Quality of Service (QoS) Management for Local Area Network (LAN) Using Traffic Policy Technique to Secure Congestion

Azamuddin, Wan Muhd Hazwan; Hassan, Rosilah; Aman, Azana Hafizah Mohd; Hasan, Mohammad Kamrul; Al-Khaleefa, Ahmed Salih

doi:10.3390/computers9020039

Cited by 17 publications

(13 citation statements)

References 16 publications

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“…The DVAEGMM objective function O(W) is generated as follows for a dataset of N samples: (26) This objective function includes the following components: Produce the structural space node embedding Z S via Eq. ( 12); 5.…”

Section: Objective Functionmentioning

confidence: 99%

“…Furthermore, the description of anomalies varies across fields, indicating that there is no generally agreed anomaly definition. Furthermore, the description of anomalies varies across fields, indicating that there is no universally accepted definition of an anomaly [25], [26]. So, it's important to deal with the following challenges: 1) Data nonlinearity and network sparsity.…”

Section: Introductionmentioning

confidence: 99%

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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

et al. 2022

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A significant aspect of today's digital information is attributed networks, which combine multiple node attributes with the basic network topology in order to extract knowledge. Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in a number of high-impact areas. The majority of current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method. These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. However, they suffer from the problem of ignoring the latent codes' embedding distribution, which results in poor representation in many instances. In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, similar to vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. An adversarial mechanism makes the encoder make more accurate estimates of how potential features might be distributed. As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. Lastly, the Gaussian Mixture Model, a distinct estimation network, is used to approximate the latent vector density, resulting in the detection of anomalies from measuring sample energy. They are trained jointly as an end-to-end framework. DVAEGMM helps in the simultaneous optimization of the mixture model, generative adversarial network, and variational autoencoder parameters. The joint optimization balances the reconstruction probability, the latent representation density approximation, and regularization. Extensive experiments on attributed networks prove that DVAEGMM significantly beats the existing methods, proving the efficiency of the presented approach. The AUC scores of our proposed framework for the BlogCatalog, Flickr, Enron, and Amazon datasets are 0.89380, 0.87130, 0.72480, and 0.75102, respectively.

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Section: Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

et al. 2022

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“…In [29], an explanation of all three heating type models has been carried out that have been expressed in Eqn. ( 3), ( 4) and (5).…”

Section: Existing Ieee and Cigri Standardmentioning

confidence: 99%

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

et al. 2021

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Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.INDEX TERMS Dynamic thermal current ratings, data acquisition system, information technology, phasor measurement unit, reliability, sensors, sensor cloud system, wide area measurement. NOMENCLATURE ABBREVIATIONS ACSR Aluminum Conductor Steel Rectifier CTE Conductor Temperature EvaluationThe associate editor coordinating the review of this manuscript and approving it for publication was Eyuphan Bulut .

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“…QoS is the most significant issue [12]. QoS is ideal to apply on the customer side rather than on the provider side [13].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Power Support Internet Infrastructure of Makassar City in Telemedicine Frame

Muhammad,

Achmad,

Adnan

et al. 2024

jiko

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This research aims to find the quality of the internet in Makassar City. It uses a 10 Mbps service from Indihome to support telemedicine. The study is a case study of sending raw MRI image data to the AWS cloud. The research uses a virtual server from the AWS cloud. It stores raw MRI image data. The data will be sent via the FTP client FileZilla. The tests were carried out eight times. They used the quality of service standard formula from TIPHON. The results come from 8 tests. In the tests, MRI image data was sent to the AWS cloud. The results show that the average throughput value was 4.53 Mbps with an index of 4. This result is excellent. Packet loss is low at 0.01% with an index of 4, which is very good. The delay is 1.7 ms with an index of 3, which is good. The jitter is 1.69 ms with an index of 3, which is good. The quality of service test results are based on TIPHON standards. They show that sending Raw MRI image data to the AWS cloud at 10 Mbps from Indihome in Makassar City is good.

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Quality of Service (QoS) Management for Local Area Network (LAN) Using Traffic Policy Technique to Secure Congestion

Cited by 17 publications

References 16 publications

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

An Evaluation of the Power Support Internet Infrastructure of Makassar City in Telemedicine Frame

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