arXiv - CS - Networking and Internet Architecture最新文献

{"title":"Anomaly Detection in Offshore Open Radio Access Network Using Long Short-Term Memory Models on a Novel Artificial Intelligence-Driven Cloud-Native Data Platform","authors":"Abdelrahim Ahmad, Peizheng Li, Robert Piechocki, Rui Inacio","doi":"arxiv-2409.02849","DOIUrl":"https://doi.org/arxiv-2409.02849","url":null,"abstract":"The radio access network (RAN) is a critical component of modern telecom\u0000infrastructure, currently undergoing significant transformation towards\u0000disaggregated and open architectures. These advancements are pivotal for\u0000integrating intelligent, data-driven applications aimed at enhancing network\u0000reliability and operational autonomy through the introduction of cognition\u0000capabilities, exemplified by the set of enhancements proposed by the emerging\u0000Open radio access network (O-RAN) standards. Despite its potential, the nascent\u0000nature of O-RAN technology presents challenges, primarily due to the absence of\u0000mature operational standards. This complicates the management of data and\u0000applications, particularly in integrating with traditional network management\u0000and operational support systems. Divergent vendor-specific design approaches\u0000further hinder migration and limit solution reusability. Addressing the skills\u0000gap in telecom business-oriented engineering is crucial for the effective\u0000deployment of O-RAN and the development of robust data-driven applications. To\u0000address these challenges, Boldyn Networks, a global Neutral Host provider, has\u0000implemented a novel cloud-native data analytics platform. This platform\u0000underwent rigorous testing in real-world scenarios of using advanced artificial\u0000intelligence (AI) techniques, significantly improving operational efficiency,\u0000and enhancing customer experience. Implementation involved adopting development\u0000operations (DevOps) practices, leveraging data lakehouse architectures tailored\u0000for AI applications, and employing sophisticated data engineering strategies.\u0000The platform successfully addresses connectivity challenges inherent in\u0000offshore windfarm deployments using long short-term memory (LSTM) Models for\u0000anomaly detection of the connectivity, providing detailed insights into its\u0000specialized architecture developed for this purpose.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Transfer for Collaborative Misbehavior Detection in Untrusted Vehicular Environments","authors":"Roshan Sedar, Charalampos Kalalas, Paolo Dini, Francisco Vazquez-Gallego, Jesus Alonso-Zarate, Luis Alonso","doi":"arxiv-2409.02844","DOIUrl":"https://doi.org/arxiv-2409.02844","url":null,"abstract":"Vehicular mobility underscores the need for collaborative misbehavior\u0000detection at the vehicular edge. However, locally trained misbehavior detection\u0000models are susceptible to adversarial attacks that aim to deliberately\u0000influence learning outcomes. In this paper, we introduce a deep reinforcement\u0000learning-based approach that employs transfer learning for collaborative\u0000misbehavior detection among roadside units (RSUs). In the presence of\u0000label-flipping and policy induction attacks, we perform selective knowledge\u0000transfer from trustworthy source RSUs to foster relevant expertise in\u0000misbehavior detection and avoid negative knowledge sharing from\u0000adversary-influenced RSUs. The performance of our proposed scheme is\u0000demonstrated with evaluations over a diverse set of misbehavior detection\u0000scenarios using an open-source dataset. Experimental results show that our\u0000approach significantly reduces the training time at the target RSU and achieves\u0000superior detection performance compared to the baseline scheme with tabula rasa\u0000learning. Enhanced robustness and generalizability can also be attained, by\u0000effectively detecting previously unseen and partially observable misbehavior\u0000attacks.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security Implications and Mitigation Strategies in MPLS Networks","authors":"Ayush Thakur","doi":"arxiv-2409.03795","DOIUrl":"https://doi.org/arxiv-2409.03795","url":null,"abstract":"Multiprotocol Label Switching (MPLS) is a high-performance telecommunications\u0000technology that directs data from one network node to another based on short\u0000path labels rather than long network addresses. Its efficiency and scalability\u0000have made it a popular choice for large-scale and enterprise networks. However,\u0000as MPLS networks grow and evolve, they encounter various security challenges.\u0000This paper explores the security implications associated with MPLS networks,\u0000including risks such as label spoofing, traffic interception, and denial of\u0000service attacks. Additionally, it evaluates advanced mitigation strategies to\u0000address these vulnerabilities, leveraging mathematical models and security\u0000protocols to enhance MPLS network resilience. By integrating theoretical\u0000analysis with practical solutions, this paper aims to provide a comprehensive\u0000understanding of MPLS security and propose effective methods for safeguarding\u0000network infrastructure.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Edge-Based Data Lake Architecture for Intelligent Transportation System","authors":"Danilo Fernandes, Douglas L. L. Moura, Gean Santos, Geymerson S. Ramos, Fabiane Queiroz, Andre L. L. Aquino","doi":"arxiv-2409.02808","DOIUrl":"https://doi.org/arxiv-2409.02808","url":null,"abstract":"The rapid urbanization growth has underscored the need for innovative\u0000solutions to enhance transportation efficiency and safety. Intelligent\u0000Transportation Systems (ITS) have emerged as a promising solution in this\u0000context. However, analyzing and processing the massive and intricate data\u0000generated by ITS presents significant challenges for traditional data\u0000processing systems. This work proposes an Edge-based Data Lake Architecture to\u0000integrate and analyze the complex data from ITS efficiently. The architecture\u0000offers scalability, fault tolerance, and performance, improving decision-making\u0000and enhancing innovative services for a more intelligent transportation\u0000ecosystem. We demonstrate the effectiveness of the architecture through an\u0000analysis of three different use cases: (i) Vehicular Sensor Network, (ii)\u0000Mobile Network, and (iii) Driver Identification applications.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low Layer Functional Split Management in 5G and Beyond: Architecture and Self-adaptation","authors":"Jordi Pérez-Romero, Oriol Sallent, David Campoy, Antoni Gelonch, Xavier Gelabert, Bleron Klaiqi","doi":"arxiv-2409.01701","DOIUrl":"https://doi.org/arxiv-2409.01701","url":null,"abstract":"Radio Access Network (RAN) disaggregation is emerging as a key trend in\u0000beyond 5G, as it offers new opportunities for more flexible deployments and\u0000intelligent network management. A relevant problem in disaggregated RAN is the\u0000functional split selection, which dynamically decides which baseband (BB)\u0000functions of a base station are kept close to the radio units and which ones\u0000are centralized. In this context, this paper firstly presents an architectural\u0000framework for supporting this concept relying on the O-RAN architecture. Then,\u0000the paper analyzes how the functional split can be optimized to adapt to the\u0000different load conditions while minimizing energy costs.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Branch Attention Convolutional Neural Network for Online RIS Configuration with Discrete Responses: A Neuroevolution Approach","authors":"George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos","doi":"arxiv-2409.01765","DOIUrl":"https://doi.org/arxiv-2409.01765","url":null,"abstract":"In this paper, we consider the problem of jointly controlling the\u0000configuration of a Reconfigurable Intelligent Surface (RIS) with unit elements\u0000of discrete responses and a codebook-based transmit precoder in RIS-empowered\u0000Multiple-Input Single-Output (MISO) communication systems. The adjustable\u0000elements of the RIS and the precoding vector need to be jointly modified in\u0000real time to account for rapid changes in the wireless channels, making the\u0000application of complicated discrete optimization algorithms impractical. We\u0000present a novel Multi-Branch Attention Convolutional Neural Network (MBACNN)\u0000architecture for this design objective which is optimized using NeuroEvolution\u0000(NE), leveraging its capability to effectively tackle the non-differentiable\u0000problem arising from the discrete phase states of the RIS elements. The channel\u0000matrices of all involved links are first passed to separate self-attention\u0000layers to obtain initial embeddings, which are then concatenated and passed to\u0000a convolutional network for spatial feature extraction, before being fed to a\u0000per-element multi-layered perceptron for the final RIS phase configuration\u0000calculation. Our MBACNN architecture is then extended to multi-RIS-empowered\u0000MISO communication systems, and a novel NE-based optimization approach for the\u0000online distributed configuration of multiple RISs is presented. The superiority\u0000of the proposed single-RIS approach over both learning-based and classical\u0000discrete optimization benchmarks is showcased via extensive numerical\u0000evaluations over both stochastic and geometrical channel models. It is also\u0000demonstrated that the proposed distributed multi-RIS approach outperforms both\u0000distributed controllers with feedforward neural networks and fully centralized\u0000ones.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Priority based inter-twin communication in vehicular digital twin networks","authors":"Qasim Zia, Chenyu Wang, Saide Zhu, Yingshu Li","doi":"arxiv-2409.01683","DOIUrl":"https://doi.org/arxiv-2409.01683","url":null,"abstract":"With the advancement and boom of autonomous vehicles, vehicular digital twins\u0000(VDTs) have become an emerging research area. VDT can solve the issues related\u0000to autonomous vehicles and provide improved and enhanced services to users.\u0000Recent studies have demonstrated the potential of using priorities in acquiring\u0000improved response time. However, since VDT is comprised of intra-twin and\u0000inter-twin communication, it leads to a reduced response time as traffic\u0000congestion grows, which causes issues in the form of accidents. It would be\u0000encouraging if priorities could be used in inter-twin communication of VDT for\u0000data sharing and processing tasks. Moreover, it would also be effective for\u0000managing the communication overhead on the digital twin layer of the cloud.\u0000This paper proposes a novel priority-based inter-twin communication in VDT to\u0000address this issue. We formulate the problem for priorities of digital twins\u0000and applications according to their categories. In addition, we describe the\u0000priority-based inter-twin communication in VDT in detail and algorithms for\u0000priority communication for intra-twin and inter-twin are designed,\u0000respectively. Finally, experiments on different priority tasks are conducted\u0000and compared with two existing algorithms, demonstrating our proposed\u0000algorithm's effectiveness and efficiency.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Digital Twin Meets 6G: Concepts, Obstacles, and Research Prospects","authors":"Wenshuai Liu, Yaru Fu, Zheng Shi, Hong Wang","doi":"arxiv-2409.02008","DOIUrl":"https://doi.org/arxiv-2409.02008","url":null,"abstract":"The convergence of digital twin technology and the emerging 6G network\u0000presents both challenges and numerous research opportunities. This article\u0000explores the potential synergies between digital twin and 6G, highlighting the\u0000key challenges and proposing fundamental principles for their integration. We\u0000discuss the unique requirements and capabilities of digital twin in the context\u0000of 6G networks, such as sustainable deployment, real-time synchronization,\u0000seamless migration, predictive analytic, and closed-loop control. Furthermore,\u0000we identify research opportunities for leveraging digital twin and artificial\u0000intelligence to enhance various aspects of 6G, including network optimization,\u0000resource allocation, security, and intelligent service provisioning. This\u0000article aims to stimulate further research and innovation at the intersection\u0000of digital twin and 6G, paving the way for transformative applications and\u0000services in the future.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Deep Reinforcement Learning-Based Intelligent Channel Access in Dense Wi-Fi Deployments","authors":"Xinyang Du, Xuming Fang, Rong He, Li Yan, Liuming Lu, Chaoming Luo","doi":"arxiv-2409.01004","DOIUrl":"https://doi.org/arxiv-2409.01004","url":null,"abstract":"The IEEE 802.11 MAC layer utilizes the Carrier Sense Multiple Access with\u0000Collision Avoidance (CSMA/CA) mechanism for channel contention and access.\u0000However, in densely deployed Wi-Fi scenarios, intense competition may lead to\u0000packet collisions among users. Although many studies have used machine learning\u0000methods to optimize channel contention and access mechanisms, most of them are\u0000based on AP-centric single-agent models or distributed models, which still\u0000suffer poor generalization and insensitivity to dynamic environments. To\u0000address these challenges, this paper proposes an intelligent channel contention\u0000access mechanism that combines Federated Learning (FL) and Deep Deterministic\u0000Policy Gradient (DDPG) algorithms. Additionally, an FL model training pruning\u0000strategy and weight aggregation algorithm are designed to enhance the\u0000effectiveness of training samples and reduce the average MAC delay. We evaluate\u0000and validate the proposed solution using NS3-AI framework. Simulation results\u0000show that in static scenarios, our proposed scheme reduces the average MAC\u0000delay by 25.24% compared to traditional FL algorithms. In dynamic scenarios, it\u0000outperforms Average Federated Reinforcement Learning (A-FRL) and distributed\u0000Deep Reinforcement Learning (DRL) algorithms by 25.72% and 45.9%, respectively.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DTRAN: A Special Use Case of RAN Optimization using Digital Twin","authors":"Caglar Tunc, Kubra Duran, Buse Bilgin, Gokhan Kalem, Berk Canberk","doi":"arxiv-2409.01136","DOIUrl":"https://doi.org/arxiv-2409.01136","url":null,"abstract":"The emergence of beyond 5G (B5G) and 6G networks underscores the critical\u0000role of advanced computer-aided tools, such as network digital twins (DTs), in\u0000fostering autonomous networks and ubiquitous intelligence. Existing solutions\u0000in the DT domain primarily aim to model and automate specific tasks within the\u0000network lifecycle, which lack flexibility and adaptability for fully autonomous\u0000design and management. Unlike the existing DT approaches, we propose RAN\u0000optimization using the Digital Twin (DTRAN) framework that follows a holistic\u0000approach from core to edge networks. The proposed DTRAN framework enables\u0000real-time data management and communication with the physical network, which\u0000provides a more accurate and detailed digital replica than the existing\u0000approaches. We outline the main building blocks of the DTRAN and describe the\u0000details of our specific use case, which is RAN configuration optimization, to\u0000demonstrate the applicability of the proposed framework for a real-world\u0000scenario.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}