IEEE Transactions on Network and Service Management最新文献_第5页

Key Transferring-Based Secure Deduplication for Cloud Storage With Resistance Against Brute-Force Attacks

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-10-07 DOI: 10.1109/TNSM.2024.3474852

Xin Tang;Luchao Jin;Jing Bai;Linjie Shi;Yudan Zhu;Ting Cui

{"title":"Key Transferring-Based Secure Deduplication for Cloud Storage With Resistance Against Brute-Force Attacks","authors":"Xin Tang;Luchao Jin;Jing Bai;Linjie Shi;Yudan Zhu;Ting Cui","doi":"10.1109/TNSM.2024.3474852","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3474852","url":null,"abstract":"Convergent encryption is an effective technique to achieve cross-user deduplication of encrypted data in cloud storage. However, it is vulnerable to brute-force attacks for data with low min-entropy. Moreover, once the content of the target data is successfully constructed through the aforementioned attacks, the corresponding index can also be obtained, leading to the risk of violating privacy during the process of data downloading. To address these challenges, we propose a key transferring-based secure deduplication (KTSD) scheme for cloud storage with support for ownership verification, which significantly improves the security against brute-force attacks during the ciphertext deduplication and downloading. Specifically, we introduce a randomly generated key in data encryption and downloading index generation to prevent the results from being inferred. And define a deduplication request index and a key request index by using the bloom filter to achieve brute-force attack resistant key transferring. An RSA-based ownership verification scheme is designed for the downloading process to effectively prevent privacy leakage. Finally, we prove the security of our schemes by security analysis and perform the performance evaluation experiments, the results of which show that compared to the state-of-the art, the cloud storage overhead can be reduced by 6.01% to 20.49% under KTSD.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"860-876"},"PeriodicalIF":4.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619944","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

Metis: Selecting Diverse Atlas Vantage Points

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-10-01 DOI: 10.1109/TNSM.2024.3470989

Malte Tashiro;Emile Aben;Romain Fontugne

{"title":"Metis: Selecting Diverse Atlas Vantage Points","authors":"Malte Tashiro;Emile Aben;Romain Fontugne","doi":"10.1109/TNSM.2024.3470989","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3470989","url":null,"abstract":"The popularity of the RIPE Atlas measurement platform comes primarily from its openness and unprecedented scale. The platform provides users with over ten thousand vantage points, called probes, and is usually considered as giving a reasonably faithful view of the Internet. A good use of Atlas, however, requires a clear understanding of its limitations and bias. In this work we highlight the influence of probe locations on Atlas measurements and advocate the importance of selecting a diverse set of probes for fair measurements. We propose Metis, a data-driven probe selection method, that picks a diverse set of probes based on topological properties (e.g., round-trip time or AS-path length). Using real experiments we show that, compared to Atlas’ default probe selection, Metis’ probe selections collect more comprehensive measurement results in terms of geographical, topological, RIR, and industry-type coverage. Metis triples the number of probes from the underrepresented AFRINIC and LACNIC regions, and improves geographical diversity by increasing the number of unique countries included in the probe set by up to 59%. In addition, we extend Metis to identify locations on the Internet where new probes would be the most beneficial for improving Atlas’ footprint. Finally, we present a website where we publish periodically updated results and provide easy integration of Metis’ selections with Atlas.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"3-14"},"PeriodicalIF":4.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620025","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

HFL-TranWGAN: Knowledge-Driven Cross-Domain Collaborative Anomaly Detection for End-to-End Network Slicing

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-10-01 DOI: 10.1109/TNSM.2024.3471808

Yanfei Wu;Liang Liang;Yunjian Jia;Wanli Wen

{"title":"HFL-TranWGAN: Knowledge-Driven Cross-Domain Collaborative Anomaly Detection for End-to-End Network Slicing","authors":"Yanfei Wu;Liang Liang;Yunjian Jia;Wanli Wen","doi":"10.1109/TNSM.2024.3471808","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3471808","url":null,"abstract":"Network slicing is a key technology that can provide service assurance for the heterogeneous application scenarios emerging in the next-generation networks. However, the heterogeneity and complexity of virtualized end-to-end network slicing environments pose challenges for network security operations and management. In this paper, we propose a knowledge-driven cross-domain collaborative anomaly detection scheme for end-to-end network slicing, namely HFL-TranWGAN. Specifically, we first design a hierarchical management framework that performs three-tier hierarchical intelligent management of end-to-end network slices, while introducing a knowledge plane to assist the management plane in making intelligent decisions. Then, we develop a knowledge-driven sub-slice anomaly detection model, the conditional TranWGAN model, in which an encoder, a generator, and multiple discriminators perform adversarial learning simultaneously. Finally, taking the sub-slice anomaly detection model as the basic training model, we utilize hierarchical federated learning to achieve inter-slice and intra-slice collaborative anomaly detection. We calculate the anomaly scores through the discrimination error and reconstruction error to obtain the anomaly detection results. Simulation results on two real-world datasets show that the proposed HFL-TranWGAN scheme performs better in anomaly detection performance such as F1 score and precision compared to the benchmark methods. Specifically, HFL-TranWGAN improved precision by up to 8.53% and F1 score by up to 1.88% compared to benchmarks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"760-776"},"PeriodicalIF":4.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621976","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

QoE-Fairness-Aware Bandwidth Allocation Design for MEC-Assisted ABR Video Transmission

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-10-01 DOI: 10.1109/TNSM.2024.3471632

Ailing Xiao;Sheng Wu;Yongkang Ou;Ning Chen;Chunxiao Jiang;Wei Zhang

{"title":"QoE-Fairness-Aware Bandwidth Allocation Design for MEC-Assisted ABR Video Transmission","authors":"Ailing Xiao;Sheng Wu;Yongkang Ou;Ning Chen;Chunxiao Jiang;Wei Zhang","doi":"10.1109/TNSM.2024.3471632","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3471632","url":null,"abstract":"Adaptive bitrate (ABR) streaming provides an effective way to improve the Quality of Experience (QoE) of video users and is now the de facto standard for video delivery. Meanwhile, mobile edge computing (MEC) has been applied to assist ABR streaming, improving the performance of mobile networks and enabling efficient video delivery. However, smooth ABR streaming relies on the bidirectional adaptation between bitrate selection and bandwidth allocation, as they operate on distinct timescales and have different optimization goals. Moreover, since the constrained wireless resources available within a cell are shared by multiple users, their QoE should be optimized not only jointly but fairly. To this end, we propose a QoE-fairness-aware bandwidth allocation (QFA-BA) method for MEC-assisted ABR video transmission. With a novel perspective on buffer occupancy modeling, the relationship between bitrate selection and bandwidth allocation is studied. An enhanced QoE evaluation model is then proposed to correlate bitrate selection with bandwidth allocation and facilitate QFA-BA. Finally, a soft actor-critic (SAC) framework improving both the QoE and QoE-fairness is presented for QFA-BA. Compared with the state-of-the-art methods, our QFA-BA can perceive fine-grained buffer occupancy and stabilize it near a preset value with relatively more and larger bitrate switchings, exhibiting smoother convergence, better QoE (50.29%) and QoE fairness (54.81%).","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"499-515"},"PeriodicalIF":4.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621980","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

A Deep Learning Approach for Throughput Enhanced Clustering and Spectrally Efficient Resource Allocation in Ultra-Dense Networks

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-30 DOI: 10.1109/TNSM.2024.3470235

Saksham Katwal;Nidhi Sharma;Krishan Kumar

{"title":"A Deep Learning Approach for Throughput Enhanced Clustering and Spectrally Efficient Resource Allocation in Ultra-Dense Networks","authors":"Saksham Katwal;Nidhi Sharma;Krishan Kumar","doi":"10.1109/TNSM.2024.3470235","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3470235","url":null,"abstract":"The primary obstacle for the wireless industry is meeting the growing demand for cellular services, which necessitates the deployment of numerous femto base stations (FBSs) in ultra-dense networks. Effective resource distribution among densely and randomly distributed FBSs in ultra-dense is difficult, mainly because of intensified interference problems. The K-means clustering is improved by employing the Davies Bouldin index, which separates the clusters to prevent overlapping and mitigate interference. The elbow approach is utilized to determine the optimal number of clusters. Afterward, attention is directed toward addressing efficient resource allocation through a distributive methodology. The proposed approach makes use of a replay buffer-based multi-agent framework and uses the generative adversarial networks deep distributional Q-network (GAN-DDQN) to efficiently model and learn state-action value distributions for intelligent resource allocation. To further improve control over the training error, the distributions are estimated by approximating a whole quantile function. The numerical results validate the effectiveness of both the proposed clustering method and the GAN-DDQN-based resource allocation scheme in optimizing throughput, fairness, energy efficiency, and spectrum efficiency, all while maintaining the QoS for all users.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"582-591"},"PeriodicalIF":4.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621905","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

Online Offloading and Mobility Awareness of DAG Tasks for Vehicle Edge Computing

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-30 DOI: 10.1109/TNSM.2024.3470777

Xiao He;Shanchen Pang;Haiyuan Gui;Kuijie Zhang;Nuanlai Wang;Shihang Yu

{"title":"Online Offloading and Mobility Awareness of DAG Tasks for Vehicle Edge Computing","authors":"Xiao He;Shanchen Pang;Haiyuan Gui;Kuijie Zhang;Nuanlai Wang;Shihang Yu","doi":"10.1109/TNSM.2024.3470777","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3470777","url":null,"abstract":"Achieving real-time processing of tasks has become a crucial objective in the Internet of Vehicles (IoV) field. During the online generation of tasks in IoV systems, many dependency tasks arrive randomly within continuous time frames, and it is impossible to predict the number of arriving tasks and the dependencies between sub-tasks. Offloading dependent tasks, which are quantity-intensive and have complex dependencies, to appropriate vehicle edge servers (VESs) for online processing of large-scale tasks remains a challenge. Firstly, we innovatively propose a VES task parallel processing framework incorporating a multi-level feedback queue to enhance the cross-slot parallel processing capabilities of the IoV system. Secondly, to reduce the complexity of problem-solving, we employ the Lyapunov optimization method to decouple the online task offloading control problem into single-stage mixed-integer nonlinear programming problem. Finally, we design an online task decision-making algorithm based on multi-agent reinforcement learning to achieve real-time task offloading decisions in complex dynamic IoV environments. To validate our algorithm’s superiority in dynamic IoV systems, we compare it with other online task offloading decision-making algorithms. Simulation results show that ours significantly reduces the all-task processing latency of IoV system by 15% compared to the comparison algorithms, and the task average latency time is reduced by 14%.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"675-690"},"PeriodicalIF":4.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621749","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

Deep Reinforcement Learning-Based Dual-Timescale Service Caching and Computation Offloading for Multi-UAV Assisted MEC Systems

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-30 DOI: 10.1109/TNSM.2024.3468312

Na Lin;Xiao Han;Ammar Hawbani;Yunhe Sun;Yunchong Guan;Liang Zhao

{"title":"Deep Reinforcement Learning-Based Dual-Timescale Service Caching and Computation Offloading for Multi-UAV Assisted MEC Systems","authors":"Na Lin;Xiao Han;Ammar Hawbani;Yunhe Sun;Yunchong Guan;Liang Zhao","doi":"10.1109/TNSM.2024.3468312","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3468312","url":null,"abstract":"The emergence of unmanned aerial vehicles (UAVs) ushers in a new era for mobile edge computing (MEC), significantly expanding its range of service and potential applications. Due to the limited storage capacity and energy budget of UAVs, it is crucial to determine a reasonable service caching and task offloading strategy. Service caching means that task-related programs and the associated databases are cached on edge servers. In this paper, we consider the time latency and energy consumption caused by frequent changes to the service caching, aiming to jointly optimize the computational offloading, resource allocation, and service caching in multi-UAV assisted MEC systems at different time scales. The objective of this optimization is to reduce the overall system delay while staying within the energy limitations of both the UAVs and ground devices. An improved service caching policy (SCP) is proposed, which is based on task popularity and utilizes the greedy dual size frequency (GDSF) algorithm. The SCP is combined with the twin delayed deep deterministic policy gradient (TD3) algorithm to propose an innovative dual timescale TD3 (DTTD3) algorithm. The numerical outcomes obtained from a substantial number of simulation experiments demonstrate that DTTD3 outperforms existing benchmark methods in terms of convergence and parameter optimization.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"605-617"},"PeriodicalIF":4.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621979","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

An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-30 DOI: 10.1109/TNSM.2024.3470853

Jatinder Kumar;Deepika Saxena;Jitendra Kumar;Ashutosh Kumar Singh;Athanasios V. Vasilakos

{"title":"An Adaptive Evolutionary Neural Network Model for Load Management in Smart Grid Environment","authors":"Jatinder Kumar;Deepika Saxena;Jitendra Kumar;Ashutosh Kumar Singh;Athanasios V. Vasilakos","doi":"10.1109/TNSM.2024.3470853","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3470853","url":null,"abstract":"To empower the management of smart meters’ demand load within a smart grid environment, this paper presents a Feed-forward Neural Network with ADaptive Evolutionary Learning Approach (ADELA). In this model, the load forecasting information is propagated via neurons of input and multiple hidden layers and the final estimated output is achieved with the help of the sigmoid activation function. An improved evolutionary algorithm is proposed for training and adjusting the interconnecting weights among the layers of the intended neural network. This model is capable of addressing the critical challenges of high volatility, uncertainty, missing smart meters data, and sudden upsurge and plunge in electricity demand. The proposed algorithm is able to learn the best suitable evolutionary operators from a given pool of operators and the probabilities associated with them. The proposed load forecasting approach is simulated over three real-world smart meter datasets, including the Australian Smart Grid Smart City project, the Irish Commission for Energy Regulation, and UMass Smart. The performance evaluation and comparison of the proposed approach with the existing state-of-the-art approaches revealed a relative improvement of up to 46.93%, 5.05%, and 2.20% in forecast accuracy over the Smart Grid Smart City, UMass Smart and the Irish Commission for Energy Regulation datasets, respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"242-254"},"PeriodicalIF":4.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621981","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

Evaluation and Optimization of Backbone Network Reliability Problems Using Decision Diagram Methods

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-30 DOI: 10.1109/TNSM.2024.3470076

Yingjun Ye;Ke Ruan;Weihao Yu

{"title":"Evaluation and Optimization of Backbone Network Reliability Problems Using Decision Diagram Methods","authors":"Yingjun Ye;Ke Ruan;Weihao Yu","doi":"10.1109/TNSM.2024.3470076","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3470076","url":null,"abstract":"The structure of the backbone network is complex, and the characteristics of multi-layer architecture and non-independent IP layer links lead to a lack of suitable reliability assessment models and methods to evaluate the reliability of the backbone network. To this end, this paper uses decision diagram methods to model the dependency relationship between IP layer links and optical layer components, relaxing the assumption of independent network link failures. The decision diagram can logically combine features, and while retaining the original connectivity reliability and capacity reliability solution methods, it supplements the dependency relationship and inter-layer relationship of the network with subgraph merging operations. In addition, the issue of capacity reliability or business reliability for multi-terminals and all-terminals has not yet yielded a suitable solution. This paper uses the directed acyclic graph feature of the decision diagram to design a state expansion algorithm, which can be used to solve the multi-terminal capacity availability of multi-state networks. Finally, based on the easy-to-parallel characteristics of the decision diagram, parallel methods are designed to parallelize the entire process of network reliability evaluation, which can alleviate the problem of state space explosion.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"344-360"},"PeriodicalIF":4.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620026","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

A Novel Adaptive Device-Free Passive Indoor Fingerprinting Localization Under Dynamic Environment 动态环境下一种新的自适应无源室内指纹定位方法

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-27 DOI: 10.1109/TNSM.2024.3469374

Xinping Rao;Le Qin;Yugen Yi;Jin Liu;Gang Lei;Yuanlong Cao

{"title":"A Novel Adaptive Device-Free Passive Indoor Fingerprinting Localization Under Dynamic Environment","authors":"Xinping Rao;Le Qin;Yugen Yi;Jin Liu;Gang Lei;Yuanlong Cao","doi":"10.1109/TNSM.2024.3469374","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3469374","url":null,"abstract":"In recent years, indoor localization has attracted a lot of interest and has become one of the key topics of Internet of Things (IoT) research, presenting a wide range of application scenarios. With the advantages of ubiquitous universal Wi-Fi platforms and the “unconscious collaborative sensing” in the monitored target, Channel State Information (CSI)-based device-free passive indoor fingerprinting localization has become a popular research topic. However, most existing studies have encountered the difficult issues of high deployment labor costs and degradation of localization accuracy due to fingerprint variations in real-world dynamic environments. In this paper, we propose BSWCLoc, a device-free passive fingerprint localization scheme based on the beyond-sharing-weights approach. BSWCLoc uses the calibrated CSI phases, which are more sensitive to the target location, as localization features and performs feature processing from a two-dimensional perspective to ultimately obtain rich fingerprint information. This allows BSWLoc to achieve satisfactory accuracy with only one communication link, significantly reducing deployment consumption. In addition, a beyond-sharing-weights (BSW) method for domain adaptation is developed in BSWCLoc to address the problem of changing CSI in dynamic environments, which results in reduced localization performance. The BSW method proposes a dual-flow structure, where one flow runs in the source domain and the other in the target domain, with correlated but not shared weights in the adaptation layer. BSWCLoc greatly exceeds the state-of-the-art in terms of positioning accuracy and robustness, according to an extensive study in the dynamic indoor environment over 6 days.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6140-6152"},"PeriodicalIF":4.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880295","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