Data-Filtering System to Avoid Total Data Distortion in IoT Networking

Kim, Dae-Young; Jeong, Young‐Sik; Kim, Seokhoon

doi:10.3390/sym9010016

Cited by 27 publications

(13 citation statements)

References 17 publications

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“…The inference engine employs Naïve Bayesian Classifier. The Naïve Bayesian Classifier is based on the Bayes rule and it is a widely used supervised learning algorithm (supervised learning is widely applied to wireless networks to estimate the variance of wireless resources and network environment [17][18][19][20][21][22][23]). It is used to estimate the most possible state from probability by the a priori statistic information.…”

Section: Wifi Status Estimation In the Inference Enginementioning

confidence: 99%

“…If 3 is stable then (6) If > then 7Find another WiFi (8) End if (9) Else (10) If > then (11) Find another WiFi and then migrate to the WiFi (12) End if (13) End if (14) Else if 2 is low then (15) If 3 is stable then (16) If ≤ then (17) Use cellular and WiFi simultaneously until the buffer meets THRD_M (18) Else (19) Use cellular and WiFi simultaneously until the buffer meets THRD_H (20) Find another WiFi and then migrate to the WiFi (21) End if (22) Else (23) If ≤ then (24) Use cellular and WiFi simultaneously until the buffer meets THRD_H (25) Find buffer, when is less than or equal to , mobile terminals simultaneously use both cellular and WiFi until the amount of data in the buffer satisfies THRD_H and then the mobile terminals search for another WiFi and migrates to the WiFi network. When is greater than , the mobile terminal only uses cellular network or finds another WiFi network to migrate to it.…”

Section: Wireless Network Selection In the Decision Enginementioning

confidence: 99%

“…If 3 is unstable then (6) Find another WiFi 7End if (8) Else if 2 is normal then (9) If 3 is stable then (10) If ≤ then (11) Find another WiFi (12) Else (13) Find another WiFi and then migrate to the WiFi (14) End if (15) Else (16) If ≤ then (17) Use cellular and WiFi simultaneously until the buffer meets THRD_M (18) Else (19) Use cellular and WiFi simultaneously until the buffer meets THRD_H (20) End if (21) End if (22) Else if 2 is low then (23) If 3 is stable then (24) If ≤ then (25) Use…”

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Network Access Control for Location-Based Mobile Services in Heterogeneous Wireless Networks

Kim

2017

Mobile Information Systems

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Recent advances in information communication technology and software have enabled mobile terminals to employ various capabilities as a smartphone. They adopt multiple interfaces for wireless communication and run as a portable computer. Mobile services are also transferred from voice to data. Mobile terminals can access Internet for data services anytime anywhere. By using location-based information, improved mobile services are enabled in heterogeneous networks. In the mobile service environment, it is required that mobile terminals should efficiently use wireless network resources. In addition, because video stream becomes a major service among the data services of mobile terminals in heterogeneous networks, the necessity of the efficient network access control for heterogeneous wireless networks is raised as an important topic. That is, quality of services of the location-based video stream is determined by the network access control. Therefore, this paper proposes a novel network access control in the heterogeneous wireless networks. The proposed method estimates the network status with Naïve Bayesian Classifier and performs network access control according to the estimated network status. Thus, it improves data transmission efficiency to satisfy the quality of services. The efficiency of the proposed method is validated through the extensive computer simulation.

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Section: Wifi Status Estimation In the Inference Enginementioning

confidence: 99%

Section: Wireless Network Selection In the Decision Enginementioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Network Access Control for Location-Based Mobile Services in Heterogeneous Wireless Networks

Kim

2017

Mobile Information Systems

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“…The Hong Kong MTR is equipped with full‐time staff for routine maintenance and inspection of test results [29,30]. Refs [24,31,32] indicate that due to the special operating environment of the subway, the underground structure facilities are inevitably affected by factors such as pollution, moisture, water seepage, water leakage, and high ground stress, which may cause date loss and distortion during data transmission. Therefore, the screening of abnormal points in the spurious monitoring data is becoming an urgent problem in the data processing.…”

Section: Introductionmentioning

confidence: 99%

A New On‐line Monitoring Method for Stray Current ofDCMetro System

Peng

Zhang

Leng

et al. 2020

IEEJ Transactions Elec Engng

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In order to solve the problem that the metro stray current cannot be directly numerically measured by the existing methods, and the data acquisition distortion caused by the special operating environment of the metro system, a new on‐line monitoring method for stray current was put forward in this paper. First, the stray current distribution function at different positions of metro trains was derived. Then, the equivalent circuit of stray current was built according to the field measured environment, and the numerical calculation formula of stray current was deduced. On the basis of the formula, the metro stray current could be calculated by the real‐time monitoring data, and the calculated data can be filtered and fitted with the stray current distribution function. In order to identify the distorted data, the potential energy function was introduced to judge the deviation degree of data point from the fitting function, so the erroneous data can be effectively eliminated. Thus, the accurate distribution value of stray current along the metro track was obtained. Taking a metro station in a certain city as the prototype, the CDEGS simulation model was built to verify the proposed method. The simulation results showed that even under the condition of sampling data distortion, the method could directly calculate the stray current value in real time, which had strong fault tolerance. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…A significant amount of research has been conducted to develop efficient intrusion detection systems using various techniques such as statistical, soft computing, combination learners, and the methods based on classification, knowledge, clustering, and so on [2][3][4][5][6]. However, in the past few years, the exponential growth of massive data in network domain has posed many challenges to researchers in the field [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

et al. 2017

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Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system's performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i) performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii) employs logistic regression and extreme gradient boosting techniques for classification; (iii) introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv) uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

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Data-Filtering System to Avoid Total Data Distortion in IoT Networking

Cited by 27 publications

References 17 publications

Network Access Control for Location-Based Mobile Services in Heterogeneous Wireless Networks

Network Access Control for Location-Based Mobile Services in Heterogeneous Wireless Networks

A New On‐line Monitoring Method for Stray Current ofDCMetro System

Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

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