IEEE Transactions on Mobile Computing最新文献_第6页

A Secure and Efficient Distributed Semantic Communication System for Heterogeneous Internet of Things 面向异构物联网的安全高效分布式语义通信系统

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-13 DOI: 10.1109/TMC.2026.3664479

Weihao Zeng;Xinyu Xu;Qianyun Zhang;Jiting Shi;Zhenyu Guan;Shufeng Li;Zhijin Qin

{"title":"A Secure and Efficient Distributed Semantic Communication System for Heterogeneous Internet of Things","authors":"Weihao Zeng;Xinyu Xu;Qianyun Zhang;Jiting Shi;Zhenyu Guan;Shufeng Li;Zhijin Qin","doi":"10.1109/TMC.2026.3664479","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664479","url":null,"abstract":"Semantic communications are expected to improve the transmission efficiency in Internet of Things (IoT) networks. However, the distributed nature of networks and heterogeneity of devices challenge the secure utilization of semantic communication systems. In this paper, we develop a distributed semantic communication system that achieves the security and efficiency during update and usage phases. A blockchain-based trust scheme for update is designed to continuously train and synchronize the system in dynamic IoT environments. To improve the updating efficiency, we propose a flexible semantic coding method base on compressive semantic knowledge bases. It greatly reduces the amount of data shared among devices for system update, and realizes the flexible adjustment of the size of knowledge bases and the number of transmitted signal symbols in model training and inference stages. In the usage phase, a signature mechanism for lossy semantics is introduced to guarantee the integrity and authenticity of the transmitted semantics in lossy semantic communications. We further design a noise-aware differential privacy mechanism, which introduces optimized noise based on the different channel information available to heterogeneous devices. Experiments on transmission tasks show that the proposed system defends against cross-phase attacks of compromising semantics integrity and reduces the data to be shared in the update phase by about 36% to 90%, and in the usage phase by 60% compared with related works.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10370-10384"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517467","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

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning 联邦学习中不可信参与者的安全模型训练

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-16 DOI: 10.1109/TMC.2026.3664822

Zhihui Zhao;Xiaorong Dong;Yimo Ren;Jianhua Wang;Dan Yu;Hongsong Zhu;Yongle Chen

{"title":"SMTFL: Secure Model Training to Untrusted Participants in Federated Learning","authors":"Zhihui Zhao;Xiaorong Dong;Yimo Ren;Jianhua Wang;Dan Yu;Hongsong Zhu;Yongle Chen","doi":"10.1109/TMC.2026.3664822","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664822","url":null,"abstract":"Federated learning is an essential distributed model training technique. However, threats such as gradient inversion attacks and poisoning attacks pose significant risks to both privacy of training data and model correctness. We propose SMTFL, a novel approach for secure model training in federated learning. To safeguard gradients privacy against gradient inversion attacks, clients are dynamically grouped, allowing one client’s gradient to be divided to obfuscate the gradients of other clients within the group. This method incorporates checks and balances to reduce the collusion for inferring specific client data. To detect poisoning attacks from malicious clients, we assess the impact of aggregated gradients on the global model’s performance, enabling effective identification and exclusion of malicious clients. Each client’s gradients are encrypted and stored, with decryption collectively managed by all clients. The detected poisoning gradients are invalidated from the global model through an unlearning method. Compared to related work, SMTFL does not rely on trusted participants, avoids the performance degradation caused with traditional noise-injection, and avoids complex homomorphic encryption during gradient aggregation. SMTFL is evaluated on five datasets, and these results demonstrate its effectiveness in defending against gradient inversion and poisoning attacks. The model accuracy is nearly restored to its pre-attack state when SMTFL is deployed. Furthermore, SMTFL achieves over 95% accuracy in identifying malicious clients while maintaining a false positive rate for honest clients within 5%, which is 6% lower than the latest methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10590-10606"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517670","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

Large Language Model-Based Gray Wolf Optimization for Near-Field ISAC Networks 基于大语言模型的近场ISAC网络灰狼优化

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-17 DOI: 10.1109/TMC.2026.3665626

Zhen Chen;Kezhi Wang;Jianqing Li;Xiu Yin Zhang;Kai-Kit Wong

{"title":"Large Language Model-Based Gray Wolf Optimization for Near-Field ISAC Networks","authors":"Zhen Chen;Kezhi Wang;Jianqing Li;Xiu Yin Zhang;Kai-Kit Wong","doi":"10.1109/TMC.2026.3665626","DOIUrl":"https://doi.org/10.1109/TMC.2026.3665626","url":null,"abstract":"The advent of extremely large antenna arrays and high-frequency signaling is expected to enable next-generation integrated sensing and communication (ISAC) networks to predominantly operate in the near-field region. Due to the dual influence of distance and angle on wave propagation characteristics in the near-field region, accurately modeling these characteristics remains a critical challenge. Motivated by the potential of large language models (LLMs) in angle prediction and distance estimation, an LLM-enhanced multi-objective optimization problem (MOOP) is developed to accurately capture the dependence of the channel on both the angular position and distance. The formulated LLM-enhanced MOOP framework is decomposed into a series of sub-problems, which can balance spectral efficiency for communication and localization accuracy for sensing. To overcome the computational and energy challenges associated with LLMs, a gray wolf optimization (GWO)-based algorithm is integrated as black-box search operator with LLM-specific prompt engineering to solve these sub-problems. Numerical results demonstrate that the proposed LLM-GWO scheme achieves an trade-off between communication and sensing performance, outperforming baseline approaches in terms of both Pareto front quality and convergence.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10577-10589"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517622","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

Optimizing Dependency-Aware Age of Information in STCC Systems via MADRL 基于MADRL的STCC系统信息依赖感知时代优化

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-16 DOI: 10.1109/TMC.2026.3654899

Changqing Xia;Jisong Yu;Chi Xu;Xi Jin;Peng Zeng

{"title":"Optimizing Dependency-Aware Age of Information in STCC Systems via MADRL","authors":"Changqing Xia;Jisong Yu;Chi Xu;Xi Jin;Peng Zeng","doi":"10.1109/TMC.2026.3654899","DOIUrl":"https://doi.org/10.1109/TMC.2026.3654899","url":null,"abstract":"Driven by the new generation of information technology, modern manufacturing is shifting from traditional rigid production to flexible, customized models. This transformation imposes higher demands on systems’ dynamic adaptability and real-time responsiveness. The emergence of Edge Computing (EC) and the Age of Information (AoI) offers promising solutions to these challenges. Significant progress has been made in areas such as edge resource allocation and information update strategies, contributing to enhanced system responsiveness. However, most existing studies assume task independence, which limits their applicability in complex scenarios (such as high-end manufacturing), where task dependency is dynamic and ubiquitous. In addition, ensuring end-to-end integration of sensing, transmission, computation, and control (STCC) remains a major challenge. To address this gap, this study proposes a collaborative framework focusing on sensing, transmission, computation, and control (STCC), centering around task chains. For the first time, it introduces and defines the Dependency-Aware Age of Information (DAoI) metric to quantify inter-task dependencies and the effects of delay propagation, thereby enabling a more accurate assessment of data timeliness. Additionally, an optimal controller using the Linear Quadratic Regulator (LQR) is designed. Our model optimizes control performance and energy consumption through a Markov Decision Process (MDP). The proposed Dependency-Aware Heterogeneous Task and Resource Co-scheduling (MAPPO-DHTCO) algorithm efficiently manages task dependencies and resource allocation. Experimental results show that this method has significantly improved performance compared to the benchmark method.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9878-9895"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517939","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

SubLoRa: High-Throughput LoRa Backscatter Communication subora：高吞吐量LoRa反向散射通信

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-19 DOI: 10.1109/TMC.2026.3655394

Jingyi Bai;Caihui Du;Jihong Yu;Ju Ren;Rongrong Zhang;Haipeng Yao

{"title":"SubLoRa: High-Throughput LoRa Backscatter Communication","authors":"Jingyi Bai;Caihui Du;Jihong Yu;Ju Ren;Rongrong Zhang;Haipeng Yao","doi":"10.1109/TMC.2026.3655394","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655394","url":null,"abstract":"Ambient LoRa backscatter enables long-range communication due to its long-period symbol. Most of the existing works struggle to balance range and throughput: systems with symbol-level modulation offers long transmission range at the cost of low data rate, while systems with high modulation efficiency suffer from limited transmission distance due to weak signals. We propose SubLoRa, which significantly improves throughput while maintaining long-range communication. SubLoRa achieves the high-rate modulation by the proposed Subchirp Frequency Offset Modulation (SFOM), which divides a chirp into multiple subchirps each being shifted by frequency. We propose a prewaveform sampling strategy that enables SFOM with low power. For decoding, we propose a Frequency-Difference Recombination of Chirp (FDRC) demodulation based on time-domain correlation, which enables reliable decoding of low-power signals in long-range links. We implement SubLoRa and conduct extensive evaluation. The results show SubLoRa can achieve up to 29.66× throughput gain and 7.54× throughput gain compared with the State-Of-The-Art (SOTA) LoRa backscatter system PLoRa and Pacim, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9833-9846"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148517940","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

mmE-Loc: Facilitating Accurate Drone Landing With Ultra-High-Frequency Localization mmE-Loc：利用超高频定位技术促进无人机精确着陆

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-19 DOI: 10.1109/TMC.2026.3666235

Haoyang Wang;Jingao Xu;Xinyu Luo;Ting Zhang;Xuecheng Chen;Ruiyang Duan;Yunhao Liu;Jianfeng Zheng;Weijie Hong;Xiaoqiang Ji;Yuqing Tang;Xinlei Chen

{"title":"mmE-Loc: Facilitating Accurate Drone Landing With Ultra-High-Frequency Localization","authors":"Haoyang Wang;Jingao Xu;Xinyu Luo;Ting Zhang;Xuecheng Chen;Ruiyang Duan;Yunhao Liu;Jianfeng Zheng;Weijie Hong;Xiaoqiang Ji;Yuqing Tang;Xinlei Chen","doi":"10.1109/TMC.2026.3666235","DOIUrl":"https://doi.org/10.1109/TMC.2026.3666235","url":null,"abstract":"For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit the temporal consistency and spatial complementarity between these two modalities, we propose two innovative modules: (i) the Consistency-instructed Collaborative Tracking module, which further leverages the drone’s physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and (ii) the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Extensive experiments (30+ hours) demonstrate that mmE-Loc attains 0.083 <inline-formula><tex-math>$text{m}$</tex-math></inline-formula> localization accuracy and 5.12 <inline-formula><tex-math>$text{ms}$</tex-math></inline-formula> end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10651-10669"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518552","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

SRDrone: LLM-Driven Self-Refinement for Embodied Drone Task Planning SRDrone: llm驱动的无人机任务规划自细化

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-16 DOI: 10.1109/TMC.2026.3664806

Deyu Zhang;Xicheng Zhang;Jinrui Zhang;Jiahao Li;Tingting Long;Xunhua Dai;Yongjian Fu;Ju Ren;Yaoxue Zhang

{"title":"SRDrone: LLM-Driven Self-Refinement for Embodied Drone Task Planning","authors":"Deyu Zhang;Xicheng Zhang;Jinrui Zhang;Jiahao Li;Tingting Long;Xunhua Dai;Yongjian Fu;Ju Ren;Yaoxue Zhang","doi":"10.1109/TMC.2026.3664806","DOIUrl":"https://doi.org/10.1109/TMC.2026.3664806","url":null,"abstract":"We introduce <italic>SRDrone, a novel system designed for self-refinement task planning in industrial-grade embodied drones. <italic>SRDrone incorporates two key technical contributions: First, it employs a continuous state evaluation methodology to robustly and accurately determine task outcomes and provide explanatory feedback. This approach supersedes conventional reliance on single-frame final-state assessment for continuous, dynamic drone operations. Second, <italic>SRDrone implements a hierarchical Behavior Tree (BT) modification model. This model integrates multi-level BT plan analysis with a constrained strategy space to enable structured reflective learning from experience. Experimental results demonstrate that <italic>SRDrone achieves a 44.87% improvement in Success Rate (SR) over baseline methods. Furthermore, real-world deployment utilizing an experience base optimized through iterative self-refinement attains a 96.25% SR. By embedding adaptive task refinement capabilities within an industrial-grade BT planning framework, <italic>SRDrone effectively integrates the general reasoning intelligence of Large Language Models (LLMs) with the stringent physical execution constraints inherent to embodied drones.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"10757-10772"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518555","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 Semantic Information Extraction and Resource Allocation in User-Centric Semantic Communication Networks 以用户为中心的语义通信网络联合语义信息提取与资源分配

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-16 DOI: 10.1109/TMC.2026.3655016

Baolin Chong;Fengqian Guo;Cheng Guo;Hancheng Lu

{"title":"Joint Semantic Information Extraction and Resource Allocation in User-Centric Semantic Communication Networks","authors":"Baolin Chong;Fengqian Guo;Cheng Guo;Hancheng Lu","doi":"10.1109/TMC.2026.3655016","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655016","url":null,"abstract":"Semantic communication (SemCom) has recently emerged as a transformative paradigm for next-generation wireless systems, aiming to enable task-oriented information transmission. However, existing SemCom networks rely on cell-centric architectures, where users, particularly those at the cell edge, suffer from severe inter-cell interference and poor channel conditions. These issues undermine semantic accuracy, induce intolerable delay, and ultimately hinder the fulfillment of quality-of-service (QoS) requirements. To overcome these limitations, we propose the user-centric SemCom (UCSC) network, a novel architecture that integrates user-centric networks with semantic-aware transmission. In UCSC, each user is served by a dedicated cooperative access point group, enabling effective interference mitigation and reliable semantic delivery, even at the cell edge. To fully exploit the potential of UCSC, we formulate a joint optimization problem for semantic information extraction and resource allocation, aiming to minimize total energy consumption under constraints on semantic similarity and delay. By decomposing the original problem into three tractable subproblems, semantic information extraction, computational capacity control, and power allocation, we propose an alternating optimization algorithm to efficiently solve it. Simulation results demonstrate that UCSC, combined with the proposed algorithm, achieves significant performance improvements in reducing energy consumption compared with cell-centric SemCom networks, especially under stringent QoS requirements.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9676-9690"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519206","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

Edge-Cloud Switched Low-Carbon Image Segmentation for Autonomous Vehicles 自动驾驶汽车边缘云切换低碳图像分割

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-16 DOI: 10.1109/TMC.2026.3655107

Siyuan Zhou;Duc Van Le;Rui Tan

{"title":"Edge-Cloud Switched Low-Carbon Image Segmentation for Autonomous Vehicles","authors":"Siyuan Zhou;Duc Van Le;Rui Tan","doi":"10.1109/TMC.2026.3655107","DOIUrl":"https://doi.org/10.1109/TMC.2026.3655107","url":null,"abstract":"Existing autonomous vehicles (AVs) utilize neither cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency, nor solar energy to offset the energy usage of car-borne computing. They are in general equipped with the resource-constrained edge computing devices which may be unable to execute the compute-intensive deep learning models in real time. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving, which is demonstrated by our city-scale real-world measurements over the fifth generation (5G) mobile networks. Moroever, the cost and form factor declines of solar harvesting systems make the quest of integrating them with AVs for improving carbon efficiency more promising. In this paper, we present the design and implementation of ECSeg, an edge-cloud switched low-carbon image segmentation system for AVs equipped with roof-mounted solar panels. ECSeg dynamically switches between edge and cloud processing to execute semantic segmentation models in real time while aiming to decarbonize AV computing by maximizing the use of harvested solar energy. The switching decision is challenging due to the complex interdependencies among various factors, including dynamic wireless channel conditions, vehicle motion, visual scene changes, and available renewable energy. To tackle this, ECSeg employs deep reinforcement learning to learn an optimal switching policy. Extensive evaluations based on real-world experiments and trace-driven simulations demonstrate that ECSeg outperforms six baseline approaches, achieving 98.8% reduction in computing’s carbon emission, while maintaining high image segmentation accuracy, compared with our earlier design without integrating the solar panel.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9691-9706"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519207","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

Scalable Deployment of Aerial Networks via Radio Tomographic Attenuation Mapping 通过无线电层析衰减映射的空中网络可扩展部署

IF 8.8 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-01-22 DOI: 10.1109/TMC.2026.3656779

Ayon Chakraborty;Subrata Das;Pranav Ramesh

{"title":"Scalable Deployment of Aerial Networks via Radio Tomographic Attenuation Mapping","authors":"Ayon Chakraborty;Subrata Das;Pranav Ramesh","doi":"10.1109/TMC.2026.3656779","DOIUrl":"https://doi.org/10.1109/TMC.2026.3656779","url":null,"abstract":"A key challenge in deploying Unmanned Aerial Vehicle (UAV)-based wireless networks is determining optimal UAV placement in 3D space to ensure robust connectivity for ground users. This requires estimating the signal propagation loss across the aerial region for each user, typically represented as a Radio Environment Map (REM). Existing approaches often rely on learning-based or interpolation methods using sparse, noisy measurements, but such maps must be recomputed as users move, incurring high measurement overhead and limiting scalability. In this paper, we present <sc>SkyScale, a Radio Tomographic Imaging (RTI)-based framework that estimates the wireless attenuation characteristics of the environment - terrain-dependent properties that remain invariant to UE locations. By decoupling REM estimation from UE-specific measurements, <sc>SkyScale enables scalable and low-overhead deployment across dynamic network conditions. We implement and evaluate <sc>SkyScale on a custom-built WiFi UAV testbed, a real-world LTE trace, and high-fidelity 5G simulation datasets. Across these settings, <sc>SkyScale reduces measurement costs by 10× or more compared to state-of-the-art REM estimation methods, while maintaining high accuracy under user mobility and UE churn.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 7","pages":"9707-9720"},"PeriodicalIF":8.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148518547","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