Annals of Telecommunications最新文献_第9页

Publisher Correction: Towards programmable IoT with ActiveNDN 出版商更正：利用 ActiveNDN 实现可编程物联网

IF 1.9 4区计算机科学

Annals of Telecommunications Pub Date : 2023-08-19 DOI: 10.1007/s12243-023-00985-4

引用次数: 0

Automated slow-start detection for anomaly root cause analysis and BBR identification 自动慢速启动检测，用于异常根源分析和 BBR 识别

IF 1.8 4区计算机科学

Annals of Telecommunications Pub Date : 2023-08-18 DOI: 10.1007/s12243-023-00982-7

Ziad Tlaiss, Alexandre Ferrieux, Isabel Amigo, Isabelle Hamchaoui, Sandrine Vaton

{"title":"Automated slow-start detection for anomaly root cause analysis and BBR identification","authors":"Ziad Tlaiss, Alexandre Ferrieux, Isabel Amigo, Isabelle Hamchaoui, Sandrine Vaton","doi":"10.1007/s12243-023-00982-7","DOIUrl":"10.1007/s12243-023-00982-7","url":null,"abstract":"<div><p>Network troubleshooting usually requires packet level traffic capturing and analyzing. Indeed, the observation of emission patterns sheds some light on the kind of degradation experienced by a connection. In the case of reliable transport traffic where congestion control is performed, such as TCP and QUIC traffic, these patterns are the fruit of decisions made by the congestion control algorithm (CCA), according to its own perception of network conditions. The CCA estimates the bottleneck’s capacity via an exponential probing, during the so-called “Slow-Start” (SS) state. The bottleneck is considered reached upon reception of congestion signs, typically lost packets or abnormal packet delays depending on the version of CCA used. The SS state duration is thus a key indicator for the diagnosis of faults; this indicator is estimated empirically by human experts today, which is time-consuming and a cumbersome task with large error margins. This paper proposes a method to automatically identify the slow-start state from actively and passively obtained bidirectional packet traces. It relies on an innovative timeless representation of the observed packets series. We implemented our method in our active and passive probes and tested it with CUBIC and BBR under different network conditions. We then picked a few real-life examples to illustrate the value of our representation for easy discrimination between typical faults and for identifying BBR among CCAs variants.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 3-4","pages":"149 - 163"},"PeriodicalIF":1.8,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78189579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Generative AI in mobile networks: a survey 移动网络中的生成式人工智能：一项调查

IF 1.8 4区计算机科学

Annals of Telecommunications Pub Date : 2023-08-17 DOI: 10.1007/s12243-023-00980-9

Athanasios Karapantelakis, Pegah Alizadeh, Abdulrahman Alabassi, Kaushik Dey, Alexandros Nikou

引用次数: 0

A game-theoretical paradigm for collaborative and distributed power control in wireless networks 无线网络中协作和分布式功率控制的博弈论范式

IF 1.8 4区计算机科学

Annals of Telecommunications Pub Date : 2023-08-14 DOI: 10.1007/s12243-023-00976-5

Duc-Tuyen Ta, Nhan Nguyen-Thanh, Duy H. N. Nguyen, Van-Tam Nguyen

{"title":"A game-theoretical paradigm for collaborative and distributed power control in wireless networks","authors":"Duc-Tuyen Ta, Nhan Nguyen-Thanh, Duy H. N. Nguyen, Van-Tam Nguyen","doi":"10.1007/s12243-023-00976-5","DOIUrl":"10.1007/s12243-023-00976-5","url":null,"abstract":"<div><p>The wireless revolution requires future wireless networks the capability of intelligently optimizing the spectrum by collaborating and using autonomy to determine not just the best use of the spectrum for its own system, but the best use of spectrum for other systems that share the same spectrum bands. How to develop the wireless paradigm of collaboration, therefore, is a crucial question. In this paper, we discuss how to model collaborative power control in a wireless interference network, where users share the same frequency band. By collaborating with other users, each user exchanges information to maximize not only its own performance but also others’ performances. A game theory framework is developed to determine the optimal power allocation. The proposed framework possesses several advantages over conventional methods, such as low complexity and fast converging algorithmic solutions, distributed implementation, and better user fairness. Simulation results state the proposed approach provides better fairness between users’ data rates, higher performance in the aggregate rate, and lower convergence time.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 1-2","pages":"1 - 14"},"PeriodicalIF":1.8,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84874472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Cooperative localisation for multi-RSU vehicular networks based on predictive beamforming 基于预测波束成形的多 RSU 车辆网络协同定位

IF 1.8 4区计算机科学

Annals of Telecommunications Pub Date : 2023-08-11 DOI: 10.1007/s12243-023-00974-7

Changhong Yu, Zhong Ye, Yinghui He, Ming Gao, Haiyan Luo, Guanding Yu

{"title":"Cooperative localisation for multi-RSU vehicular networks based on predictive beamforming","authors":"Changhong Yu, Zhong Ye, Yinghui He, Ming Gao, Haiyan Luo, Guanding Yu","doi":"10.1007/s12243-023-00974-7","DOIUrl":"10.1007/s12243-023-00974-7","url":null,"abstract":"<div><p>The integration of sensing and communication has become essential to next-generation vehicular networks. In this paper, we investigate a vehicle-to-infrastructure (V2I) network with multiple roadside units (RSUs) based on the dual-functional radar-communication (DFRC) technique. Since there are multiple RSUs in the system, we first propose a signal-switching model between vehicles and different RSUs. These RSUs estimate and predict vehicles’ motion parameters based on the DFRC signal echoes and the state evolution model. Accordingly, we utilise a neural network to extract angle information from signal echoes instead of traditional methods, thus improving the angle estimation accuracy. To further improve the estimation performance, we formulate an optimisation problem to minimise the Cramer-Rao bound (CRB) on angle estimation by properly allocating power to each RSU. Finally, we propose a novel weighting method to further improve the cooperative localisation accuracy of the multi-RSU system. Simulation results show that the performance of angle estimation can be improved by utilising the proposed neural network method and the novel power allocation scheme. In addition, the novel weighting method can considerably improve the localisation accuracy.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 1-2","pages":"85 - 100"},"PeriodicalIF":1.8,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87646195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

A low-complexity iterative algorithm for multiuser millimeter-wave systems 多iuser 毫米波系统的低复杂度迭代算法

IF 1.8 4区计算机科学

Annals of Telecommunications Pub Date : 2023-08-02 DOI: 10.1007/s12243-023-00979-2

Mustafa Mulla, Ali Hakan Ulusoy, Ahmet Rizaner, Hasan Amca

引用次数: 0

Publisher Correction: Introduction to the special issue: 5+G network energy consumption, energy efficiency and environmental impact 出版商更正：特刊简介：5+G网络能耗、能效与环境影响

IF 1.9 4区计算机科学

Annals of Telecommunications Pub Date : 2023-08-01 DOI: 10.1007/s12243-023-00978-3

引用次数: 0

A dense memory representation using bitmap data structure for improving NDN push-traffic model 使用位图数据结构的密集内存表示法，用于改进 NDN 推送流量模型

IF 1.8 4区计算机科学

Annals of Telecommunications Pub Date : 2023-07-24 DOI: 10.1007/s12243-023-00972-9

Amer Sallam, Noran Aklan, Norhan Aklan, Taha H. Rassem

{"title":"A dense memory representation using bitmap data structure for improving NDN push-traffic model","authors":"Amer Sallam, Noran Aklan, Norhan Aklan, Taha H. Rassem","doi":"10.1007/s12243-023-00972-9","DOIUrl":"10.1007/s12243-023-00972-9","url":null,"abstract":"<div><p>The exponential growth of the Internet demands in return new technologies and protocols that can handle the new requirements of such growth efficiently. Such developments have enabled and offered many new services with sophisticated requirements that go beyond the TCP/IP host-centric model capabilities and increase its complexity. Researchers have proposed new architecture called Named-Data Networking (NDN) for Information-Centric Networking (ICN) based on a strict pull-based model as an alternative option to TCP/IP. This model has gained significant attention in the research field. However, this model still suffers from the looped data redundancy problem, which may lead to frequent link failures when dealing with real-time streaming due to the persistent interest packets. In this paper, a push-based model along with a bitmap algorithm has been proposed for improving the ICN efficiency by eliminating such problems. The presented model involved extensive experimental simulations. The experimental results demonstrate the model feasibility by preventing most of the data redundancy and improving the harmonic rein of frequent link failures respectively.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"79 1-2","pages":"73 - 83"},"PeriodicalIF":1.8,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78769507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

klm-PPSA v. 1.1: machine learning-augmented profiling and preventing security attacks in cloud environments klm-PPSA v. 1.1:机器学习增强分析和防止云环境中的安全攻击

IF 1.9 4区计算机科学

Annals of Telecommunications Pub Date : 2023-07-17 DOI: 10.1007/s12243-023-00971-w

Nahid Eddermoug, Abdeljebar Mansour, Mohamed Sadik, Essaid Sabir, Mohamed Azmi

{"title":"klm-PPSA v. 1.1: machine learning-augmented profiling and preventing security attacks in cloud environments","authors":"Nahid Eddermoug, Abdeljebar Mansour, Mohamed Sadik, Essaid Sabir, Mohamed Azmi","doi":"10.1007/s12243-023-00971-w","DOIUrl":"10.1007/s12243-023-00971-w","url":null,"abstract":"<div><p>Nowadays, cloud computing is one of the key enablers for productivity in different domains. However, this technology is still subject to security attacks. This article aims at overcoming the limitations of detecting unknown attacks by “intrusion detection and prevention systems (IDPSs)” while addressing the black-box issue (lack of interpretability) of the widely used machine learning (ML) models in cybersecurity. We propose a “<i>klm</i>-based profiling and preventing security attacks (<i>klm</i>-PPSA)” system (v. 1.1) to detect, profile, and prevent both known and unknown security attacks in cloud environments or even cloud-based IoT. This system is based on <i>klm</i> security factors related to passwords, biometrics, and keystroke techniques. Besides, two sub-schemes of the system were developed based on the updated and improved version of the <i>klm</i>-PPSA scheme (v. 1.1) to analyze the impact of these factors on the performance of the generated models (<i>k</i>-PPSA, <i>km</i>-PPSA, and <i>klm</i>-PPSA). The models were built using two accurate and interpretable ML algorithms: regularized class association rules (RCAR) and classification based on associations (CBA). The empirical results show that <i>klm</i>-PPSA is the best model compared to other models owing to its high performance and attack prediction capability using RCAR/CBA. In addition, RCAR performs better than CBA.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"78 11-12","pages":"729 - 755"},"PeriodicalIF":1.9,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81833440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2

Building Lightweight Deep learning Models with TensorFlow Lite for Human Activity Recognition on Mobile Devices 使用TensorFlow Lite构建轻量级深度学习模型，用于移动设备上的人类活动识别

IF 1.9 4区计算机科学

Annals of Telecommunications Pub Date : 2023-07-15 DOI: 10.1007/s12243-023-00962-x

Sevda Özge Bursa, Özlem Durmaz İncel, Gülfem Işıklar Alptekin

{"title":"Building Lightweight Deep learning Models with TensorFlow Lite for Human Activity Recognition on Mobile Devices","authors":"Sevda Özge Bursa, Özlem Durmaz İncel, Gülfem Işıklar Alptekin","doi":"10.1007/s12243-023-00962-x","DOIUrl":"10.1007/s12243-023-00962-x","url":null,"abstract":"<div><p>Human activity recognition (HAR) is a research domain that enables continuous monitoring of human behaviors for various purposes, from assisted living to surveillance in smart home environments. These applications generally work with a rich collection of sensor data generated using smartphones and other low-power wearable devices. The amount of collected data can quickly become immense, necessitating time and resource-consuming computations. Deep learning (DL) has recently become a promising trend in HAR. However, it is challenging to train and run DL algorithms on mobile devices due to their limited battery power, memory, and computation units. In this paper, we evaluate and compare the performance of four different deep architectures trained on three datasets from the HAR literature (WISDM, MobiAct, OpenHAR). We use the TensorFlow Lite platform with quantization techniques to convert the models into lighter versions for deployment on mobile devices. We compare the performance of the original models in terms of accuracy, size, and resource usage with their optimized versions. The experiments reveal that the model size and resource consumption can significantly be reduced when optimized with TensorFlow Lite without sacrificing the accuracy of the models.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"78 11-12","pages":"687 - 702"},"PeriodicalIF":1.9,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87740879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1