Computer Networks最新文献_第2页

Semantic Edge Computing and Semantic Communications in 6G networks: A unifying survey and research challenges 6G网络中的语义边缘计算和语义通信：一个统一的调查和研究挑战

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-18 DOI: 10.1016/j.comnet.2025.111531

Milin Zhang , Mohammad Abdi , Venkat R. Dasari , Francesco Restuccia

引用次数: 0

MGGPT: A Multi-Graph GPT-enhanced framework for dynamic fraud detection in cryptocurrency networks MGGPT：用于加密货币网络中动态欺诈检测的多图gpt增强框架

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-18 DOI: 10.1016/j.comnet.2025.111508

Ansu Badjie , Grace Mupoyi Ntuala , Qi Xia , Jianbin Gao , Hu Xia

{"title":"MGGPT: A Multi-Graph GPT-enhanced framework for dynamic fraud detection in cryptocurrency networks","authors":"Ansu Badjie , Grace Mupoyi Ntuala , Qi Xia , Jianbin Gao , Hu Xia","doi":"10.1016/j.comnet.2025.111508","DOIUrl":"10.1016/j.comnet.2025.111508","url":null,"abstract":"<div><div>The rapid increase in cryptocurrency transactions has increased demand for advanced fraud detection systems. Conventional methods are often rigid and do not effectively capture cryptocurrency networks’ intricate temporal and structural patterns, while existing dynamic approaches struggle with incomplete or missing information. To tackle this issue, we present MGGPT, a new hybrid framework that integrates Graph Attention Neural Networks (GAT) with GPT-based transformers to improve fraud detection within cryptocurrency transaction networks. Our approach utilizes temporal graph structures through reachability networks (reach-nets) to derive essential node features, while also directly integrating edge labels into the embedding vectors, and introduces an innovative mechanism for predicting missing information to address the challenges posed by incomplete data in blockchain networks. The model features a dual-perspective learning strategy, employing local graph structures via GAT Networks and global contextual patterns through GPT-based sequence modeling to capture both structural and temporal dynamics in transaction networks. Our MGGPT framework implements a sophisticated edge classification mechanism using Support Vector Machines (SVM) for the final prediction. Experimental findings on actual cryptocurrency transaction datasets indicate superior efficacy in identifying fraudulent patterns, achieving notable improvements of 8.5% AUC, a 10.2% increase in Precision, 29.5% increment in recall, and 20.5% improvement in F1-score. Compared to baseline models such as STA-GT and CTGN, the proposed MGGPT improves the representation of dynamic relationships and faster convergence. Overall, the analysis reveals that our framework is not only more accurate but also more robust and scalable for real-world temporal graph applications. Ultimately, we assessed the robustness of our framework against adversarial attacks to show its practical applications in blockchains.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111508"},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663183","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

Learning to shape beams: Using a neural network to control a beamforming antenna 学习塑造波束：使用神经网络来控制波束形成天线

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-18 DOI: 10.1016/j.comnet.2025.111544

Jose David Fernández-Rodríguez , Iván García-Aguilar , Rafael Marcos Luque-Baena , Ezequiel López-Rubio , Marcos Baena-Molina , Juan Francisco Valenzuela-Valdés

{"title":"Learning to shape beams: Using a neural network to control a beamforming antenna","authors":"Jose David Fernández-Rodríguez , Iván García-Aguilar , Rafael Marcos Luque-Baena , Ezequiel López-Rubio , Marcos Baena-Molina , Juan Francisco Valenzuela-Valdés","doi":"10.1016/j.comnet.2025.111544","DOIUrl":"10.1016/j.comnet.2025.111544","url":null,"abstract":"<div><div>The field of reconfigurable intelligent surfaces (RIS) has gained significant traction in recent years in the wireless communications domain, owing to the ability to dynamically reconfigure surfaces to change their electromagnetic radiance patterns in real-time. In this work, we propose utilizing a novel deep learning model that innovatively employs only the parameters of each signal or beam as input, eliminating the need for the entire one-dimensional signal or its diffusion map (two-dimensional information). This approach enhances efficiency and reduces the overall complexity of the model, drastically reducing network size and enabling its implementation on low-cost devices. Furthermore, to enhance training effectiveness, the learning model attempts to estimate the discrete cosine transform applied to the output matrix rather than the raw matrix, significantly improving the achieved accuracy. This scheme is validated on a 1-bit programmable metasurface of size 10<span><math><mo>×</mo></math></span>10, achieving an accuracy close to 95% using a K-fold methodology with K=10.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111544"},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680517","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

Enhanced RIS-assisted vehicular network with TDMA and Bayesian Compressive Sensing-based channel estimation 基于TDMA和贝叶斯压缩感知信道估计的增强ris辅助车辆网络

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-17 DOI: 10.1016/j.comnet.2025.111525

Xiaoliang Zhang , Miao Wang , Mengxiong Wang , Liqiang Wang , Hong Zhang

{"title":"Enhanced RIS-assisted vehicular network with TDMA and Bayesian Compressive Sensing-based channel estimation","authors":"Xiaoliang Zhang , Miao Wang , Mengxiong Wang , Liqiang Wang , Hong Zhang","doi":"10.1016/j.comnet.2025.111525","DOIUrl":"10.1016/j.comnet.2025.111525","url":null,"abstract":"<div><div>In recent years, vehicular communication networks have become increasingly critical for intelligent transportation systems and autonomous driving applications. However, traditional vehicular networks face significant challenges in achieving reliable high-throughput communication, particularly for vehicles at the network edge or in non-line-of-sight scenarios. While Reconfigurable Intelligent Surface (RIS) technology offers promising solutions through programmable signal reflections, the joint optimization of RIS configuration and resource allocation in dynamic vehicular environments remains a complex and open challenge. In this paper, we propose an RIS-assisted uplink multi-input single-output (MISO) vehicular network communication system, where vehicle sensors transmit the collected data to roadside units (RSUs) in their specific time slots. To enhance transmission efficiency and reliability, we employ an adaptive Time Division Multiple Access (TDMA) scheme, which assigns dedicated time slots to each vehicle, thereby avoiding signal collisions and improving spectrum utilization. Furthermore, to address the channel estimation challenge in mobile scenarios, we develop a practical and efficient channel estimation framework based on Bayesian Compressive Sensing (BCS). Specifically, to leverage the inherent sparsity in the channel structure, our approach minimizes pilot overhead while enabling accurate and efficient recovery of the channel state information (CSI) in both direct and RIS-assisted paths under Rician fading conditions. To maximize the system throughput through the joint optimization of RIS phase shifts, power allocation, and time slots, we utilize the Block Coordinate Descent (BCD) algorithm to solve this non-convex optimization problem. The numerical results demonstrate that the proposed BCS-based method significantly enhances channel estimation accuracy and system throughput compared to other state-of-the-art approaches.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111525"},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing UAV scheduling and trajectory planning: An online auction framework 优化无人机调度和轨迹规划：一个在线拍卖框架

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-17 DOI: 10.1016/j.comnet.2025.111528

Kaiwei Mo , Xianglong Li , Zongpeng Li , Hong Xu

{"title":"Optimizing UAV scheduling and trajectory planning: An online auction framework","authors":"Kaiwei Mo , Xianglong Li , Zongpeng Li , Hong Xu","doi":"10.1016/j.comnet.2025.111528","DOIUrl":"10.1016/j.comnet.2025.111528","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) are envisioned to be a critical form of network service provisioning, when the ground infrastructure is vulnerable to disruptions from conflicts and natural disasters. Existing methodologies often fall short in fully optimizing UAV scheduling and resource allocation, leading to suboptimal service performance. This work aims to enhance social welfare through refining UAV scheduling and trajectory planning processes. To address this complex challenge, we first formulate social welfare maximization into a non-traditional integer linear program, and subsequently transform it into its exponential and dual forms. We propose a bifurcated framework called Online Scheduling and Trajectory (OST) which comprises two algorithms: The <span><math><msub><mrow><mi>A</mi></mrow><mrow><mi>O</mi><mi>S</mi><mi>T</mi></mrow></msub></math></span> algorithm is responsible for managing task bids and allocating UAV resources by taking into account bid values, available resources, and task requirements, prioritizing tasks based on their intrinsic value. The <span><math><msub><mrow><mi>A</mi></mrow><mrow><mi>d</mi><mi>u</mi><mi>a</mi><mi>l</mi></mrow></msub></math></span> algorithm optimizes task selection and UAV trajectory planning by balancing the costs and benefits associated with each task. Theoretical analysis demonstrates that the proposed approach achieves an equilibrium that significantly enhances social welfare by ensuring optimal decisions regarding task allocation and resource distribution. Empirical evaluations corroborate these findings, illustrating notable improvements in network service efficiency and validating the practical applicability of our method in maximizing social welfare.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111528"},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686299","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

SecTopo: Efficient hybrid model for detecting LLDP topology poisoning attack in programmable data plane SecTopo：一种检测可编程数据平面LLDP拓扑中毒攻击的高效混合模型

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-17 DOI: 10.1016/j.comnet.2025.111510

Lilima Jain , Venkanna U. , Satyanarayana Vollala

{"title":"SecTopo: Efficient hybrid model for detecting LLDP topology poisoning attack in programmable data plane","authors":"Lilima Jain , Venkanna U. , Satyanarayana Vollala","doi":"10.1016/j.comnet.2025.111510","DOIUrl":"10.1016/j.comnet.2025.111510","url":null,"abstract":"<div><div>The SDN controller constructs a global topology view of the programmable data plane leveraging the LLDP-based discovery mechanism. Although the controller has complete topology information, it is susceptible to attacks. Specifically, the LLDP topology poisoning attack aims to poison the topology view of the controller to degrade network performance. The attacker disrupts the controller by sending a false LLDP packet request. Sending this false LLDP request creates false link information, causes huge packet loss, and the controller gets saturated. Existing methods detect false LLDP packets through address verification and coarse-grained monitoring, which proves ineffective in achieving granular network attack classification. Moreover, the previous solution is deployed on the control plane and cannot cope with increased traffic rates and volumes in large-scale networks. This paper proposes SecTopo, an in-network hybrid model-based solution to secure topology discovery services with fine-grained monitoring of LLDP topology poisoning attacks in a programmable data plane. This solution employs autoencoders and a decision tree model to detect and mitigate LLDP topology poisoning attacks. Here, an autoencoder-based decision tree model is inferred within the match and action pipeline. The proposed solution was implemented and tested in Tofino hardware switch-based network topology. The experimental results reveal that SecTopo detects the attack, providing high accuracy (98.76%) and less resource consumption. Additionally, it identifies LLDP attack packets correctly with improved network performance and reduced control channel utilization.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111510"},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663184","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

Towards effective black-box attacks on DoH tunnel detection systems 对DoH隧道检测系统的有效黑盒攻击

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-17 DOI: 10.1016/j.comnet.2025.111524

Linghao Li, Yan Zhu, Yun Li, Wei Qiao, Zelin Cui, Susu Cui, Bo Jiang, Zhigang Lu

{"title":"Towards effective black-box attacks on DoH tunnel detection systems","authors":"Linghao Li, Yan Zhu, Yun Li, Wei Qiao, Zelin Cui, Susu Cui, Bo Jiang, Zhigang Lu","doi":"10.1016/j.comnet.2025.111524","DOIUrl":"10.1016/j.comnet.2025.111524","url":null,"abstract":"<div><div>The introduction of DNS-over-HTTPS (DoH) aims to mitigate the security vulnerabilities of traditional DNS. However, attackers have begun exploiting DoH to establish tunnels for malicious activities. Machine learning (ML)-based network intrusion detection systems (NIDSs) have emerged as a promising approach for detecting DoH tunnel attacks. Paradoxically, these ML models are susceptible to adversarial machine learning attacks. A growing number of researchers are investigating adversarial techniques to circumvent NIDS, yet they neglect the real-world viability of implementing these attack strategies under specific network constraints. To address this gap, we propose a black-box attack framework leveraging the transferability of adversarial samples, along with an adversarial sample generation algorithm called Strategic Feature-Adaptive Adversarial Attack (SFAA) which serves as the black-box attack framework’s core component. SFAA incorporates feature correlations and feature importance to optimize the perturbation direction, thereby generating more realistic adversarial samples. In the context of DoH intrusion attacks, we employ our proposed black-box attack framework to carry out adversarial attacks on commonly used and highly effective ML models. Our experimental results demonstrate that the proposed black-box attack framework effectively evades ML models, and adversarial samples generated by SFAA achieve an attack success rate (ASR) of 63.26%, surpassing state-of-the-art adversarial attacks, including the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), DeepFool, Carlini & Wagner (C&W), and Jacobian Saliency Map Attack (JSMA). Moreover, we propose a defense framework combining adversarial training and confidence-driven secondary classification, providing a novel paradigm for the robust design of machine learning models to mitigate adversarial attacks.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111524"},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703507","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

Exploring SDN architectures for IoT over Low-power and Lossy Networks (LLNs): A survey 在低功耗和有损网络（lln）上探索物联网的SDN架构：调查

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-16 DOI: 10.1016/j.comnet.2025.111494

Liticia Djennadi , Gladys Diaz , Khaled Boussetta , Christophe Cerin

{"title":"Exploring SDN architectures for IoT over Low-power and Lossy Networks (LLNs): A survey","authors":"Liticia Djennadi , Gladys Diaz , Khaled Boussetta , Christophe Cerin","doi":"10.1016/j.comnet.2025.111494","DOIUrl":"10.1016/j.comnet.2025.111494","url":null,"abstract":"<div><div>While the Internet of Things (IoT) is transforming our daily lives, it still poses significant challenges in non-industrial use cases, such as smart homes or public facilities, where Low-power and Lossy Networks (LLNs) are commonly used. These technologies are cost-effective, but also raise important management issues. Software-Defined Networking (SDN) has emerged as a promising paradigm to address these challenges by introducing flexible and centralized network management. Although several SDN-based architectures have been proposed for IoT in non-industrial LLN environments, no comprehensive and focused survey has yet consolidated the recent advancements in this field. This paper discusses the need for SDN in IoT over LLNs, exploring how traditional approaches fall short and why SDN is essential for efficient network management. We also analyze existing SDN architectures for LLNs, propose an updated taxonomy, and identify research gaps and future challenges. This work serves as a valuable reference for researchers and practitioners aiming to adopt SDN on LLNs networks.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111494"},"PeriodicalIF":4.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654152","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

In-network aggregation enabled multiple sub-blocks parallel repair in erasure-coded storage system 网络内聚合实现了擦除编码存储系统的多个子块并行修复

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-16 DOI: 10.1016/j.comnet.2025.111523

Lei Liu , Yong Wang , Yangfan Liang , Junqi Chen , Qian He

{"title":"In-network aggregation enabled multiple sub-blocks parallel repair in erasure-coded storage system","authors":"Lei Liu , Yong Wang , Yangfan Liang , Junqi Chen , Qian He","doi":"10.1016/j.comnet.2025.111523","DOIUrl":"10.1016/j.comnet.2025.111523","url":null,"abstract":"<div><div>Erasure coding has gained widespread adoption in large-scale distributed storage systems since it can significantly reduce storage overhead while ensuring high reliability. However, repairing failed data in erasure-coded systems requires retrieving data from multiple nodes, which generates substantial network traffic, and often leads to incast congestion and degraded repair performance. Existing solutions alleviate requester-side congestion by offloading aggregation operations to helpers (nodes that provide repair data), but they inevitable increase inter-helper traffic and still struggle to fully utilize global network resources. To this end, we propose lnaPR (In-network Aggregation Enabled Parallel Repair for Multiple Sub-Blocks), a framework that leverages programmable switches to perform in-network aggregation during data repair. InaPR decomposes a data repair task into multiple tree-structured pipelines, enabling data repair to collect source data from more helpers beyond the fixed k-nodes requirement. Then, the bandwidth allocation for each pipeline is optimized through a two-stage methodology: (1) a heuristic helper allocation strategy that assigns high-bandwidth helpers across multiple pipelines while distributing low-capacity ones among distinct pipelines; (2) a throughput-maximizing bandwidth allocation formulated as a linear programming model. Furthermore, we also extend the architecture to cross-rack scenarios through virtual node decomposition. Finally, we prototype lnaPR using a P4-programmable switch and validate its performance in real-world evaluations and multi-rack simulations. Experimental results demonstrate that InaPR achieves 6.74% higher repair throughput than state-of-the-art methods in single-rack prototype tests and an 11.03% improvement in cross-rack simulations.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111523"},"PeriodicalIF":4.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670881","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

Collaborative computation offloading and trajectory planning in locally observable multi-UAV MEC networks 局部可观测多无人机MEC网络协同计算卸载与轨迹规划

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2025-07-15 DOI: 10.1016/j.comnet.2025.111554

Yuan He , Xie Jun , Yaqun Liu , Xijian Luo

{"title":"Collaborative computation offloading and trajectory planning in locally observable multi-UAV MEC networks","authors":"Yuan He , Xie Jun , Yaqun Liu , Xijian Luo","doi":"10.1016/j.comnet.2025.111554","DOIUrl":"10.1016/j.comnet.2025.111554","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) can be deployed as aerial edge servers to provide ground users with mobile edge computing (MEC) services in a collaborative manner. In this paper, we investigate the motion and computation decision-making problem of the multi-UAV-assisted MEC in a locally observed environment. Firstly, a multi-UAV-assisted MEC network model is proposed taking into account the collaborative computation among UAVs and the task’s queuing delay. Secondly, we propose a reinforcement learning algorithm based on Graph Attention Networks (GAT) named GatPPO to implement UAV control based on local information. The local networks observed by UAVs are abstracted into heterogeneous and homogeneous graphs. Then, we extract and aggregate the graphs’ features with GAT to alleviate the problem of inconsistent input dimensions caused by the number of neighbors and users changing. In addition, two actor-critic networks are designed in each UAV agent for the trajectory planning and computation decisions respectively to solve the problem of asynchronous actions selection due to different frequencies. The numerical simulation results show that compared with the benchmark algorithms, GatPPO reduces the computation delay by about 10 %–30 % and improves user satisfaction by about 113 % at most when a single UAV has limited computing resources.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"270 ","pages":"Article 111554"},"PeriodicalIF":4.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696464","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