IEEE Transactions on Services Computing最新文献_第3页

AdpFL: A Privacy-Preserving Federated Learning Framework Through Adaptive Model Pruning on Non-IID Data 基于非iid数据自适应模型剪枝的隐私保护联邦学习框架

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-04-23 DOI: 10.1109/TSC.2026.3686986

Dewei Ning;Yong-Feng Ge;Elisa Bertino;Zhonglong Zheng;Yunliang Jiang;Hua Wang

{"title":"AdpFL: A Privacy-Preserving Federated Learning Framework Through Adaptive Model Pruning on Non-IID Data","authors":"Dewei Ning;Yong-Feng Ge;Elisa Bertino;Zhonglong Zheng;Yunliang Jiang;Hua Wang","doi":"10.1109/TSC.2026.3686986","DOIUrl":"10.1109/TSC.2026.3686986","url":null,"abstract":"Federated learning (FL) has shown great potential, especially with the rise of complex foundation models and growing privacy needs. FL has experienced challenges, including high communication costs, privacy concerns of user data, and the complexities of non-independent and identically distributed (non-IID) data. This paper aims to address these challenges through our proposed FL framework called Adaptive Pruning FL (<bold>AdpFL). AdpFL conducts an exploration phase prior to the formal FL process, during which each local dataset’s deviation and corresponding threshold are computed locally. These results are then utilised to perform model pruning during the formal FL training. By doing so, the method achieves full adaptivity while simultaneously addressing challenges related to communications costs and non-IID data. Meanwhile, AdpFL enhances pruning by replacing part of the parameters with zeros, thereby preventing membership inference attacks (MIA) and ensuring privacy in both local and global models. The extensive experiments on various datasets and FL tasks demonstrate that our method not only efficiently accelerates global convergence but also achieves superior accuracy and robustness compared to traditional methods. AdpFL reduces computational overhead and decreases the effective update size (i.e., the proportion of non-zero parameters) by approximately half while improving global accuracy by up to 20%, and it also lowers the accuracy of white-box MIA attacks across various models. These improvements make FL more efficient, scalable, and practical in real-world situations.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3269-3282"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147735578","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

PrivTI: Efficient End-to-End Privacy-Preserving Inference for Transformer-Based Models in MLaaS MLaaS中基于变压器模型的高效端到端隐私保护推理

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-04-17 DOI: 10.1109/TSC.2026.3685039

Mingshun Luo;Haolei He;Wenti Yang;Meng Li;Longfei Wu;Zijian Zhang;Zhitao Guan

{"title":"PrivTI: Efficient End-to-End Privacy-Preserving Inference for Transformer-Based Models in MLaaS","authors":"Mingshun Luo;Haolei He;Wenti Yang;Meng Li;Longfei Wu;Zijian Zhang;Zhitao Guan","doi":"10.1109/TSC.2026.3685039","DOIUrl":"10.1109/TSC.2026.3685039","url":null,"abstract":"Machine Learning as a Service (MLaaS) offers convenient access to high-quality transformer-based model inference without local deployment, but it requires uploading sensitive data to the server, raising significant privacy concerns. Recent advances in homomorphic encryption (HE) and secure multi-party computation (MPC) have shown great potential for enabling privacy-preserving inference. However, existing schemes face challenges with transformer-based models due to massive matrix multiplications and complex non-linear functions. To address these challenges, we propose PrivTI, an efficient end-to-end privacy-preserving inference framework for transformer-based models. Specifically, we design an HE-based single-instruction multiple-data (SIMD) encoding scheme to perform matrix multiplication efficiently. To handle non-linear functions, we develop MPC-friendly protocols for core functions like GELU, Softmax, and LayerNorm, and further extend support to SiLU and RMSNorm. Moreover, PrivTI supports conversions between HE and MPC, enabling end-to-end privacy-preserving inference. Experimental results demonstrate that PrivTI achieves speedups 1.9–45.2× in matrix multiplication and 1.1–18.9× in non-linear function evaluation compared to baseline schemes IRON, BOLT, and NEXUS under the Local Area Network (LAN) setting. Additionally, PrivTI reduces communication overhead by 1.1–4898.7×. Moreover, end-to-end benchmarks show 3.3–9.8× improvements in computation speed and 1.6–6.2× reductions in communication overhead.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3198-3211"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708692","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

When Blockchain Meets Tor: Enabling Automatically Distributed Blind Choosing of Relay Nodes in Anonymous Communication 当区块链遇到Tor时：实现匿名通信中继节点的自动分布式盲选择

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-07-15 DOI: 10.1109/TSC.2026.3713765

Jiahang Sun;Yan Wu;Zijian Zhang;Meng Li;Mengyu Wang;Yao Xiao;Liehuang Zhu;Chengxiang Si

{"title":"When Blockchain Meets Tor: Enabling Automatically Distributed Blind Choosing of Relay Nodes in Anonymous Communication","authors":"Jiahang Sun;Yan Wu;Zijian Zhang;Meng Li;Mengyu Wang;Yao Xiao;Liehuang Zhu;Chengxiang Si","doi":"10.1109/TSC.2026.3713765","DOIUrl":"10.1109/TSC.2026.3713765","url":null,"abstract":"Anonymous communication services represent a critical class of privacy-preserving infrastructure within the service computing paradigm, where trustworthiness, resilience, and quantifiable quality-of-service (QoS) are foundational requirements. Tor—the most widely deployed anonymous communication service—relies on a centralized Directory Authority (DA) component that functions as a monolithic trust anchor. This architectural choice introduces severe service-level vulnerabilities: single points of failure degrade availability (QoS), metadata exposure compromises privacy (a core service attribute), and centralized control enables censorship—directly violating service computing principles of decentralization, fault tolerance, and user-centric trust. We propose AuDi-BCoRN (Automatically Distributed BlindChoosing of Relay Nodes), a service-native re-architecture that transforms Tor’s directory layer into a decentralized, self-governing service ecosystem grounded in the tenets of service computing. By integrating blockchain as a decentralized trust service and using smart contracts for automated service coordination, AuDi-BCoRN eliminates centralized dependencies while closely matching native Tor performance: on a 3% tornettools-generated Tor topology, the empirical CDFs of TTFB, latency, and throughput are nearly indistinguishable from Tor beyond the median, while demonstrably strengthening service integrity against DoS, censorship, and traffic-analysis threats.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3661-3677"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148460815","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

FairWeight: Private Fairness-Aware Aggregation Using Model Interpretation Based Weightage in Federated Learning 公平权重：在联邦学习中使用基于模型解释的权重的私有公平感知聚合

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-04-22 DOI: 10.1109/TSC.2026.3686323

Harsh Kasyap;Ugur Ilker Atmaca;Carsten Maple;Nic Lane

{"title":"FairWeight: Private Fairness-Aware Aggregation Using Model Interpretation Based Weightage in Federated Learning","authors":"Harsh Kasyap;Ugur Ilker Atmaca;Carsten Maple;Nic Lane","doi":"10.1109/TSC.2026.3686323","DOIUrl":"10.1109/TSC.2026.3686323","url":null,"abstract":"Federated learning (FL) has been adopted as a promising solution for collaborative privacy-preserving machine learning. FL provides independence over the training data, but this can lead to statistical heterogeneity, which may result in issues such as bias and class imbalance. Thus, a global model, even with high accuracy, may be prone to discrimination and perform well only for certain sensitive attributes and classes. Global accuracy is not a perfect measure of the model’s effectiveness; it should be reported in terms of balanced accuracy and fairness. Existing solutions attempt to mitigate bias using local or global debiasing techniques. However, local debiasing results in poor performance with increasing heterogeneity, and global debiasing is either designed only for achieving fairness, ignoring balanced accuracy, or costly in terms of finding an optimal solution. We present a novel model interpretation based bias mitigation aggregation scheme in FL, called FairWeight. It calculates the aggregation weightage by considering both fairness and loss for biased samples from the minority class. After optimizing the combined loss for prediction and fairness, the important weights responsible for bias and misclassification are calculated locally between clients. These weight indices are shared using secure aggregation to calculate the final aggregation weightage. Further, a secure aggregation scheme is devised that penalises biased weights across clients, rather than punishing the global model. FairWeight is evaluated on five real-world datasets and compared against five state-of-the-art fairness-aware aggregation schemes. The results provide empirical evidence that FairWeight achieves both balanced accuracy and fairness, with reduced computation and communication overheads.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3283-3294"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147735581","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

Toward Robust and Fair Partial Label Federated Learning Service 面向鲁棒和公平的部分标签联邦学习服务

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-04-22 DOI: 10.1109/TSC.2026.3686458

Sen Fu;Wei Bao;Zhengjie Yang;Xinyi Sheng;Yichen Guo;Yuqin Liu;Bing Bing Zhou

{"title":"Toward Robust and Fair Partial Label Federated Learning Service","authors":"Sen Fu;Wei Bao;Zhengjie Yang;Xinyi Sheng;Yichen Guo;Yuqin Liu;Bing Bing Zhou","doi":"10.1109/TSC.2026.3686458","DOIUrl":"10.1109/TSC.2026.3686458","url":null,"abstract":"Machine learning services require accurately annotated datasets for AI training, which are often expensive and difficult to obtain. Weakly supervised learning, particularly Partial Label Learning (PLL), reduces costs by requiring only candidate label sets. However, most existing PLL works focus primarily on improving predictive performance, often neglecting potential security threats under such weakly supervised conditions. In this work, we study these threats within Federated Learning (FL), a distributed paradigm enabling privacy-preserving machine learning services. Specifically, we extend PLL to Partial Attribute Partial Label Learning (PAPL), where each sample is associated not only with partial labels but also with partial attributes, better reflecting real-world service environments involving uncertain or incomplete data collection. We then propose three innovative adversarial attacks that exploit attribute-label correlations and mixup strategies to compromise the fairness of models in FL services. To mitigate these threats, we propose FairFedPAPL, a gradient inversion-based defense mechanism that reconstructs representative pseudo-datasets to approximate each client’s local distribution, enabling accurate identification of adversarial participants. Our analytical and empirical results demonstrate that the proposed attacks significantly compromise model fairness, while our defense effectively identifies attackers, preserving robustness and fairness in weakly supervised FL.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3295-3307"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147735579","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

Privacy-Preserving and Verifiable Federated Learning Framework for Biometric Authentication at the Edge 边缘生物特征认证的隐私保护和可验证联邦学习框架

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-04-21 DOI: 10.1109/TSC.2026.3686112

Hao Zhou;Lin Li;Geng Yang;Hua Dai;Fusen Guo;Chao Chen

{"title":"Privacy-Preserving and Verifiable Federated Learning Framework for Biometric Authentication at the Edge","authors":"Hao Zhou;Lin Li;Geng Yang;Hua Dai;Fusen Guo;Chao Chen","doi":"10.1109/TSC.2026.3686112","DOIUrl":"10.1109/TSC.2026.3686112","url":null,"abstract":"Federated learning (FL) has emerged as a pivotal privacy-preserving paradigm for training models across decentralized biometric datasets. However, integrating secure biometric verification and data integrity in FL remains challenging, especially under non-IID distributions and resource constraints in edge environments. This paper introduces BPVFL, a novel Biometric Privacy-preserving and Verifiable Federated Learning framework, which synergizes differential privacy, cryptographic signatures, and photoplethysmogram (PPG)-based biometric recognition. Distinct from prior works, BPVFL supports both verifiable model updates and privacy-preserving identity authentication through a dual-layer protection scheme. We propose a customized privacy noise mechanism tailored for biometric features and a lightweight signature protocol to trace and validate local contributions. Experimental evaluations on three biometric datasets [SigD, BIDMC, and CapnoBase (TBME)] demonstrate that BPVFL achieves over 92.7% accuracy under strong privacy guarantees, while reducing communication costs via deep network modularization. To substantiate verifiability beyond provenance, we further integrate an optional Correctness Verification Layer (CVL) based on remote attestation, which makes accepted updates <italic>protocol-correctness verifiable (i.e., generated by an approved clipping/noising/training pipeline) at a small constant overhead. We provide an empirical comparison to representative verifiable FL designs along the axes of client cryptographic time, extra uplink, and end-to-end round latency, clarifying the engineering trade-offs for edge biometric services.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3212-3225"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147731793","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

Budget-Aware Cost-Efficient Edge AI Inference for Data Streams via Blockchain-Empowered Auctions 通过区块链授权拍卖对数据流进行预算敏感的成本效益边缘人工智能推断

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-07-08 DOI: 10.1109/TSC.2026.3711102

Yining Zhang;Lei Jiao;Konglin Zhu;Yebo Feng;Lin Zhang;Jiahua Xu

{"title":"Budget-Aware Cost-Efficient Edge AI Inference for Data Streams via Blockchain-Empowered Auctions","authors":"Yining Zhang;Lei Jiao;Konglin Zhu;Yebo Feng;Lin Zhang;Jiahua Xu","doi":"10.1109/TSC.2026.3711102","DOIUrl":"10.1109/TSC.2026.3711102","url":null,"abstract":"To achieve efficient edge AI inference, edge AI service providers often acquire pre-trained models from the model market. However, this process becomes complex due to unknown bid and inference quality distributions, the strategic behavior of self-interested providers, strict budgetary constraints, and the single-point-of-failure risks inherent in centralized platforms. To address all these challenges, we propose a blockchain-empowered model auction mechanism that facilitates secure and cost-efficient model procurement. Specifically, we design a novel budget-aware auction-assisted dual-learning algorithm framework, named <monospace>AucLearn</monospace>, which jointly optimizes model auction and blockchain control decisions. <monospace>AucLearn</monospace> leverages real-time performance feedback to minimize long-term total cost while strictly adhering to both financial and computational resource budgets. We further provide rigorous proofs of multiple performance guarantees for <monospace>AucLearn</monospace>, including sub-linear regret for both subproblems and the overall problem, sub-linear fit for long-term capacity constraints, as well as truthfulness and individual rationality in each auction. Extensive evaluations driven by real-world traces demonstrate that <monospace>AucLearn</monospace> exhibits significant advantages over multiple state-of-the-art solutions.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3387-3401"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414722","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

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations FIRE：边缘计算迁移的故障自适应强化学习框架

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2025-10-31 DOI: 10.1109/TSC.2025.3626791

Marie Siew;Shikhar Sharma;Zekai Li;Kun Guo;Chao Xu;Tania Lorido-Botran;Tony Q.S. Quek;Carlee Joe-Wong

{"title":"FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations","authors":"Marie Siew;Shikhar Sharma;Zekai Li;Kun Guo;Chao Xu;Tania Lorido-Botran;Tony Q.S. Quek;Carlee Joe-Wong","doi":"10.1109/TSC.2025.3626791","DOIUrl":"10.1109/TSC.2025.3626791","url":null,"abstract":"In edge computing, users’ service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE’s boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. We extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3479-3495"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412102","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

NET-Tracker: A Service Oriented IP State Traceback for Inter Autonomous Systems 一个面向服务的IP状态回溯自治系统

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-08-17 DOI: 10.1109/TSC.2026.3724298

Ning Lu;Congcong Su;Zenghui Liu;Mingxi Liu;Wenbo Shi;Ximeng Liu

{"title":"NET-Tracker: A Service Oriented IP State Traceback for Inter Autonomous Systems","authors":"Ning Lu;Congcong Su;Zenghui Liu;Mingxi Liu;Wenbo Shi;Ximeng Liu","doi":"10.1109/TSC.2026.3724298","DOIUrl":"10.1109/TSC.2026.3724298","url":null,"abstract":"IP state traceback is a necessary emergency incident response technology, which can help Internet Service Providers (ISPs) to capture the footprint of any traveled IP flow at the Autonomous System (AS) level and reconstruct inter-AS routing path to locate the source. To do this, it requires each AS to establish the routing state fingerprints for as many flows as possible. However, when taking the autonomy of AS, its limited flow processing capability and routing state privacy into consideration, existing schemes suffer the following drawbacks: lack of economic incentives for adoption, lower traceback accuracy and routing fingerprint leakage. For this, we propose NET-Tracker, a service oriented IP state traceback. In NET-Tracker, we adopt blockchain oracle technique to build an inter-AS traceback service trading platform, which can allows the requester to automatically and securely complete fingerprint transaction with participating ASes. And this well addresses the economic issue for its practical deployment. Moreover, with the restriction of fingerprint generation capacity on routers, we devise an AS-level fingerprint establishment optimization algorithm to maximize the fingerprints established in each AS, and thus improve the overall traceback accuracy. Furthermore, considering that the attackers may exploit the blockchain feature of open and transparent transaction to initiate variety of strategic attacks, we devise a privacy-preserving protocol for NET-Tracker. We have conducted the rigorous theoretical analysis and the extensive simulation experiments based on real-world topology to evaluate the performance. The results show that its traceback accuracy could be well anchored at levels close to 97%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3524-3539"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148755530","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

AirLoc: A QoS-Aware Mobile Location Service Framework via UAV-Assisted LoRa Networks AirLoc：基于无人机辅助LoRa网络的qos感知移动定位服务框架

IF 6.2 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2026-09-01 Epub Date: 2026-08-20 DOI: 10.1109/TSC.2026.3725677

Ciyuan Chen;Zhuqing Xu;Feng Shan;Runqun Xiong;Weizheng Wang;Zhimeng Yin;Ye Wei;Junzhou Luo;Xiaohua Jia

{"title":"AirLoc: A QoS-Aware Mobile Location Service Framework via UAV-Assisted LoRa Networks","authors":"Ciyuan Chen;Zhuqing Xu;Feng Shan;Runqun Xiong;Weizheng Wang;Zhimeng Yin;Ye Wei;Junzhou Luo;Xiaohua Jia","doi":"10.1109/TSC.2026.3725677","DOIUrl":"10.1109/TSC.2026.3725677","url":null,"abstract":"As the demand for Location-Based Services (LBS) grows in remote or resource-limited regions, providing high Quality of Service (QoS) location for wide mobile applications has become increasingly crucial. Existing approaches typically rely on fixed gateways and static nodes, which is unsuitable or expensive in demanding environments such as wild fields or temporary sites. In this paper, we propose AirLoc, a QoS-aware framework that ensures long-range, accurate, and low-cost location services for wide mobile scenarios. Considering that LoRa (Long Range) is suitable for localization in wide areas due to its long-range and low-power nature. AirLoc uses only one single legacy Uncrewed Aerial Vehicle (UAV)-assisted LoRa gateway to track mobile LoRa targets without relying on dense infrastructure. AirLoc is mainly based on the key observation that LoRa’s RSSI is insensitive at long ranges and unstable at short ranges. To address these issues, we propose three key service modules for AirLoc framework. We first propose a LoRa spreading factor-based long-range localization module to determine the coarse region of the targets. Additionally, we design an RSSI reconstruction–based module to achieve accurate tracking in short ranges. Finally, a QoS-driven interaction protocol module is used to operate the architecture with minimal overhead. We implement AirLoc on both a real-world testbed that includes a UAV-assisted LoRa gateway and a large-scale simulation platform. Extensive real-world experiments show that AirLoc accurately tracks mobile targets in a <inline-formula><tex-math>$550 {rm m} times 400 {rm m}$</tex-math></inline-formula> area at low power, making it a QoS-aware, modular service architecture that easily integrates into existing LBS systems.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 5","pages":"3347-3360"},"PeriodicalIF":6.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148768153","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