Network functions virtualization for flexible deployment of converged optical-wireless access infrastructure

Krasko, Olena; Al-Zayadi, Haider; Pashkevych, Volodymyr; Копець, Г. Р.; Humeniuk, Bohdan

doi:10.1109/tcset.2018.8336394

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…IoT technology helps to create a more sophisticated virtual world by recording the real world more closely. Therefore, IoT technology is the next generation tool that transforms most of our everyday life and industry [1][2][3][4][5][6]. IoT is defined as a global network with an infrastructure that has self-configuring capabilities [7].…”

Section: Introductionmentioning

confidence: 99%

Improving data quality using a deep learning network

Hwang

Lee

Jung

2020

IJEECS

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<p>IoT data is collected in real time and is treated as highly reliable data because of its high precision. However, it often exhibits incomplete values for reasons such as sensor aging and failure, poor operating environment, and communication problems. The characteristics of IoT data transmitted with high precision and time series are suitable to use LSTM, which is one kind of RNN. In this paper, when applying LSTM to data quality improvement in IoT environment where data are collected simultaneously from several sensors, it is suggested that it is effective to construct LSTM individually for each sensor accuracy.</p>

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Section: Introductionmentioning

confidence: 99%

Improving data quality using a deep learning network

Hwang

Lee

Jung

2020

IJEECS

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The WEAF Mnecosystem: a perspective of MEMS/NEMS technologies as pillars of future 6G, tactile internet and super-IoT

Iannacci

2021

Microsyst Technol

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Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks

Chen

Wei³

et al. 2019

IEEE Access

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Network function virtualization (NFV) is a promising network paradigm that enables the design and implementation of novel network services with lower cost, increased agility, and faster time-to-value. However, network anomalies caused by software malfunction, hardware failure, mis-configuration, or cyber attacks can greatly degrade the performance of NFV networks. A few matrix decomposition-based methods have shown their effectiveness in finding the existence of network-wide anomalies. However, a little attention has been paid to multiple anomalies detection and anomaly devices localization. To bridge this gap, in this paper, we propose a matrix differential decomposition (MDD)-based anomaly detection and localization algorithm for NFV networks. First, an NFV network prototype is built to investigate the property of NFV networks, and the effectiveness of traditional anomaly detection methods is evaluated. Second, we detail the MDD-based Anomaly DEtection and Localization (MADEL) algorithm. Finally, a series of experiments are conducted on three different NFV networks to evaluate the performance of the proposed algorithm. Experimental results show that the MADEL algorithm could effectively detect and localize different types of network anomalies.

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Network functions virtualization for flexible deployment of converged optical-wireless access infrastructure

Cited by 7 publications

References 27 publications

Improving data quality using a deep learning network

Improving data quality using a deep learning network

The WEAF Mnecosystem: a perspective of MEMS/NEMS technologies as pillars of future 6G, tactile internet and super-IoT

Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks

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