Pub Date : 2025-08-26DOI: 10.1109/TMC.2025.3602796
Yang Jiao;Kai Yang;Dongjin Song
Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. Nevertheless, prevailing DRO techniques encounter three primary challenges in distributed environments: 1) addressing asynchronous updating efficiently; 2) leveraging the prior distribution effectively; 3) appropriately adjusting the degree of robustness based on varying scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the federated distributionally robust optimization (FDRO) problem. In addition, a new uncertainty set, i.e., constrained $D$-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. We further enhance the proposed framework by integrating various uncertainty sets and conducting a comprehensive theoretical analysis of the computational complexity associated with each uncertainty set. To expedite convergence speed, we also introduce ASPIRE-ADP, a method that can dynamically adjust the number of active workers. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity and communication complexity are also analyzed. Extensive empirical studies on real-world datasets validate that the proposed method excels not only in achieving fast convergence and robustness against data heterogeneity and malicious attacks but also in effectively managing the trade-off between robustness and performance.
{"title":"Federated Distributionally Robust Optimization With Non-Convex Objectives: Algorithm and Analysis","authors":"Yang Jiao;Kai Yang;Dongjin Song","doi":"10.1109/TMC.2025.3602796","DOIUrl":"https://doi.org/10.1109/TMC.2025.3602796","url":null,"abstract":"Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. Nevertheless, prevailing DRO techniques encounter three primary challenges in distributed environments: 1) addressing asynchronous updating efficiently; 2) leveraging the prior distribution effectively; 3) appropriately adjusting the degree of robustness based on varying scenarios. To this end, we propose an asynchronous distributed algorithm, named <b>A</b>synchronous <b>S</b>ingle-loo<b>P</b> alternat<b>I</b>ve g<b>R</b>adient proj<b>E</b>ction (ASPIRE) algorithm with the it<b>E</b>rative <b>A</b>ctive <b>S</b><b>E</b>t method (EASE) to tackle the federated distributionally robust optimization (FDRO) problem. In addition, a new uncertainty set, i.e., constrained <inline-formula><tex-math>$D$</tex-math></inline-formula>-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. We further enhance the proposed framework by integrating various uncertainty sets and conducting a comprehensive theoretical analysis of the computational complexity associated with each uncertainty set. To expedite convergence speed, we also introduce ASPIRE-ADP, a method that can dynamically adjust the number of active workers. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity and communication complexity are also analyzed. Extensive empirical studies on real-world datasets validate that the proposed method excels not only in achieving fast convergence and robustness against data heterogeneity and malicious attacks but also in effectively managing the trade-off between robustness and performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1219-1235"},"PeriodicalIF":9.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659195","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}
Pub Date : 2025-08-25DOI: 10.1109/TMC.2025.3602221
Yawen Zheng;Fan Dang;Zihao Yang;Jinyan Jiang;Xu Wang;Lin Wang;Kebin Liu;Xinlei Chen;Yunhao Liu
Bluetooth Low Energy (BLE) is a prevalent technology in various applications due to its low power consumption and wide device compatibility. Despite its numerous advantages, the encryption methods of BLE often expose devices to potential attacks. To fortify security, we investigate the application of Physical-layer Key Generation (PKG), a promising technology that enables devices to generate a shared secret key from their shared physical environment. Although extensively investigated, PKG is generally discussed in the context of Wi-Fi, and existing solutions for BLE demonstrate significantly lower performance. To bridge this gap, we propose a distinctive approach that capitalizes on the inherent characteristics of BLE to facilitate efficient PKG. We utilize the constant tone extension within BLE protocols to extract comprehensive physical layer information and introduce an innovative method that employs Legendre polynomial quantization for PKG. This method facilitates the exchange of secret keys with a high key matching rate and a high key generation rate. The efficacy of our approach is validated through extensive experiments on a software-defined radio platform, underscoring its potential to enhance security in the rapidly expanding field of BLE applications. A pilot study on commercial off-the-shelf BLE devices further validates the system’s practicality, revealing important trade-offs between performance and hardware constraints in real-world deployments.
{"title":"BlueKey: Exploiting Bluetooth Low Energy for Enhanced Physical-Layer Key Generation","authors":"Yawen Zheng;Fan Dang;Zihao Yang;Jinyan Jiang;Xu Wang;Lin Wang;Kebin Liu;Xinlei Chen;Yunhao Liu","doi":"10.1109/TMC.2025.3602221","DOIUrl":"https://doi.org/10.1109/TMC.2025.3602221","url":null,"abstract":"Bluetooth Low Energy (BLE) is a prevalent technology in various applications due to its low power consumption and wide device compatibility. Despite its numerous advantages, the encryption methods of BLE often expose devices to potential attacks. To fortify security, we investigate the application of Physical-layer Key Generation (PKG), a promising technology that enables devices to generate a shared secret key from their shared physical environment. Although extensively investigated, PKG is generally discussed in the context of Wi-Fi, and existing solutions for BLE demonstrate significantly lower performance. To bridge this gap, we propose a distinctive approach that capitalizes on the inherent characteristics of BLE to facilitate efficient PKG. We utilize the constant tone extension within BLE protocols to extract comprehensive physical layer information and introduce an innovative method that employs Legendre polynomial quantization for PKG. This method facilitates the exchange of secret keys with a high key matching rate and a high key generation rate. The efficacy of our approach is validated through extensive experiments on a software-defined radio platform, underscoring its potential to enhance security in the rapidly expanding field of BLE applications. A pilot study on commercial off-the-shelf BLE devices further validates the system’s practicality, revealing important trade-offs between performance and hardware constraints in real-world deployments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1337-1351"},"PeriodicalIF":9.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659213","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}
Pub Date : 2025-08-21DOI: 10.1109/TMC.2025.3601369
Mohd Shariq;Norziana Jamil;Gopal Singh Rawat;Shehzad Ashraf Chaudhry;Mehedi Masud;Ashok Kumar Das
With the rapid advancements in wireless communication technologies, Uncrewed Aerial Vehicles (UAVs), also known as Small Uncrewed Aerial Vehicles (SUAVs) or drones, have been increasingly used in various applications, including the civilian sector. As a result, the security of SUAVs has garnered significant attention from the research community. Furthermore, drones are resource-constrained in nature and can be vulnerable to various known cybersecurity attacks over wireless communication. In light of these considerations, we propose a Provably Secure and Reliable Privacy-Preserving Authentication Scheme for Drone-to-Drone Communications in Internet of Autonomous Things (PSRS-D2D). The proposed scheme employs a secure one-way cryptographic hash and Elliptic Curve Cryptography (ECC) to accomplish a certain level of security. We provide security and privacy analysis, comparing it with competing UAV authentication schemes. This ensures that the PSRS-D2D scheme can withstand various prominent security properties, including mutual authentication and strong anonymity, and is secure against several attacks, such as replay, impersonation, and Man-In-The-Middle (MITM) attacks. We evaluated the performance of the proposed scheme in terms of computational and communicational costs. Furthermore, we conducted a formal security analysis using the Real-Or-Random (ROR) model and the Scyther simulation tools, which demonstrate that our scheme offers significant advantages in terms of security and performance.
{"title":"Provably Secure and Reliable Privacy-Preserving Authentication Scheme for Drone-to-Drone Communications in Internet of Autonomous Things","authors":"Mohd Shariq;Norziana Jamil;Gopal Singh Rawat;Shehzad Ashraf Chaudhry;Mehedi Masud;Ashok Kumar Das","doi":"10.1109/TMC.2025.3601369","DOIUrl":"https://doi.org/10.1109/TMC.2025.3601369","url":null,"abstract":"With the rapid advancements in wireless communication technologies, Uncrewed Aerial Vehicles (UAVs), also known as Small Uncrewed Aerial Vehicles (SUAVs) or drones, have been increasingly used in various applications, including the civilian sector. As a result, the security of SUAVs has garnered significant attention from the research community. Furthermore, drones are resource-constrained in nature and can be vulnerable to various known cybersecurity attacks over wireless communication. In light of these considerations, we propose a <underline>P</u>rovably <underline>S</u>ecure and <underline>R</u>eliable Privacy-Preserving Authentication <underline>S</u>cheme for <underline>D</u>rone-to-<underline>D</u>rone Communications in Internet of Autonomous Things (PSRS-D2D). The proposed scheme employs a secure one-way cryptographic hash and Elliptic Curve Cryptography (ECC) to accomplish a certain level of security. We provide security and privacy analysis, comparing it with competing UAV authentication schemes. This ensures that the PSRS-D2D scheme can withstand various prominent security properties, including mutual authentication and strong anonymity, and is secure against several attacks, such as replay, impersonation, and Man-In-The-Middle (MITM) attacks. We evaluated the performance of the proposed scheme in terms of computational and communicational costs. Furthermore, we conducted a formal security analysis using the Real-Or-Random (ROR) model and the Scyther simulation tools, which demonstrate that our scheme offers significant advantages in terms of security and performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1445-1456"},"PeriodicalIF":9.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659209","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}
Pub Date : 2025-08-21DOI: 10.1109/TMC.2025.3601357
Jinrui Zhang;Deyu Zhang;Tingting Long;Wenxin Chen;Ju Ren;Yunxin Liu;Yudong Zhao;Yaoxue Zhang;Youngki Lee
We present MobiFuse, a high-precision depth perception system on mobile devices that combines dual RGB and Time-of-Flight (ToF) cameras. To achieve this, we leverage physical principles from various environmental factors to propose the Depth Error Indication (DEI) modality, characterizing the depth error of ToF and stereo-matching. Furthermore, we employ a progressive fusion strategy, merging geometric features from ToF and stereo depth maps with depth error features from the DEI modality to create precise depth maps. Additionally, we create a new ToF-Stereo depth dataset, RealToF, to train and validate our model. Our experiments demonstrate that MobiFuse excels over baselines by significantly reducing depth measurement errors by up to 77.7%. It also showcases strong generalization across diverse datasets and proves effectiveness in two downstream tasks: 3D reconstruction and 3D segmentation.
{"title":"MobiFuse: A High-Precision On-Device Depth Perception System With Multi-Data Fusion","authors":"Jinrui Zhang;Deyu Zhang;Tingting Long;Wenxin Chen;Ju Ren;Yunxin Liu;Yudong Zhao;Yaoxue Zhang;Youngki Lee","doi":"10.1109/TMC.2025.3601357","DOIUrl":"https://doi.org/10.1109/TMC.2025.3601357","url":null,"abstract":"We present <bold>MobiFuse</b>, a high-precision depth perception system on mobile devices that combines dual RGB and Time-of-Flight (ToF) cameras. To achieve this, we leverage physical principles from various environmental factors to propose the Depth Error Indication (DEI) modality, characterizing the depth error of ToF and stereo-matching. Furthermore, we employ a progressive fusion strategy, merging geometric features from ToF and stereo depth maps with depth error features from the DEI modality to create precise depth maps. Additionally, we create a new ToF-Stereo depth dataset, <italic>RealToF</i>, to train and validate our model. Our experiments demonstrate that <bold>MobiFuse</b> excels over baselines by significantly reducing depth measurement errors by up to 77.7%. It also showcases strong generalization across diverse datasets and proves effectiveness in two downstream tasks: 3D reconstruction and 3D segmentation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1469-1482"},"PeriodicalIF":9.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659206","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}
Federated Learning (FL) is a popular distributed machine learning method that enables the development of a robust global model through decentralized computation and periodic model aggregation, without requiring direct access to clients’ data. However, data heterogeneity poses a significant challenge in FL, and the global long-tail distribution exacerbates this issue. While substantial research has focused on mitigating performance degradation caused by long-tailed distributions, existing methods typically concentrate on addressing discrepancies between local and global class distributions, often overlooking the fact that these discrepancies stem from variations in the data itself. To address this, we propose a novel approach, Federated Context Optimization and Feature Information Decoupling (FedDR), which generates partition strategies for each sample to extract and leverage long-tail, global, personalized, and label-text information within its features to enhance the representational distinction of tail classes. Specifically, we first design a Feature Information Decoupling module that separates global, personalized, and long-tail information within the features and incorporates this information into the loss function to strengthen the global model’s focus on personalized information in tail samples. Furthermore, to exploit the textual label information embedded in the samples, we integrate a cross-modal model, CoOp, which utilizes open-vocabulary prior knowledge, and implement dynamic knowledge distillation between the client model and CoOp to enhance the client model’s feature representation capability. Extensive experimental results on multiple benchmarks demonstrate that the proposed FedDR outperforms state-of-the-art methods in the federated long-tailed learning setting.
联邦学习（FL）是一种流行的分布式机器学习方法，它可以通过分散计算和周期性模型聚合来开发健壮的全局模型，而不需要直接访问客户端的数据。然而，数据异质性对FL提出了重大挑战，全球长尾分布加剧了这一问题。虽然大量的研究集中在减轻长尾分布导致的性能下降上，但现有的方法通常集中在解决局部和全局类分布之间的差异上，往往忽略了这些差异源于数据本身变化的事实。为了解决这个问题，我们提出了一种新的方法，联邦上下文优化和特征信息解耦（federatedcontext Optimization and Feature Information Decoupling, federdr），它为每个样本生成分区策略，以提取和利用其特征中的长尾、全局、个性化和标签文本信息，以增强尾部类的代表性区别。具体来说，我们首先设计了一个特征信息解耦模块，将特征中的全局信息、个性化信息和长尾信息分离出来，并将这些信息合并到损失函数中，以加强全局模型对尾部样本中个性化信息的关注。此外，为了挖掘样本中嵌入的文本标签信息，我们集成了一个利用开放词汇先验知识的跨模态模型CoOp，并在客户端模型和CoOp之间实现动态知识蒸馏，以增强客户端模型的特征表示能力。在多个基准上的大量实验结果表明，在联邦长尾学习设置中，所提出的FedDR优于最先进的方法。
{"title":"Federated Learning on Heterogeneous and Long-Tailed Data via Disentangled Representation","authors":"Yizhi Zhou;Junxiao Wang;Yuchen Qin;Xin Xie;Zhipeng Song;Heng Qi","doi":"10.1109/TMC.2025.3600767","DOIUrl":"https://doi.org/10.1109/TMC.2025.3600767","url":null,"abstract":"Federated Learning (FL) is a popular distributed machine learning method that enables the development of a robust global model through decentralized computation and periodic model aggregation, without requiring direct access to clients’ data. However, data heterogeneity poses a significant challenge in FL, and the global long-tail distribution exacerbates this issue. While substantial research has focused on mitigating performance degradation caused by long-tailed distributions, existing methods typically concentrate on addressing discrepancies between local and global class distributions, often overlooking the fact that these discrepancies stem from variations in the data itself. To address this, we propose a novel approach, Federated Context Optimization and Feature Information Decoupling (FedDR), which generates partition strategies for each sample to extract and leverage long-tail, global, personalized, and label-text information within its features to enhance the representational distinction of tail classes. Specifically, we first design a Feature Information Decoupling module that separates global, personalized, and long-tail information within the features and incorporates this information into the loss function to strengthen the global model’s focus on personalized information in tail samples. Furthermore, to exploit the textual label information embedded in the samples, we integrate a cross-modal model, CoOp, which utilizes open-vocabulary prior knowledge, and implement dynamic knowledge distillation between the client model and CoOp to enhance the client model’s feature representation capability. Extensive experimental results on multiple benchmarks demonstrate that the proposed FedDR outperforms state-of-the-art methods in the federated long-tailed learning setting.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1367-1380"},"PeriodicalIF":9.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659184","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}
Pub Date : 2025-08-19DOI: 10.1109/TMC.2025.3600390
Jing Li;Jianping Wang;Weifa Liang;Xiaohua Jia;Albert Y. Zomaya
Digital twin (DT) technology enables smooth integrations of cyber and physical worlds in alignment with the Industry 4.0 initiative. DTs are virtual presentations of physical objects. Through synchronizations with physical objects in real-time, DTs can reflect the states of their objects with high fidelity. Orthogonal to the DT technology, mobile edge computing (MEC) is a promising computing paradigm that shifts computing power to the edge network, which is appropriate for delay-sensitive intelligent services. In this paper, we study fidelity-aware inference services in a DT-assisted MEC environment, where machine learning-based inference models must be continuously retrained using updated DT data in order to provide high-fidelity services for consumers. To this end, we first formulate two novel optimization problems: the initial DT and model placement problem with the aim of minimizing the total cost of various resources consumed for the placements, and the cumulative fidelity maximization problem to maximize the long-term cumulative fidelity of all service models while minimizing the cost of resource consumption on enhancements of service model fidelitiess over a given time horizon, through jointly scheduling mobile devices to synchronize with their DTs by uploading their update data and determining whether DTs and/or models to be migrated at each time slot. We then develop an efficient algorithm for the initial DT and model placement problem, through a reduction to a series of minimum-cost maximum matching problems in auxiliary graphs. We also devise an online algorithm with a provable competitive ratio for the cumulative fidelity maximization problem, by designing an elegant service request admission strategy. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their baselines by no less than 28%.
{"title":"Inference Service Fidelity Maximization in DT-Assisted Edge Computing","authors":"Jing Li;Jianping Wang;Weifa Liang;Xiaohua Jia;Albert Y. Zomaya","doi":"10.1109/TMC.2025.3600390","DOIUrl":"https://doi.org/10.1109/TMC.2025.3600390","url":null,"abstract":"Digital twin (DT) technology enables smooth integrations of cyber and physical worlds in alignment with the Industry 4.0 initiative. DTs are virtual presentations of physical objects. Through synchronizations with physical objects in real-time, DTs can reflect the states of their objects with high fidelity. Orthogonal to the DT technology, mobile edge computing (MEC) is a promising computing paradigm that shifts computing power to the edge network, which is appropriate for delay-sensitive intelligent services. In this paper, we study fidelity-aware inference services in a DT-assisted MEC environment, where machine learning-based inference models must be continuously retrained using updated DT data in order to provide high-fidelity services for consumers. To this end, we first formulate two novel optimization problems: the initial DT and model placement problem with the aim of minimizing the total cost of various resources consumed for the placements, and the cumulative fidelity maximization problem to maximize the long-term cumulative fidelity of all service models while minimizing the cost of resource consumption on enhancements of service model fidelitiess over a given time horizon, through jointly scheduling mobile devices to synchronize with their DTs by uploading their update data and determining whether DTs and/or models to be migrated at each time slot. We then develop an efficient algorithm for the initial DT and model placement problem, through a reduction to a series of minimum-cost maximum matching problems in auxiliary graphs. We also devise an online algorithm with a provable competitive ratio for the cumulative fidelity maximization problem, by designing an elegant service request admission strategy. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their baselines by no less than 28%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1352-1366"},"PeriodicalIF":9.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659185","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}
Pub Date : 2025-08-19DOI: 10.1109/TMC.2025.3599838
Samuel D. Okegbile;Haoran Gao;Jun Cai
This paper introduces a novel secure split federated semantic learning (SFsL) framework to facilitate the maintenance and evolution of digital twin networks (DTNs). Efficiently updating and evolving DTNs generally involves several critical processes: semantic extraction and transmission for physical-to-virtual synchronization, virtual model transformation and verification, and ensuring the security and privacy of physical entity data. While conventional semantic communication frameworks can effectively address semantic extraction and transmission, the complexities of virtual model transformation, verification, and data security demand a more comprehensive approach. To address these challenges, the proposed SFsL framework integrates split federated learning with task-oriented secure semantic communication schemes. In addition, it incorporates a token-based semantic defence method to distinguish between adversarial and authentic semantic data and an asynchronous secure model aggregation mechanism to enhance data-sharing efficiency. The system reliability is then formulated as a stochastic optimization problem, aiming to minimize cost complexity while maintaining high accuracy during periodic model aggregation. Evaluation results, obtained using performance metrics such as privacy loss, experienced loss, accuracy, cost and reliability, demonstrate that the SFsL framework outperforms other commonly adopted security and privacy schemes, offering improved efficiency towards the maintenance and evolution of such dynamic systems. This highlights the capability of SFsL to enable adaptive, efficient and reliable network evolutions when deployed in practical DTNs with dynamic resource constraints.
{"title":"A Novel Secure Split Federated Semantic Learning Framework and its Optimization for Digital Twin Network Evolution","authors":"Samuel D. Okegbile;Haoran Gao;Jun Cai","doi":"10.1109/TMC.2025.3599838","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599838","url":null,"abstract":"This paper introduces a novel secure split federated semantic learning (SFsL) framework to facilitate the maintenance and evolution of digital twin networks (DTNs). Efficiently updating and evolving DTNs generally involves several critical processes: semantic extraction and transmission for physical-to-virtual synchronization, virtual model transformation and verification, and ensuring the security and privacy of physical entity data. While conventional semantic communication frameworks can effectively address semantic extraction and transmission, the complexities of virtual model transformation, verification, and data security demand a more comprehensive approach. To address these challenges, the proposed SFsL framework integrates split federated learning with task-oriented secure semantic communication schemes. In addition, it incorporates a token-based semantic defence method to distinguish between adversarial and authentic semantic data and an asynchronous secure model aggregation mechanism to enhance data-sharing efficiency. The system reliability is then formulated as a stochastic optimization problem, aiming to minimize cost complexity while maintaining high accuracy during periodic model aggregation. Evaluation results, obtained using performance metrics such as privacy loss, experienced loss, accuracy, cost and reliability, demonstrate that the SFsL framework outperforms other commonly adopted security and privacy schemes, offering improved efficiency towards the maintenance and evolution of such dynamic systems. This highlights the capability of SFsL to enable adaptive, efficient and reliable network evolutions when deployed in practical DTNs with dynamic resource constraints.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1302-1319"},"PeriodicalIF":9.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659186","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}
Pub Date : 2025-08-19DOI: 10.1109/TMC.2025.3600460
Shanshan Song;Xiujuan Wu;Cangzhu Xu;Miao Pan;Guangjie Han
Critical node identification is essential for Underwater Acoustic Sensor Networks (UASNs) to ensure network connectivity and reliability. Existing methods identify critical nodes by evaluating their contributions to network connectivity and node communication count. However, these methods identify critical nodes inaccurately due to neglecting the influence of packet collisions, leading to unreliable network. Packet collisions disrupt connected links and cause communication failures, resulting in unreliable network connectivity and improper communication count. To this end, we propose the Collision-Aware Critical Node Identification Algorithm (CCNIA), which accounts for the impact of packet collisions to improve the accuracy of critical node identification and enhance network reliability. CCNIA identifies critical nodes with high connectivity, large collision probability, and heavy network load, through building the three following interdependent models. Specifically, Topological Connectivity Model (TCM) evaluates link reachability by analyzing connectivity and density within a node’s local network. Based on TCM, Collision Probability Model (CPM) further ensures packet reliability by quantifying the impact of packet collisions on critical node identification. Through CPM’s reliable packet transmissions, Network Load Model (NLM) assesses network efficiency by analyzing node occurrence count within global end-to-end communication paths. Experiments show that CCNIA outperforms existing methods across diverse network configurations, enhancing network reliability in terms of packet delivery ratio, delay, and energy efficiency.
{"title":"Enhancing Network Reliability in UASNs: A Collision-Aware Critical Node Identification Algorithm","authors":"Shanshan Song;Xiujuan Wu;Cangzhu Xu;Miao Pan;Guangjie Han","doi":"10.1109/TMC.2025.3600460","DOIUrl":"https://doi.org/10.1109/TMC.2025.3600460","url":null,"abstract":"Critical node identification is essential for Underwater Acoustic Sensor Networks (UASNs) to ensure network connectivity and reliability. Existing methods identify critical nodes by evaluating their contributions to network connectivity and node communication count. However, these methods identify critical nodes inaccurately due to neglecting the influence of packet collisions, leading to unreliable network. Packet collisions disrupt connected links and cause communication failures, resulting in unreliable network connectivity and improper communication count. To this end, we propose the Collision-Aware Critical Node Identification Algorithm (CCNIA), which accounts for the impact of packet collisions to improve the accuracy of critical node identification and enhance network reliability. CCNIA identifies critical nodes with high connectivity, large collision probability, and heavy network load, through building the three following interdependent models. Specifically, Topological Connectivity Model (TCM) evaluates link reachability by analyzing connectivity and density within a node’s local network. Based on TCM, Collision Probability Model (CPM) further ensures packet reliability by quantifying the impact of packet collisions on critical node identification. Through CPM’s reliable packet transmissions, Network Load Model (NLM) assesses network efficiency by analyzing node occurrence count within global end-to-end communication paths. Experiments show that CCNIA outperforms existing methods across diverse network configurations, enhancing network reliability in terms of packet delivery ratio, delay, and energy efficiency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1398-1413"},"PeriodicalIF":9.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659196","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}
Pub Date : 2025-08-19DOI: 10.1109/TMC.2025.3600682
Wenwen Xie;Geng Sun;Bei Liu;Jiahui Li;Jiacheng Wang;Hongyang Du;Dusit Niyato;Dong In Kim
Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an uncrewed aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication.Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.
{"title":"Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications With Deep Reinforcement Learning","authors":"Wenwen Xie;Geng Sun;Bei Liu;Jiahui Li;Jiacheng Wang;Hongyang Du;Dusit Niyato;Dong In Kim","doi":"10.1109/TMC.2025.3600682","DOIUrl":"https://doi.org/10.1109/TMC.2025.3600682","url":null,"abstract":"Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an uncrewed aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication.Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1381-1397"},"PeriodicalIF":9.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659210","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}
Pub Date : 2025-08-19DOI: 10.1109/TMC.2025.3599880
Tianyu Zhang;Jiachen Wang;X. Sharon Hu;Song Han
Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, wide coverage, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling data flows to meet their hard real-time requirements in industrial applications. In this paper, we first leverage a 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantee through time-frequency resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct a schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. For large-scale multi-cell 5G industrial systems with cloud radio access network (C-RAN) architecture, we further introduce a user association algorithm respecting the real-time requirements of individual user equipment (UEs). Extensive experimental studies show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.
{"title":"5G-TPS: A Two-Phase Real-Time Scheduling and Adaptation Framework for 5G Radio Access Networks","authors":"Tianyu Zhang;Jiachen Wang;X. Sharon Hu;Song Han","doi":"10.1109/TMC.2025.3599880","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599880","url":null,"abstract":"Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, wide coverage, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling data flows to meet their hard real-time requirements in industrial applications. In this paper, we first leverage a 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantee through time-frequency resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct a schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. For large-scale multi-cell 5G industrial systems with cloud radio access network (C-RAN) architecture, we further introduce a user association algorithm respecting the real-time requirements of individual user equipment (UEs). Extensive experimental studies show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1320-1336"},"PeriodicalIF":9.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659226","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}
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