Zhu","doi":"10.1109/TMC.2025.3570437","DOIUrl":"https://doi.org/10.1109/TMC.2025.3570437","url":null,"abstract":"Drones are evolving into highly capable and adaptable devices, prompting the development of advanced control frameworks. This paper introduces a novel online control framework tailored for a multi-task drone, explicitly addressing the simultaneous execution of in-situ sensing and goods delivery. To tackle this complex scenario, a finite-horizon Markov decision process (FH-MDP) is formulated to ensure not only the prompt delivery of goods but also the minimization of energy consumption and the maximization of the drone's reward for in-situ sensing. A significant contribution lies in establishing the monotonicity and subadditivity of the FH-MDP. This mathematical foundation provides evidence for the existence of an optimal, monotone, deterministic Markovian policy. The crux of the optimal policy revolves around flight distance- and time-related thresholds, determining the precise points at which the drone should switch its optimal action. This unique feature empowers the multi-task drone to make real-time decisions, such as adjusting flight speed or engaging in in-situ sensing, by comparing its current state with these predefined thresholds. This process can be accomplished with a linear complexity, ensuring efficiency in decision-making. The optimality of our approach is rigorously demonstrated through numerical validation, where it is compared against a computationally expensive, dynamic programming-based alternative. Under the considered simulation settings, our approach reduces drone energy consumption by a substantial 19.8% compared to existing benchmarks. This not only highlights the practical effectiveness of the proposed framework but also underscores its potential for significant advancements in the field of drone operations and energy efficiency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10055-10068"},"PeriodicalIF":9.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021356","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

I Sense You Fast: Simultaneous Action and Identity Inference by Slimming Multi-Branch RadarNet I Sense You Fast：通过精简多分支雷达网络实现同步行动和身份推断

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-15 DOI: 10.1109/TMC.2025.3570757

Biyun Sheng;Yan Bao;Hui Cai;Linqing Gui;Fu Xiao

{"title":"I Sense You Fast: Simultaneous Action and Identity Inference by Slimming Multi-Branch RadarNet","authors":"Biyun Sheng;Yan Bao;Hui Cai;Linqing Gui;Fu Xiao","doi":"10.1109/TMC.2025.3570757","DOIUrl":"https://doi.org/10.1109/TMC.2025.3570757","url":null,"abstract":"With the increasing connection between internet and human society, millimeter-wave radar based action recognition and user authentication exhibit remarkable prospects in security scenarios. Existing solutions usually focus on one of the tasks and mainly emphasize accuracy without reducing the inference time. In this paper, we propose a dual-task based Polymorphic Lightweight (PolyLite) RadarNet framework, in which the shared features are fed into two split streams for different tasks under joint supervision. The polymorphic concept here means that the trained network with parallel designs can be slimmed as a single-branch structure for inference. By this design strategy, we can not only efficiently extract spatial-temporal features during the training stage but also largely improve the response speed for simultaneously testing human activities and identities. Specifically, we design triple-view (TRIview) video-like data as the input by successively concatenating the range-velocity and range-angle matrices. Then a PolyLite module with linear and lightweight designs in each branch is integrated into our RadarNet framework to learn discriminative representations. Experimental results demonstrate that our approach is able to reach the accuracy over 98<inline-formula><tex-math>${%}$</tex-math></inline-formula> within 0.21 ms inference time. Especially, untrained intruders can also be successfully identified by a simple matching computation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10743-10759"},"PeriodicalIF":9.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036820","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

Multi-Attribute Consistency Segment Resilient Routing for LEO Satellite Mega Constellations 低轨道卫星大星座的多属性一致性分段弹性路由

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-15 DOI: 10.1109/TMC.2025.3570670

Zhuang Du;Jian Jiao;Hao Liu;Ye Wang;Qinyu Zhang

{"title":"Multi-Attribute Consistency Segment Resilient Routing for LEO Satellite Mega Constellations","authors":"Zhuang Du;Jian Jiao;Hao Liu;Ye Wang;Qinyu Zhang","doi":"10.1109/TMC.2025.3570670","DOIUrl":"https://doi.org/10.1109/TMC.2025.3570670","url":null,"abstract":"Low earth orbit (LEO) satellite mega constellations are regarded to provide pervasive intelligent services in the upcoming sixth generation network via the inter-satellite links (ISL). However, the inherent challenges of LEO satellites including limited onboard resources and failure-prone topology, create substantial hurdles for multi-attribute services routing in mega constellations. In this paper, we propose a multi-attribute consistency segment resilient (MCSR) routing algorithm, and a segmentation approach is designed to partition the mega constellation into non-intersecting segment routing domains (SRDs) through joint optimization of intra- and inter-SDRs update time, which leads to the potential of balancing network load and minimizing routing convergence time. Then, we utilize the multi-attribute consistency to determine the dominant paths of ISLs within and between SRDs for multi-attribute services. Furthermore, we develop a resilient rerouting strategy that utilizes the ephemeris to manage periodic ISL handovers, and selects a reserved/recalculated candidate path from the dominant paths for ISL random failures. Thus, our MCSR routing can converge to an optimal path for multi-attribute services from the dominant paths under ISL failures in mega constellations. Finally, we develop a testbed and simulation results validate the advantages of MCSR routing in handling multi-attribute services and rerouting capability in response to failures.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10823-10839"},"PeriodicalIF":9.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11005668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FBDT: Sum-Throughput Achieving Transport Layer Solution for Multi-RAT Networks FBDT：多rat网络的总吞吐量实现传输层解决方案

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-15 DOI: 10.1109/TMC.2025.3569453

Suresh Srinivasan;Sam Shippey;Ehsan Aryafar;Jacob Chakareski

{"title":"FBDT: Sum-Throughput Achieving Transport Layer Solution for Multi-RAT Networks","authors":"Suresh Srinivasan;Sam Shippey;Ehsan Aryafar;Jacob Chakareski","doi":"10.1109/TMC.2025.3569453","DOIUrl":"https://doi.org/10.1109/TMC.2025.3569453","url":null,"abstract":"Emerging mobile applications give rise to new bandwidth-hungry and latency-sensitive traffic classes that challenge existing wireless systems. Addressing them requires innovative approaches such as simultaneous data transmission across multiple Radio Access Technologies (RATs), e.g., WiFi and WiGig. However, existing transport layer multi-RAT traffic aggregation schemes, e.g., multi-path TCP, suffer from Head-of-Line (HoL) blocking and sub-optimal traffic splitting across the RATs that severely penalize their performance. In this paper, we investigate the design of FBDT, a novel multi-path transport layer solution that for the first time can achieve the sum of the throughput rates across the individual RATs network paths, despite their channel conditions’ dynamics. We have implemented FBDT in the Linux kernel and show substantial improvement in throughput relative to state-of-the-art schemes, e.g, 2.5x gain in a dual-RAT scenario (WiFi and WiGig) when the client is mobile. Second, we extend FBDT to more than two radios and demonstrate that its throughput performance scales linearly with the number of RATs, in contrast to multi-path TCP, whose performance degrades with an increase in the number of RATs. We evaluate the performance of FBDT on different traffic classes and demonstrate: (i) 2-3 times shorter file download times, (ii) up to 10 times shorter streaming times and 10 dB higher video quality for progressive download video applications, and (iii) up to 9 dB higher viewport quality for interactive mobile VR applications, when our viewport quality maximization framework is employed along with FBDT.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10069-10084"},"PeriodicalIF":9.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021178","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