IEEE Transactions on Network Science and Engineering最新文献_第2页

Blockchain-Assisted Flexible Revocable Anonymous Authentication in Industrial Internet of Things 工业物联网中区块链辅助的灵活可撤销匿名认证

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-21 DOI: 10.1109/TNSE.2024.3503996

Fengqun Wang;Jie Cui;Qingyang Zhang;Debiao He;Hong Zhong

{"title":"Blockchain-Assisted Flexible Revocable Anonymous Authentication in Industrial Internet of Things","authors":"Fengqun Wang;Jie Cui;Qingyang Zhang;Debiao He;Hong Zhong","doi":"10.1109/TNSE.2024.3503996","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3503996","url":null,"abstract":"In Industrial Internet of Things (IIoT) systems, data sharing between industrial departments is often utilized to optimize management models and improve decision-making efficiency. To enable secure data sharing, authentication between smart devices is critical. However, existing authentication schemes do not comprehensively consider data anonymity, data traceability, pseudonym management, and flexible revocation of devices, which cannot meet the needs of IIoT systems for security, real-time, and dynamicity. Therefore, we propose a blockchain-assisted lightweight authentication scheme. First, we design a lightweight authentication method based on Okamoto's protocol and elliptic curve cryptography, which achieves fast authentication of smart devices while ensuring data anonymity and traceability. Second, we design a two-level key derivation algorithm and combine it with blockchain technology to address the issue of pseudonym management. Smart devices can generate pseudonyms without requesting them from the key generation center and can be revoked flexibly. Third, security proof and analysis demonstrate that the proposed scheme achieves the security objectives and is resistant to various common attacks. Finally, the performance evaluation results show that our proposed scheme performs better than the other related schemes regarding computational and communication overheads.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"518-532"},"PeriodicalIF":6.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880380","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 the Ratio-Based Offloading in Federated Cloud-Edge Systems: A MADRL Approach 联邦云边缘系统中基于比率的卸载优化：一种MADRL方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-21 DOI: 10.1109/TNSE.2024.3501398

Seifu Birhanu Tadele;Widhi Yahya;Binayak Kar;Ying-Dar Lin;Yuan-Cheng Lai;Frezer Guteta Wakgra

{"title":"Optimizing the Ratio-Based Offloading in Federated Cloud-Edge Systems: A MADRL Approach","authors":"Seifu Birhanu Tadele;Widhi Yahya;Binayak Kar;Ying-Dar Lin;Yuan-Cheng Lai;Frezer Guteta Wakgra","doi":"10.1109/TNSE.2024.3501398","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3501398","url":null,"abstract":"In the evolving landscape of cloud-edge federated systems, Multi-Access Edge Computing (MEC) plays a crucial role by being closer to user equipment (UEs). However, it has limited capacity compared to the cloud, leading to challenges during periods of high network traffic, commonly referred to as hotspot traffic, when MEC resources can become overwhelmed. To mitigate this issue, horizontal and vertical traffic offloading between edges and core sites, and from core sites to the cloud, respectively, is employed. The offloading decisions, crucial for ensuring network efficiency, must be made within seconds. Traditional optimization techniques are unsuitable due to their computational intensity and time-consuming nature, necessitating a shift toward machine learning methods. This research introduces a ratio-based offloading approach, leveraging a multi-agent deep reinforcement learning (MADRL) approach based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. In a comparative evaluation against the simulated annealing (SA) algorithm and single-agent deep reinforcement learning (DRL) approaches, our proposed solution exhibits superior performance, particularly in terms of decision time. The DRL-based approach achieves convergence within seconds, whereas SA takes minutes. Additionally, the average latency experienced by traffic in the multi-agent TD3 configuration is approximately 3–4 times less than in the single-agent configuration.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"463-475"},"PeriodicalIF":6.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880449","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

Guest Editorial: Introduction to the Special Section on Research on Power Technology, Economy and Policy Towards Net-Zero Emissions 特邀社论：迈向净零排放的电力技术、经济和政策研究》特别章节导言

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-20 DOI: 10.1109/TNSE.2024.3478396

Junhua Zhao;Jing Qiu;Fushuan Wen;Junbo Zhao;Ciwei Gao;Yue Zhou

引用次数: 0

Guest Editorial: Introduction to the Special Section on Aerial Computing Networks in 6G 特邀编辑：6G 空中计算网络专题介绍

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-20 DOI: 10.1109/TNSE.2024.3483408

Yang Yang;Chen Chen;Rose Qingyang Hu;Schahram Dustdar;Qingqi Pei

引用次数: 0

A Cost-Effective Hybrid Cloud Resource Scaling Framework for Batch Processing Services 批处理服务的低成本混合云资源扩展框架

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-19 DOI: 10.1109/TNSE.2024.3502503

Qinzhi Zhang;Li Pan;Shijun Liu

{"title":"A Cost-Effective Hybrid Cloud Resource Scaling Framework for Batch Processing Services","authors":"Qinzhi Zhang;Li Pan;Shijun Liu","doi":"10.1109/TNSE.2024.3502503","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3502503","url":null,"abstract":"Batch processing services, like offline video processing, are pivotal in modern data analysis. Software as a Service (SaaS) providers typically purchase virtual machines (VMs) or Function as a Service (FaaS) instances, also known as function instances, from cloud providers to provision computational resources for their services. VMs offer stable performance and cost-effectiveness for continuous workloads but may incur resource waste due to idleness. Conversely, function instances, with rapid auto-scaling and fine-grained billing, excel in handling discrete workloads, albeit at a higher unit price. SaaS providers can leverage the advantages of both VMs and function instances, to achieve cost-effective service delivery while ensuring overall performance. However, due to the complexity and unpredictability of batch processing service workloads, achieving this goal is challenging. To address these issues, in this paper we propose a proximal policy optimization (PPO) based hybrid resource scaling algorithm and design a hybrid resource scaling framework. The proposed scaling framework considers the workload characteristics and performance requirements of batch processing services, adaptively making cost-optimal resource scaling decisions based on current workloads and configuration of computational resources, while ensuring the overall performance of the service. We conduct extensive simulation experiments on multiple workloads with different levels of discreteness extracted from Microsoft and Huawei datasets, and the results demonstrate that our framework can achieve optimal service cost while ensuring overall performance.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"476-487"},"PeriodicalIF":6.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880327","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

EigenObfu: A Novel Network Topology Obfuscation Defense Method 特征混淆：一种新的网络拓扑混淆防御方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-18 DOI: 10.1109/TNSE.2024.3501396

Ziliang Zhu;Guopu Zhu;Yu Zhang;Jiantao Shi;Xiaoxia Huang;Yuguang Fang

{"title":"EigenObfu: A Novel Network Topology Obfuscation Defense Method","authors":"Ziliang Zhu;Guopu Zhu;Yu Zhang;Jiantao Shi;Xiaoxia Huang;Yuguang Fang","doi":"10.1109/TNSE.2024.3501396","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3501396","url":null,"abstract":"Link flooding attack is a kind of attack based on the topology information of a network. Sophisticated attackers tend to conduct network reconnaissance before they launch effective attacks to infer key information about the whole network. Existing active defense methods against link flooding attacks either focus on protecting the key links within the network or safeguarding the key nodes with the simple degree centrality. This paper proposes a novel network topology obfuscation method called EigenObfu to protect the key nodes. Instead of using the degree centrality in existing defense methods, our eigenvector centrality-based EigenObfu comprehensively utilizes network topology information and better measures the importance of nodes in a network. EigenObfu is designed to output a secure obfuscated topology suitable for networks, regardless of their sizes, by hiding important nodes while maintaining connectivity and ensuring the protection of key nodes. We evaluate EigenObfu through several comparison experiments on nine different topologies. The results confirm the effectiveness of our method.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"451-462"},"PeriodicalIF":6.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880334","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

Distributed On-Demand Routing Algorithm With Graph Representation Learning for Industrial IoT 基于图表示学习的工业物联网分布式按需路由算法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-14 DOI: 10.1109/TNSE.2024.3496438

Bin Dai;Hetao Li;Wenrui Huang

引用次数: 0

Graph-Based Learning in Core and Edge Virtualized O-RAN for Handling Real-Time AI Workloads 处理实时AI工作负载的核心和边缘虚拟化O-RAN中的基于图的学习

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-14 DOI: 10.1109/TNSE.2024.3495583

Prohim Tam;Seokhoon Kim

{"title":"Graph-Based Learning in Core and Edge Virtualized O-RAN for Handling Real-Time AI Workloads","authors":"Prohim Tam;Seokhoon Kim","doi":"10.1109/TNSE.2024.3495583","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3495583","url":null,"abstract":"AI-empowered applications have been deployed in many aspects of networking, and federated learning (FL) has emerged as a complementary approach due to its ability to enable privacy-preserving model training and inference. However, without self-organizing capability, practical FL systems face several issues to co-exist in real-time networking. Therefore, this paper aims to design autonomous FL management with integrated graph neural networks (GNN) and deep reinforcement learning (DRL), namely AutoFedGDRL, to sustain heterogeneous FL execution in optimized open radio access network (O-RAN) and intelligent core network architectures and offer automated policy-driven orchestration by intelligent agent controller. Edge cloud virtualized O-RAN is integrated to assist model computation and support multiple services with elastic containerized resource scaling. The practicability of FL systems is stimulated by modelling the participants and aggregators as a graph representation and subsequently analyzing to predict the accessibility and trustworthiness of the nodes, bandwidth capacities, and virtual link relationship. Our proposed AutoFedGDRL aims to obtain specifications of hidden FL, service, and networking states in order to control the main policies, such as training management, resource sharing, aggregation scheduling, and service prioritization. In the experiment, AutoFedGDRL surpassed reference models (non-federated training) in global accuracy, achieving 98.23% for MNIST and 97.12% for CIFAR-10, compared to 98.22% and 95.89% for PrimaryGNN-FL. The proposed scheme also improved end-to-end convergence speed, with execution times 10.58 ms to 32.79 ms faster. Model delivery ratios reached 99.98%, ensuring the federated system's reliability and sharing workload efficiency.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"302-318"},"PeriodicalIF":6.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880385","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

Link-Identified Routing Architecture in Space 空间中的链路识别路由体系结构

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-14 DOI: 10.1109/TNSE.2024.3498042

Hefan Zhang;Zhiyuan Wang;Shan Zhang;Qingkai Meng;Hongbin Luo

{"title":"Link-Identified Routing Architecture in Space","authors":"Hefan Zhang;Zhiyuan Wang;Shan Zhang;Qingkai Meng;Hongbin Luo","doi":"10.1109/TNSE.2024.3498042","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3498042","url":null,"abstract":"Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient packet delivery. In this paper, we propose a Link-identified Routing (LiR) architecture for LEO satellite networks. The LiR architecture leverages the deterministic neighbor relation of LEO constellations, and identifies each inter-satellite link (ISL). Moreover, LiR architecture adopts source-route-style forwarding based on in-packet bloom filter (BF). Each satellite could efficiently encode multiple ISL identifiers via an in-packet BF to specify the end-to-end path for the packets. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwarding cases emerge. Based on the topology characteristics, we derive the expected forwarding overhead in a closed-form and propose the optimal encoding policy. To accommodate link-state changes in LEO satellite networks, we propose the on-demand rerouting scheme and the on-demand detouring scheme to address the intermittent ISLs. We also elaborate how to take advantage of LiR architecture to achieve seamless handover for ground-satellite links (GSLs). Finally, we conduct extensive numerical experiments and packet-level simulations to verify our analytical results and evaluate the performance of the LiR architecture.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"392-408"},"PeriodicalIF":6.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880386","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

OpenFi: Open-Set WiFi Human Sensing via Virtual Embedding Confidence-Aware OpenFi：通过虚拟嵌入置信度感知的开放式WiFi人体传感

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-14 DOI: 10.1109/TNSE.2024.3496496

Han Liu;Liang Xi;Wei Wang;Fengbin Zhang;Zygmunt J. Haas

{"title":"OpenFi: Open-Set WiFi Human Sensing via Virtual Embedding Confidence-Aware","authors":"Han Liu;Liang Xi;Wei Wang;Fengbin Zhang;Zygmunt J. Haas","doi":"10.1109/TNSE.2024.3496496","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3496496","url":null,"abstract":"WiFi sensing technology utilizes Channel State Information (CSI) to analyze human behavior and plays a crucial role in mobile computing. WiFi sensing systems are typically deployed by collecting data in specific known environments, known as closed-set settings. However, in practical deployment, WiFi sensing systems may encounter unknown environments, generating unknown CSI patterns due to signal reflection, multipath effects, and interference. In such open-set conditions, the WiFi sensing system should possess the capability to recognize unknown CSI patterns, enhancing its security and reliability. In response, this work proposes an open-set WiFi human sensing method based on virtual embedding confidence-aware (OpenFi). The core of OpenFi is virtual embedding generation to simulate a realistic open-set feature space. This strategy minimizes both empirical and open-set risks, enabling OpenFi to recognize unknown CSI patterns effectively. We conducted extensive experiments on diverse datasets, covering various WiFi sensing tasks, including human identification, human activity recognition, and sign language recognition. Experimental results demonstrate that OpenFi accurately identifies previously unseen CSI patterns in open-set conditions, achieving significant improvements of up to 27% and 10.26% in the FPR95 and OSCR metrics, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"344-355"},"PeriodicalIF":6.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880328","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