IEEE Transactions on Network and Service Management最新文献_第7页

LLM-Powered Intent-Driven Configuration Generation for Multi-Vendor Networks 基于llm的多厂商网络意图驱动配置生成

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI: 10.1109/TNSM.2026.3675409

Jingyu Wang;Bo He;Jinyu Zhao;Yixin Xuan;Haifeng Sun;Qi Qi;Junzhe Liang;Zirui Zhuang;Jianxin Liao

{"title":"LLM-Powered Intent-Driven Configuration Generation for Multi-Vendor Networks","authors":"Jingyu Wang;Bo He;Jinyu Zhao;Yixin Xuan;Haifeng Sun;Qi Qi;Junzhe Liang;Zirui Zhuang;Jianxin Liao","doi":"10.1109/TNSM.2026.3675409","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3675409","url":null,"abstract":"Network configuration management has become increasingly complex, inefficient, and prone to errors due to frequent updates in command structures and the prevalence of multi-vendor network infrastructures. To tackle these challenges, this paper introduces a novel cognitive communication approach, formulating a new task called intent-driven multi-vendor network configuration generation. Within the broader intent-based networking lifecycle, this task specifically targets the realization and command generation stage—translating natural language operational intents into accurate and syntactically valid network commands compatible with multiple vendors, rather than addressing high-level intent interpretation or decomposition. Three primary challenges are addressed: syntactical command validity, vendor-specific syntax diversity, and outdated or inconsistent network knowledge. We propose ConfGen, a cognitive and intent-driven multi-vendor configuration generation framework that consists of two phases: vendor-agnostic syntax retrieval and syntax-constrained command generation. In the first phase, a cognitive retrieval mechanism and reranking strategy identify the most relevant syntax structures based on user intents, while vendor-specific syntax components are effectively generalized. The second phase employs a Large Language Model (LLM) guided by retrieved syntax constraints and user intents to generate precise and valid network commands. To ensure syntactical correctness and vendor compatibility, syntax-constrained decoding strategies are integrated into the LLM generation process. Extensive experimental evaluations conducted on a novel dataset containing network commands from Huawei, Cisco, Nokia, and Juniper demonstrate the superiority of ConfGen. Results confirm significant performance improvements over state-of-the-art solutions in generating accurate, multi-vendor-compatible network configurations driven by user intent.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3537-3555"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557952","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 Decentralized Online Federated Learning for Smart Healthcare Service Systems 智能医疗保健服务系统的隐私分散在线联邦学习

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-04-02 DOI: 10.1109/TNSM.2026.3680310

Luyao Jiang;Xinguo Ming;Mengli Wei

{"title":"Privacy Decentralized Online Federated Learning for Smart Healthcare Service Systems","authors":"Luyao Jiang;Xinguo Ming;Mengli Wei","doi":"10.1109/TNSM.2026.3680310","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3680310","url":null,"abstract":"The Smart Healthcare Service Systems (SHSS) aim to integrate decentralized healthcare institutions, intelligent technologies, and end users into a cyber-physical system that enables high-quality medical decision-making. However, the sensitive nature of healthcare data presents significant privacy and security challenges, which hinder effective collaboration among healthcare providers. Moreover, existing research lacks a comprehensive theoretical framework that spans the full pipeline from data acquisition to intelligent decision services. To address these challenges, we propose a theoretical framework for SHSS that systematically analyzes data processing and user demand to establish the goals of secure, stable, and adaptive collaborative learning. Guided by these goals, a Decentralized Online Federated Learning (DOFL) network model is tailored for SHSS, where participating institutions interact through a decentralized federated learning structure. Building on this model, we design DP-DOOR (Differentially Private Decentralized Online Federated Learning with One-Point Residual Feedback), a fully decentralized algorithm that supports row-stochastic communication topologies, accommodating practical limitations where bidirectional synchronization is often infeasible. DP-DOOR ensures data privacy through differential privacy (DP) mechanisms and achieves efficient gradient estimation using a one-point residual feedback (OPRF) approach. Theoretical analysis shows that DP-DOOR provides <inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>-DP guarantees and achieves sub-linear regret. Experimental evaluations on diverse real-world medical datasets under both IID and non-IID settings demonstrate the algorithm’s robustness and effectiveness in enabling secure, decentralized collaboration and enhancing adaptability in dynamic healthcare environments.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3881-3895"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Optimization of Routing and Scheduling in Cross-Domain Deterministic Networks 跨域确定性网络中路由与调度的联合优化

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-04-01 DOI: 10.1109/TNSM.2026.3679810

Xiaolong Wang;Haipeng Yao;Lin Zhu;Wenji He;Wei Zhang;Mohsen Guizani

{"title":"Joint Optimization of Routing and Scheduling in Cross-Domain Deterministic Networks","authors":"Xiaolong Wang;Haipeng Yao;Lin Zhu;Wenji He;Wei Zhang;Mohsen Guizani","doi":"10.1109/TNSM.2026.3679810","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3679810","url":null,"abstract":"Industrial Internet applications require networks to guarantee deterministic end-to-end latency and zero packet loss at both the data link and network layers. Traditional best-effort communication models in consumer networks are insufficient to meet these stringent demands. To meet these stringent demands, the IEEE 802.1 standards introduce Time-Sensitive Networking (TSN) at the data link layer, while the IETF proposes Deterministic Networking (DetNet) for the network layer. However, enabling seamless cross-domain communication between TSN and DetNet remains a significant challenge. This paper proposes a unified cross-domain network architecture and a time-slot alignment strategy that compensates for synchronization errors between the TSN and DetNet layers. We further develop a Joint Routing and Scheduling algorithm for Deterministic Cross-Domain Transmission (JRS-DCT), which simultaneously addresses routing and scheduling under cross-domain constraints. The algorithm leverages Cycle-Specified Queuing and Forwarding (CSQF) in DetNet and Cycle Queuing and Forwarding (CQF) in TSN to ensure bounded latency and deterministic transmission. Extensive simulations demonstrate that the proposed JRS-DCT algorithm significantly improves the scheduling success rate and effectively reduces network resource utilization compared to two baseline algorithms. These results validate the effectiveness and robustness of the proposed framework in supporting time-sensitive communication across heterogeneous network environments.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3921-3933"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696722","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

FoV Prediction-Based Adaptive Streaming Mechanism for 6DoF Volumetric MR Applications in Multi-Base-Station Networks 基于FoV预测的多基站6DoF体积MR自适应流机制

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-04-20 DOI: 10.1109/TNSM.2026.3685670

Li-Chin Siang;Wen-Hsing Kuo;Pei-Chieh Lin;Chih-Wei Huang;De-Nian Yang

{"title":"FoV Prediction-Based Adaptive Streaming Mechanism for 6DoF Volumetric MR Applications in Multi-Base-Station Networks","authors":"Li-Chin Siang;Wen-Hsing Kuo;Pei-Chieh Lin;Chih-Wei Huang;De-Nian Yang","doi":"10.1109/TNSM.2026.3685670","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3685670","url":null,"abstract":"The emergence of mixed reality (MR) as a significant application in mobile networks has garnered significant attention. Wireless headsets enable unrestricted user movement within femtocell networks comprising numerous small base stations, offering a promising solution for MR applications. However, the complexity of these systems poses challenges in optimizing resource allocation across base stations. This paper proposes a novel resource allocation method for volumetric MR streaming in multi-base-station environments. The method consists of two phases. Firstly, the method uses neural networks to model and forecast users’ viewing directions. Leveraging these predictions, their confidence levels, and layer characteristics, the algorithm adjusts video quality for each user and allocates transmission resources across base stations to optimize overall performance. Through comprehensive analysis, we prove that this novel problem is NP-hard and show that our approach achieves a performance within a bounded gap from the optimal solution. Simulation results reveal that our proposed algorithm outperforms existing techniques, enhancing aggregate performance across diverse scenarios.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"4228-4243"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796037","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

Bandwidth-Delay Optimal Segment Routing: Upper-Bound and Lower-Bound Algorithms 带宽延迟最优分段路由：上界和下界算法

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-03-26 DOI: 10.1109/TNSM.2026.3678190

Jianwei Zhang;Bowen Cui

引用次数: 0

Lightweight Learning for Symbiotic Secure and Efficient ISAC in RIS-Assisted Intelligent Transportation Networks ris辅助智能交通网络中共生安全高效ISAC的轻量级学习

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-03-31 DOI: 10.1109/TNSM.2026.3679370

Yifei Xie;Zhi Lin;Kefeng Guo;Ruiqian Ma;Hussam Al Hamadi;Fatima Asiri;Ahlam Almusharraf

{"title":"Lightweight Learning for Symbiotic Secure and Efficient ISAC in RIS-Assisted Intelligent Transportation Networks","authors":"Yifei Xie;Zhi Lin;Kefeng Guo;Ruiqian Ma;Hussam Al Hamadi;Fatima Asiri;Ahlam Almusharraf","doi":"10.1109/TNSM.2026.3679370","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3679370","url":null,"abstract":"Achieving real-time processing in integrated sensing and communication (ISAC) systems presents significant challenges due to the high computational burden of conventional optimization methods, particularly within intelligent transportation networks (ITN). This paper addresses these challenges by proposing lightweight supervised and unsupervised deep learning (DL) algorithms, respectively for quasi-static and dynamic environments, aiming to improve the secrecy energy efficiency (SEE) of ITN under the constraints of the Cramér-Rao bound (CRB) for direction-of-arrival (DOA) estimation and the transmission rate of each user. By jointly optimizing power allocation and reconfigurable intelligent surface (RIS) phase shifts, the framework ensures robust physical layer security (PLS) alongside communication efficiency, aligning with defense-in-depth strategies for securing next-generation ITN. For quasi-static environments, a supervised deep neural network (DNN) algorithm leverages offline codebook-generated labels to achieve near-optimal channel state information (CSI) mapping, explicitly minimizing signal leakage to eavesdroppers. In dynamic scenarios, an unsupervised channel attention mechanism-based residual network (CAM-ResNet) eliminates labeling overhead through direct physics-informed SEE optimization with adaptive constraint enforcement, enabling real-time adaptation to rapidly varying channels and evolving security threats. Simulation results demonstrate that both algorithms achieve comparable SEE performance with the zero-forcing (ZF) method, while significantly reducing computational complexity, with the CAM-ResNet demonstrating superior resilience to dynamic security threats. This work contributes to advancing secure and efficient ISAC solutions, reinforcing multi-layered defense mechanisms critical for future ITN.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3896-3908"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665252","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

GAN-Empowered Parasitic Covert Communication: Data Privacy in Next-Generation Networks gan授权的寄生隐蔽通信：下一代网络中的数据隐私

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-02-20 DOI: 10.1109/TNSM.2026.3666669

Pengcheng Guo;Zhi Lin;Haotong Cao;Yifu Sun;Kuljeet Kaur;Sherif Moussa

{"title":"GAN-Empowered Parasitic Covert Communication: Data Privacy in Next-Generation Networks","authors":"Pengcheng Guo;Zhi Lin;Haotong Cao;Yifu Sun;Kuljeet Kaur;Sherif Moussa","doi":"10.1109/TNSM.2026.3666669","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3666669","url":null,"abstract":"The widespread integration of artificial intelligence (AI) in next-generation communication networks poses a serious threat to data privacy while achieving advanced signal processing. Eavesdroppers can use AI-based analysis to detect and reconstruct transmitted signals, leading to serious leakage of confidential information. In order to protect data privacy at the physical layer, we redefine covert communication as an active data protection mechanism. We propose a new parasitic covert communication framework in which communication signals are embedded into dynamically generated interference by generative adversarial networks (GANs). This method is implemented by our CDGUBSS (complex double generator unsupervised blind source separation) system. The system is explicitly designed to prevent unauthorized AI-based strategies from analyzing and compromising signals. For the intended recipient, the pre-trained generator acts as a trusted key and can perfectly recover the original data. Extensive experiments have shown that our framework achieves powerful covert communication, and more importantly, it provides strong defense against data reconstruction attacks, ensuring excellent data privacy in next-generation wireless systems.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3365-3379"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557659","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

6Global: Dynamic IPv6 Active Address Scanning Assisted by Global Perspective 6Global：全局视角辅助下的动态IPv6主动地址扫描

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-03-16 DOI: 10.1109/TNSM.2026.3674490

Junqing Wang;Lejun Zhang;Zhihong Tian;Kejia Zhang;Shen Su;Jing Qiu;Yanbin Sun;Ran Guo

{"title":"6Global: Dynamic IPv6 Active Address Scanning Assisted by Global Perspective","authors":"Junqing Wang;Lejun Zhang;Zhihong Tian;Kejia Zhang;Shen Su;Jing Qiu;Yanbin Sun;Ran Guo","doi":"10.1109/TNSM.2026.3674490","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3674490","url":null,"abstract":"Network scanning is crucial for both network management and cybersecurity. However, due to the vast address space of IPv6, brute-force scanning is infeasible. Seed-based target generation algorithms have recently attracted considerable research attention. However, existing target generation algorithms lack a deeper exploration of patterns, leading to poor capture of dense regions and consequently low hitrate. To address this issue, we propose 6Global, a dynamic IPv6 active address scanning method assisted by global perspective. 6Global first performs rapid clustering of seed addresses based on their descriptive attributes. Then, for each cluster, patterns are generated in a bottom-up manner based on entropy, using subranges to represent patterns and resulting in denser patterns. Finally, dynamic scanning is conducted using these patterns. During scanning, the reward of each pattern is dynamically adjusted based on its active density and global statistics, which enhances the capability in capturing dense regions. Experimental results on six seed datasets show that 6Global overall outperforms seven baseline methods and demonstrates significant advantages across multiple datasets.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3406-3418"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557950","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

Dual-Timescale Joint Optimization for Dynamic Edge Service Deployment and Task Scheduling in Space–Air–Ground Integrated Networks 天空地一体化网络中动态边缘业务部署与任务调度的双时间尺度联合优化

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-04-20 DOI: 10.1109/TNSM.2026.3685292

Shuai Liu;Xiangxu Meng;Yun Zhong;Wenfeng Li;Kanglian Zhao

{"title":"Dual-Timescale Joint Optimization for Dynamic Edge Service Deployment and Task Scheduling in Space–Air–Ground Integrated Networks","authors":"Shuai Liu;Xiangxu Meng;Yun Zhong;Wenfeng Li;Kanglian Zhao","doi":"10.1109/TNSM.2026.3685292","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3685292","url":null,"abstract":"This paper investigates dual-timescale joint optimization for dynamic edge service deployment and task scheduling in space-air-ground integrated networks (SAGINs) with mobile edge computing (MEC). We decompose the original problem into a long-term (LT) service deployment subproblem and a short-term (ST) task scheduling subproblem to address the timescale disparity and coupling constraints. For the LT subproblem, an improved whale optimization algorithm (IWOA) is developed with a cosine-based nonlinear convergence factor, vertical-horizontal crossover, and elite opposition-based learning to enhance exploration and convergence. For the ST subproblem, we model task scheduling as a decentralized partially observable Markov decision process (Dec-POMDP) and adopt multi-agent proximal policy optimization (MAPPO) with KL regularization and clipping for stable policy updates. A hierarchical alternating framework (with a self-attention module for traffic-feature extraction) is designed to coordinate the two timescales iteratively. Simulation results demonstrate that, compared with the baseline WOA-PPO algorithm, the proposed method increases service deployment benefits by 27.2% and reduces task scheduling costs by 38.5%.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"4146-4163"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147736993","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

Communication-Efficient Client Selection for Federated Learning With Unknown Channel State 信道状态未知的联邦学习的高效通信客户端选择

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2026-01-01 Epub Date: 2026-04-08 DOI: 10.1109/TNSM.2026.3682080

Jun Xu;Dejun Yang;Abdulelah Talea

{"title":"Communication-Efficient Client Selection for Federated Learning With Unknown Channel State","authors":"Jun Xu;Dejun Yang;Abdulelah Talea","doi":"10.1109/TNSM.2026.3682080","DOIUrl":"https://doi.org/10.1109/TNSM.2026.3682080","url":null,"abstract":"Federated learning (FL) utilizes distributed edge devices for training based on local datasets which preserves data privacy at the cost of frequent communications of model parameters. The channel state between clients and the aggregator affects the successful delivery of model parameters. A client under poor channel state may fail to deliver its local model parameters and thus results in energy waste. Besides, obtaining the channel state takes extra overhead, which may degrade communication efficiency. It motivates us to investigate the client selection problem for FL with unknown channel state. We first derive an upper bound of the convergence for FL, which reflects the effects of the channel state and client selection decisions. We then formulate a client selection problem considering both the convergence and energy consumption. To solve this problem, we further transform it into a restless multi-armed bandit (RMAB) problem. We prove its indexability and propose an index-based client selection algorithm, termed <inline-formula> <tex-math>$textsf {IDXSel}$ </tex-math></inline-formula>, which has low time complexity, is easy to implement, and is proved to be asymptotically optimal. We compare our IDXSel algorithm with the FedAvg, TransP, IS, FedNorm, UCB-based, and <inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>-greedy-based algorithms on the MNIST and CIFAR-10 datasets. Results show that our algorithm achieves comparable or higher accuracy than the baselines, but wastes more than <inline-formula> <tex-math>$5times $ </tex-math></inline-formula> less energy than the worst of the baselines among all the evaluated scenarios.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"23 ","pages":"3948-3963"},"PeriodicalIF":5.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696576","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