Annals of Telecommunications最新文献_第9页

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

IF 1.9 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, D. Nguyen, Van-Tam Nguyen

引用次数: 0

Cooperative localisation for multi-RSU vehicular networks based on predictive beamforming 基于预测波束形成的多rsu车辆网络协同定位

IF 1.9 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

引用次数: 0

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

IF 1.9 4区计算机科学

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

Mustafa Mulla, A. H. Ulusoy, A. Rizaner, H. 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 一种基于位图数据结构的密集内存表示，用于改进NDN推送流量模型

IF 1.9 4区计算机科学

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

A. Sallam, N. Aklan, Norhan Aklan, Taha H. Rassem

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":null,"pages":null},"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":null,"pages":null},"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

Adaptive password guessing: learning language, nationality and dataset source 自适应密码猜测：学习语言、国籍和数据集来源

IF 1.9 4区计算机科学

Annals of Telecommunications Pub Date : 2023-07-11 DOI: 10.1007/s12243-023-00969-4

Hazel Murray, David Malone

引用次数: 0

Towards programmable IoT with ActiveNDN 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 : 2023-07-04 DOI: 10.1007/s12243-023-00954-x

Preechai Mekbungwan, Adisorn Lertsinsrubtavee, Sukumal Kitisin, Giovanni Pau, Kanchana Kanchanasut

{"title":"Towards programmable IoT with ActiveNDN","authors":"Preechai Mekbungwan, Adisorn Lertsinsrubtavee, Sukumal Kitisin, Giovanni Pau, Kanchana Kanchanasut","doi":"10.1007/s12243-023-00954-x","DOIUrl":"10.1007/s12243-023-00954-x","url":null,"abstract":"<div><p>We propose to perform robust distributed computation, such as analysing and filtering raw data in real time, as close as possible to sensors in an environment with intermittent Internet connectivity and resource-constrained computable IoT nodes. To enable this computation, we deploy a named data network (NDN) for IoT applications, which allows data to be referenced by names. The novelty of our work lies in the inclusion of computation functions in each NDN router and allowing functions to be treated as executable Data objects. Function call is expressed as part of the NDN Interest names with proper name prefixes for NDN routing. With the results of the function computation returned as NDN Data packets, a normal NDN is converted to an ActiveNDN node. Distributed function executions can be orchestrated by an ActiveNDN program to perform required computations in the network. In this paper, we describe the design of ActiveNDN with a small prototype network as a proof of concept. We conduct extensive simulation experiments to investigate the performance and effectiveness of ActiveNDN in large-scale wireless IoT networks. Two programmable IoT air quality monitoring applications on our real-world ActiveNDN testbed are described, demonstrating that programmable IoT devices with on-site execution are capable of handling increasingly complex and time-sensitive IoT scenarios.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85671563","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

Deep unfolding for energy-efficient resource allocation in mmWave networks with multi-connectivity 多连通毫米波网络中高效节能资源分配的深度展开

IF 1.9 4区计算机科学

Annals of Telecommunications Pub Date : 2023-07-01 DOI: 10.1007/s12243-023-00970-x

Pan Chongrui, Yu Guanding

引用次数: 0