Pub Date : 2023-09-13DOI: 10.1109/LNET.2023.3314736
Yun Lai;Yu Duan;Lifeng Wang
Conventional vehicle-to-vehicle communication aided platooning system is distributed under various communication topologies and suffers from the severe interference, uncertain topologies, and heterogeneous communication delays, particularly in the urban areas with dense vehicles. Platooning design with the vehicle-to-infrastructure communication (V2I) enables the minimum number of communication links. Therefore, this letter proposes an optimal control scheme for minimizing the overall status errors under delay concern in the V2I based platooning system. By transforming the continuous-time plant into a discrete-time linear system, the considered system is confirmed to be controllable. Numerical results confirm that the proposed scheme is effective and achieves stability in the presence of different communication delay conditions.
{"title":"Optimal Control for Platooning in Vehicle-to-Infrastructure Communications Networks","authors":"Yun Lai;Yu Duan;Lifeng Wang","doi":"10.1109/LNET.2023.3314736","DOIUrl":"10.1109/LNET.2023.3314736","url":null,"abstract":"Conventional vehicle-to-vehicle communication aided platooning system is distributed under various communication topologies and suffers from the severe interference, uncertain topologies, and heterogeneous communication delays, particularly in the urban areas with dense vehicles. Platooning design with the vehicle-to-infrastructure communication (V2I) enables the minimum number of communication links. Therefore, this letter proposes an optimal control scheme for minimizing the overall status errors under delay concern in the V2I based platooning system. By transforming the continuous-time plant into a discrete-time linear system, the considered system is confirmed to be controllable. Numerical results confirm that the proposed scheme is effective and achieves stability in the presence of different communication delay conditions.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"289-293"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135402805","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}
Pub Date : 2023-08-30DOI: 10.1109/LNET.2023.3310359
Cyril Shih-Huan Hsu;Danny De Vleeschauwer;Chrysa Papagianni
For a network slice that spans multiple technology and/or administrative domains, these domains must ensure that the slice’s End-to-End (E2E) Service Level Agreement (SLA) is met. Thus, the E2E SLA should be decomposed to partial SLAs, assigned to each of these domains. Assuming a two-level management architecture consisting of an E2E service orchestrator and local domain controllers, we consider that the former is only aware of historical data of the local controllers’ responses to previous slice requests, and captures this knowledge in a risk model per domain. In this letter, we propose the use of Neural Network (NN) based risk models, using such historical data, to decompose the E2E SLA. Specifically, we introduce models that incorporate monotonicity, applicable even in cases involving small datasets. An empirical study on a synthetic multi-domain dataset demonstrates the efficiency of our approach.
{"title":"SLA Decomposition for Network Slicing: A Deep Neural Network Approach","authors":"Cyril Shih-Huan Hsu;Danny De Vleeschauwer;Chrysa Papagianni","doi":"10.1109/LNET.2023.3310359","DOIUrl":"10.1109/LNET.2023.3310359","url":null,"abstract":"For a network slice that spans multiple technology and/or administrative domains, these domains must ensure that the slice’s End-to-End (E2E) Service Level Agreement (SLA) is met. Thus, the E2E SLA should be decomposed to partial SLAs, assigned to each of these domains. Assuming a two-level management architecture consisting of an E2E service orchestrator and local domain controllers, we consider that the former is only aware of historical data of the local controllers’ responses to previous slice requests, and captures this knowledge in a risk model per domain. In this letter, we propose the use of Neural Network (NN) based risk models, using such historical data, to decompose the E2E SLA. Specifically, we introduce models that incorporate monotonicity, applicable even in cases involving small datasets. An empirical study on a synthetic multi-domain dataset demonstrates the efficiency of our approach.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"294-298"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73257990","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}
Pub Date : 2023-08-28DOI: 10.1109/LNET.2023.3309537
Narasimha Rao Banavathu
This letter proposes a generic ${L}$ -out-of- ${M}$ counting rule-based sensing, wherein the cognitive radios (CRs) with non-identical receiver operating characteristic (ROC) curves and the fusion node cooperatively identify the primary user’s state. We formulate a generalized Neyman-Pearson problem to jointly optimize the individual CRs’ operational points on the ROC curves and the generic ${L}$ -out-of- ${M}$ counting rule for the CR system. Then, a fast-sensing problem is formulated to find the least number of CRs needed for practical sensing. We provide generalized solutions for any detector employed in the CR system. The proposed scheme shows superior detection performance compared to the traditional scheme.
{"title":"Optimal Generic L-Out-of-M Counting Rule for Neyman-Pearson Test in Cognitive Radio Networks","authors":"Narasimha Rao Banavathu","doi":"10.1109/LNET.2023.3309537","DOIUrl":"10.1109/LNET.2023.3309537","url":null,"abstract":"This letter proposes a generic \u0000<inline-formula> <tex-math>${L}$ </tex-math></inline-formula>\u0000-out-of-\u0000<inline-formula> <tex-math>${M}$ </tex-math></inline-formula>\u0000 counting rule-based sensing, wherein the cognitive radios (CRs) with non-identical receiver operating characteristic (ROC) curves and the fusion node cooperatively identify the primary user’s state. We formulate a generalized Neyman-Pearson problem to jointly optimize the individual CRs’ operational points on the ROC curves and the generic \u0000<inline-formula> <tex-math>${L}$ </tex-math></inline-formula>\u0000-out-of-\u0000<inline-formula> <tex-math>${M}$ </tex-math></inline-formula>\u0000 counting rule for the CR system. Then, a fast-sensing problem is formulated to find the least number of CRs needed for practical sensing. We provide generalized solutions for any detector employed in the CR system. The proposed scheme shows superior detection performance compared to the traditional scheme.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"189-193"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88012477","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}
Pub Date : 2023-08-18DOI: 10.1109/LNET.2023.3306419
Shachi Sharma;Prakash Datt Bhatt
Statistical analysis of IoT network traffic establishes the presence of lognormal distributed inter-arrivals. This letter presents analysis of LN/M/1 queueing model using approximate results of Laplace-Stieltjes transformation of lognormal distribution in terms of Lambert ${{mathcal { W}}}$ (.) function. The comparative performance evaluation of the LN/M/1 model with traditional M/M/1 model reveals that the quality of service metrics of LN/M/1 model mostly remain lower than M/M/1 implying that less number of buffers are required in IoT systems driven by such traffic. The asymptotic expressions for loss probability are derived using finite capacity LN/M/1/N model for different values of traffic intensity.
{"title":"Analysis of Queues Fed by Lognormal Distributed Arrivals for IoT Network Traffic","authors":"Shachi Sharma;Prakash Datt Bhatt","doi":"10.1109/LNET.2023.3306419","DOIUrl":"10.1109/LNET.2023.3306419","url":null,"abstract":"Statistical analysis of IoT network traffic establishes the presence of lognormal distributed inter-arrivals. This letter presents analysis of LN/M/1 queueing model using approximate results of Laplace-Stieltjes transformation of lognormal distribution in terms of Lambert \u0000<inline-formula> <tex-math>${{mathcal { W}}}$ </tex-math></inline-formula>\u0000(.) function. The comparative performance evaluation of the LN/M/1 model with traditional M/M/1 model reveals that the quality of service metrics of LN/M/1 model mostly remain lower than M/M/1 implying that less number of buffers are required in IoT systems driven by such traffic. The asymptotic expressions for loss probability are derived using finite capacity LN/M/1/N model for different values of traffic intensity.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"260-264"},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73760229","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}
Segment Routing is a source routing paradigm that simplifies packet forwarding and management across networks. Two major challenges of segment routing are the limitation of header stack size and the size of the Forwarding Table specific to each node. It has been observed that there is a trade-off between these two factors. In this letter, we have addressed this trade-off and presented a mechanism to minimize the stack size as well as the forwarding table. Instead of individual Segment IDs used in traditional segment routing, unique Path IDs are considered. This reduces the size of the header stack to one irrespective of the number of segments in the path. To reduce the size of the forwarding table, a forwarding technique based on matrix operations is used. Our approach also works on the reliability issues in forwarding and guarantees 100% successful packet forwarding.
网段路由是一种源路由范例，可简化网络中的数据包转发和管理。段路由的两大挑战是报头堆栈大小的限制和每个节点特定的转发表的大小。据观察，这两个因素之间存在权衡。在这封信中，我们解决了这一权衡问题，并提出了一种将堆栈大小和转发表最小化的机制。我们考虑使用唯一的路径 ID 代替传统段路由中使用的单个段 ID。这样，无论路径上有多少网段，报头堆栈的大小都会减小到一个。为了减少转发表的大小，我们采用了基于矩阵运算的转发技术。我们的方法还能解决转发中的可靠性问题，并保证数据包 100% 成功转发。
{"title":"A Hybrid Approach to Header Size and Forwarding Table Optimization in Segment Routing","authors":"Anushree Roy;Tania Sarkar;Pranav Kumar Singh;Ranjan Maity","doi":"10.1109/LNET.2023.3305247","DOIUrl":"10.1109/LNET.2023.3305247","url":null,"abstract":"Segment Routing is a source routing paradigm that simplifies packet forwarding and management across networks. Two major challenges of segment routing are the limitation of header stack size and the size of the Forwarding Table specific to each node. It has been observed that there is a trade-off between these two factors. In this letter, we have addressed this trade-off and presented a mechanism to minimize the stack size as well as the forwarding table. Instead of individual Segment IDs used in traditional segment routing, unique Path IDs are considered. This reduces the size of the header stack to one irrespective of the number of segments in the path. To reduce the size of the forwarding table, a forwarding technique based on matrix operations is used. Our approach also works on the reliability issues in forwarding and guarantees 100% successful packet forwarding.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"275-278"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87707371","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}
Pub Date : 2023-08-07DOI: 10.1109/LNET.2023.3302833
Abdullah Ridwan Hossain;Weiqi Liu;Nirwan Ansari;Abbas Kiani;Tony Saboorian
3GPP envisions exploiting AI-Native for the day-to-day operations of 6G core networks (CNs). As opposed to 5G CNs whose uses for artificial intelligence (AI) are limited and not yet standardized, 6G CNs seek a revolutionary redesign where AI will no longer simply be an overlaid service but rather the foundation upon which all network functions run, i.e., AI-Native. By leveraging knowledge of the CN control plane, we utilize an AI framework to optimize our three proposed CN configurations and minimize the CN execution time which is imperative for end-to-end routing in the user plane.
{"title":"AI-Native for 6G Core Network Configuration","authors":"Abdullah Ridwan Hossain;Weiqi Liu;Nirwan Ansari;Abbas Kiani;Tony Saboorian","doi":"10.1109/LNET.2023.3302833","DOIUrl":"10.1109/LNET.2023.3302833","url":null,"abstract":"3GPP envisions exploiting AI-Native for the day-to-day operations of 6G core networks (CNs). As opposed to 5G CNs whose uses for artificial intelligence (AI) are limited and not yet standardized, 6G CNs seek a revolutionary redesign where AI will no longer simply be an overlaid service but rather the foundation upon which all network functions run, i.e., AI-Native. By leveraging knowledge of the CN control plane, we utilize an AI framework to optimize our three proposed CN configurations and minimize the CN execution time which is imperative for end-to-end routing in the user plane.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"255-259"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87314904","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}
Pub Date : 2023-08-02DOI: 10.1109/LNET.2023.3301008
Gaolei Fei;Zeyu Li;Yunpeng Zhou;Xuemeng Zhai;Jian Ye;Guangmin Hu
In this letter, we propose a novel network topology measurement method called the neural network gradient-guided method (NGM), which can use traceroutes to efficiently collect topological information (IPs and links) by selecting appropriate destination IPs. In this method, the mapping between probing destination IPs and probing revenues is modelled into a multiclassification problem using a neural network model, and networks are iteratively probed under the guidance of the neural network gradients. Experimental results in real networks demonstrated that NGM can collect more IPs and links by comparing with the methods that randomly selects IPs and subnets for probing.
在这封信中，我们提出了一种名为神经网络梯度引导法（NGM）的新型网络拓扑测量方法，该方法可通过选择适当的目标 IP，利用跟踪路由有效地收集拓扑信息（IP 和链接）。在这种方法中，探测目标 IP 与探测收入之间的映射被模拟成一个使用神经网络模型的多分类问题，并在神经网络梯度的引导下对网络进行迭代探测。真实网络的实验结果表明，与随机选择 IP 和子网进行探测的方法相比，NGM 可以收集更多的 IP 和链接。
{"title":"Efficiently Measure the Topologies of Large-Scale Networks Under the Guidance of Neural Network Gradients","authors":"Gaolei Fei;Zeyu Li;Yunpeng Zhou;Xuemeng Zhai;Jian Ye;Guangmin Hu","doi":"10.1109/LNET.2023.3301008","DOIUrl":"10.1109/LNET.2023.3301008","url":null,"abstract":"In this letter, we propose a novel network topology measurement method called the neural network gradient-guided method (NGM), which can use traceroutes to efficiently collect topological information (IPs and links) by selecting appropriate destination IPs. In this method, the mapping between probing destination IPs and probing revenues is modelled into a multiclassification problem using a neural network model, and networks are iteratively probed under the guidance of the neural network gradients. Experimental results in real networks demonstrated that NGM can collect more IPs and links by comparing with the methods that randomly selects IPs and subnets for probing.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"250-254"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86022898","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}
Pub Date : 2023-07-31DOI: 10.1109/LNET.2023.3300274
K. Bagadi;T. Abrao;F. Benedetto
This letter proposes a novel hybrid spectrum management scheme combining transfer actor-critic learning (TACT) and Q-learning algorithms to improve the cognitive radio access network’s spectrum efficiency. The TACT algorithm improves its mean opinion score over time, while the Q-learning achieves faster convergence during spectral management. Thus, this letter seeks to alleviate resource constraints by better exploiting unused communication channels. Computer simulations are carried out compared to reinforcement learning and conventional TACT algorithms. The results evidence the efficiency of our approach for intelligent spectrum management in cognitive radio networks.
{"title":"A Novel Machine Learning Approach for Intelligent Spectrum Management in Cognitive Radio Networks","authors":"K. Bagadi;T. Abrao;F. Benedetto","doi":"10.1109/LNET.2023.3300274","DOIUrl":"10.1109/LNET.2023.3300274","url":null,"abstract":"This letter proposes a novel hybrid spectrum management scheme combining transfer actor-critic learning (TACT) and Q-learning algorithms to improve the cognitive radio access network’s spectrum efficiency. The TACT algorithm improves its mean opinion score over time, while the Q-learning achieves faster convergence during spectral management. Thus, this letter seeks to alleviate resource constraints by better exploiting unused communication channels. Computer simulations are carried out compared to reinforcement learning and conventional TACT algorithms. The results evidence the efficiency of our approach for intelligent spectrum management in cognitive radio networks.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"232-236"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90821394","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}
Future wireless networks will support applications demanding high data-rates, ultra-low latency, and high reliabilities. One technology for such ultra-reliable low latency communication (URLLC) is grant-free access for uplink resources, which enables user equipment (UE) to transmit data over pre-allocated resources, reducing signaling overhead and communication latency. This letter proposes a novel ensemble Deep Reinforcement Learning grant-allocator architecture combining offline and online learning providing robust performance with a wide range of dynamic network and UE scenarios. Results show enhancement of the overall latency of UEs for URLLC applications achieving less than 20 Transmission Time Interval latency for 95% of the transmissions.
{"title":"Intelligent Resource Allocation for Grant-Free Access: A Reinforcement Learning Approach","authors":"Mariam Elsayem;Hatem Abou-Zeid;Ali Afana;Sidney Givigi","doi":"10.1109/LNET.2023.3299182","DOIUrl":"https://doi.org/10.1109/LNET.2023.3299182","url":null,"abstract":"Future wireless networks will support applications demanding high data-rates, ultra-low latency, and high reliabilities. One technology for such ultra-reliable low latency communication (URLLC) is grant-free access for uplink resources, which enables user equipment (UE) to transmit data over pre-allocated resources, reducing signaling overhead and communication latency. This letter proposes a novel ensemble Deep Reinforcement Learning grant-allocator architecture combining offline and online learning providing robust performance with a wide range of dynamic network and UE scenarios. Results show enhancement of the overall latency of UEs for URLLC applications achieving less than 20 Transmission Time Interval latency for 95% of the transmissions.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 3","pages":"154-158"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49979549","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}
Pub Date : 2023-07-25DOI: 10.1109/LNET.2023.3298775
Amir Masoud Aminian Modarres;Ghazaleh Sarbishaei
Almost all IoT applications require secure user authentication protocols. Due to the limitations of most IoT devices and the possibility of physical attacks, lightweight authentication protocols based on Physical Unclonable Functions (PUF) are widely used. To design an efficient scheme, it is helpful to learn weaknesses of existing protocols and provide guidelines for future protocol designers. In this letter, using a formal framework, we discuss the vulnerabilities of a recent PUF-based protocol proposed by Gope et al. We prove our claims concerning protocol vulnerabilities in this framework according to propositional logic. Moreover, we suggest countermeasures for improving the protocol’s security.
{"title":"Systematic Cryptanalysis of PUF-Based Authentication Protocols for IoT, A Case Study","authors":"Amir Masoud Aminian Modarres;Ghazaleh Sarbishaei","doi":"10.1109/LNET.2023.3298775","DOIUrl":"10.1109/LNET.2023.3298775","url":null,"abstract":"Almost all IoT applications require secure user authentication protocols. Due to the limitations of most IoT devices and the possibility of physical attacks, lightweight authentication protocols based on Physical Unclonable Functions (PUF) are widely used. To design an efficient scheme, it is helpful to learn weaknesses of existing protocols and provide guidelines for future protocol designers. In this letter, using a formal framework, we discuss the vulnerabilities of a recent PUF-based protocol proposed by Gope et al. We prove our claims concerning protocol vulnerabilities in this framework according to propositional logic. Moreover, we suggest countermeasures for improving the protocol’s security.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"5 4","pages":"304-308"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87811584","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}
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