The Experts below are selected from a list of 102 Experts worldwide ranked by ideXlab platform
Bin Lu - One of the best experts on this subject based on the ideXlab platform.
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ICII - A Hilbert-Huang Transform-Based Traffic Estimation Algorithm to Power Line Communications
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Haiyang Cong, Bin Lu, Ran Li, Yi Lu, Dongdong Wang, Diying Wu, Taiyi FuAbstract:Network Traffic is significantly difficult be correctly estimated and predicted in power line communications because Network Traffic has obvious dynamic features. How to accurately estimate and predict Network Traffic in power communication Networks is very significant for power scheduling. This paper studies the Network Traffic estimation problem in power line telecommunications and proposes a new estimation method to accurately forecast power communication Network Traffic. Firstly, we use the Hibert-Huang transform theory to Capture Network Traffic features in power line communications. Secondly, we construct the Hibert-Huang transform estimation model about Network Traffic in power line communications to forecast dynamic Network Traffic. Thirdly, we propose a new Traffic estimation algorithm to estimate Network Traffic. Simulation results show that our approach is feasible.
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ICII - A STFT-Based Traffic Acquirement Algorithm to Remote Smart Managements of Optical Fiber Cores
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Fanbo Meng, Bin Lu, Huan Li, Jianhong Kong, Zhibin YangAbstract:Network Traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, Network operators need to perform the accurate and real-time acquirement for Network Traffic. However, to quickly and accurately estimate Network Traffic is an open problem in current communication Networks including power communication Networks. This paper propose an accurate acquirement approach to obtain Network Traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end Network Traffic in power telecommunications. Secondly, based on the STFT method, Network Traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model Network Traffic. The corresponding model method is used to construct and Capture Network Traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain Network Traffic. Simulation results show that our approach is promising.
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SimuTools - A Linear Regression-Based Prediction Method to Traffic Flow for Low-Power WAN with Smart Electric Power Allocations
Simulation Tools and Techniques, 2019Co-Authors: Fanbo Meng, Yun Zhao, Xinge Qi, Bin Lu, Kai YangAbstract:Currently power telecommunication access Networks have many new requirements to meet the low-power WAN with smart electric power allocations. In such a case, Network Traffic in the low-power WAN has exhibited new features and there are some challenges for Network managements. This paper uses the linear regression model to propose a new method to model and predict Network Traffic. Firstly, Network Traffic is modeled as a linear regression model according to the regression model theory. Then the linear regression modeling method is used to Capture Network Traffic features. By calculating the parameters of the model, it can be decided correctly. Then, we can predict Network Traffic accurately. Simulation results show that our approach is effective and promising.
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A Hilbert-Huang Transform-Based Traffic Estimation Algorithm to Power Line Communications
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Haiyang Cong, Bin Lu, Ran Li, Yi Lu, Dongdong Wang, Diying Wu, Taiyi FuAbstract:Network Traffic is significantly difficult be correctly estimated and predicted in power line communications because Network Traffic has obvious dynamic features. How to accurately estimate and predict Network Traffic in power communication Networks is very significant for power scheduling. This paper studies the Network Traffic estimation problem in power line telecommunications and proposes a new estimation method to accurately forecast power communication Network Traffic. Firstly, we use the Hibert-Huang transform theory to Capture Network Traffic features in power line communications. Secondly, we construct the Hibert-Huang transform estimation model about Network Traffic in power line communications to forecast dynamic Network Traffic. Thirdly, we propose a new Traffic estimation algorithm to estimate Network Traffic. Simulation results show that our approach is feasible.
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A STFT-Based Traffic Acquirement Algorithm to Remote Smart Managements of Optical Fiber Cores
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Fanbo Meng, Bin Lu, Huan Li, Jianhong Kong, Zhibin YangAbstract:Network Traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, Network operators need to perform the accurate and real-time acquirement for Network Traffic. However, to quickly and accurately estimate Network Traffic is an open problem in current communication Networks including power communication Networks. This paper propose an accurate acquirement approach to obtain Network Traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end Network Traffic in power telecommunications. Secondly, based on the STFT method, Network Traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model Network Traffic. The corresponding model method is used to construct and Capture Network Traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain Network Traffic. Simulation results show that our approach is promising.
Na Ruan - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Who Moved My Cheese: Towards Automatic and Fine-Grained Classification and Modeling Ad Network
2016 IEEE Global Communications Conference (GLOBECOM), 2020Co-Authors: Huaxin Li, Na Ruan, Di MaAbstract:The mobile advertisement (ad) Network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad Network primarily focus on the privacy, permission and malware detection while less attention has been paid to the Traffic consumption issue incurred by ad Network. Though it is well known that ad Network plays an important role in Network consumption, it represents a great challenge of giving a fine-grained classification of ad Networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the Network consumption of Ad Network in Android. We develop an automatic ad analysis platform to quantify the ad Network Traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of Network Traffic. On one hand, we modify the android webview and log system in system level to Capture Network Traffic accurately. On the other hand, we Capture Network Traffic in router level to collect detailed information of Traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad Traffic takes major percentage of the whole Network Traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more Network Traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.
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Who Moved My Cheese: Towards Automatic and Fine-Grained Classification and Modeling Ad Network
2016 IEEE Global Communications Conference (GLOBECOM), 2016Co-Authors: Huaxin Li, Na RuanAbstract:The mobile advertisement (ad) Network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad Network primarily focus on the privacy, permission and malware detection while less attention has been paid to the Traffic consumption issue incurred by ad Network. Though it is well known that ad Network plays an important role in Network consumption, it represents a great challenge of giving a fine-grained classification of ad Networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the Network consumption of Ad Network in Android. We develop an automatic ad analysis platform to quantify the ad Network Traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of Network Traffic. On one hand, we modify the android webview and log system in system level to Capture Network Traffic accurately. On the other hand, we Capture Network Traffic in router level to collect detailed information of Traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad Traffic takes major percentage of the whole Network Traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more Network Traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.
Zhibin Yang - One of the best experts on this subject based on the ideXlab platform.
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ICII - A STFT-Based Traffic Acquirement Algorithm to Remote Smart Managements of Optical Fiber Cores
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Fanbo Meng, Bin Lu, Huan Li, Jianhong Kong, Zhibin YangAbstract:Network Traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, Network operators need to perform the accurate and real-time acquirement for Network Traffic. However, to quickly and accurately estimate Network Traffic is an open problem in current communication Networks including power communication Networks. This paper propose an accurate acquirement approach to obtain Network Traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end Network Traffic in power telecommunications. Secondly, based on the STFT method, Network Traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model Network Traffic. The corresponding model method is used to construct and Capture Network Traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain Network Traffic. Simulation results show that our approach is promising.
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A STFT-Based Traffic Acquirement Algorithm to Remote Smart Managements of Optical Fiber Cores
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Fanbo Meng, Bin Lu, Huan Li, Jianhong Kong, Zhibin YangAbstract:Network Traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, Network operators need to perform the accurate and real-time acquirement for Network Traffic. However, to quickly and accurately estimate Network Traffic is an open problem in current communication Networks including power communication Networks. This paper propose an accurate acquirement approach to obtain Network Traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end Network Traffic in power telecommunications. Secondly, based on the STFT method, Network Traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model Network Traffic. The corresponding model method is used to construct and Capture Network Traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain Network Traffic. Simulation results show that our approach is promising.
Huaxin Li - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Who Moved My Cheese: Towards Automatic and Fine-Grained Classification and Modeling Ad Network
2016 IEEE Global Communications Conference (GLOBECOM), 2020Co-Authors: Huaxin Li, Na Ruan, Di MaAbstract:The mobile advertisement (ad) Network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad Network primarily focus on the privacy, permission and malware detection while less attention has been paid to the Traffic consumption issue incurred by ad Network. Though it is well known that ad Network plays an important role in Network consumption, it represents a great challenge of giving a fine-grained classification of ad Networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the Network consumption of Ad Network in Android. We develop an automatic ad analysis platform to quantify the ad Network Traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of Network Traffic. On one hand, we modify the android webview and log system in system level to Capture Network Traffic accurately. On the other hand, we Capture Network Traffic in router level to collect detailed information of Traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad Traffic takes major percentage of the whole Network Traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more Network Traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.
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Who Moved My Cheese: Towards Automatic and Fine-Grained Classification and Modeling Ad Network
2016 IEEE Global Communications Conference (GLOBECOM), 2016Co-Authors: Huaxin Li, Na RuanAbstract:The mobile advertisement (ad) Network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad Network primarily focus on the privacy, permission and malware detection while less attention has been paid to the Traffic consumption issue incurred by ad Network. Though it is well known that ad Network plays an important role in Network consumption, it represents a great challenge of giving a fine-grained classification of ad Networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the Network consumption of Ad Network in Android. We develop an automatic ad analysis platform to quantify the ad Network Traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of Network Traffic. On one hand, we modify the android webview and log system in system level to Capture Network Traffic accurately. On the other hand, we Capture Network Traffic in router level to collect detailed information of Traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad Traffic takes major percentage of the whole Network Traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more Network Traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.
Fanbo Meng - One of the best experts on this subject based on the ideXlab platform.
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ICII - A STFT-Based Traffic Acquirement Algorithm to Remote Smart Managements of Optical Fiber Cores
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Fanbo Meng, Bin Lu, Huan Li, Jianhong Kong, Zhibin YangAbstract:Network Traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, Network operators need to perform the accurate and real-time acquirement for Network Traffic. However, to quickly and accurately estimate Network Traffic is an open problem in current communication Networks including power communication Networks. This paper propose an accurate acquirement approach to obtain Network Traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end Network Traffic in power telecommunications. Secondly, based on the STFT method, Network Traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model Network Traffic. The corresponding model method is used to construct and Capture Network Traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain Network Traffic. Simulation results show that our approach is promising.
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SimuTools - A Linear Regression-Based Prediction Method to Traffic Flow for Low-Power WAN with Smart Electric Power Allocations
Simulation Tools and Techniques, 2019Co-Authors: Fanbo Meng, Yun Zhao, Xinge Qi, Bin Lu, Kai YangAbstract:Currently power telecommunication access Networks have many new requirements to meet the low-power WAN with smart electric power allocations. In such a case, Network Traffic in the low-power WAN has exhibited new features and there are some challenges for Network managements. This paper uses the linear regression model to propose a new method to model and predict Network Traffic. Firstly, Network Traffic is modeled as a linear regression model according to the regression model theory. Then the linear regression modeling method is used to Capture Network Traffic features. By calculating the parameters of the model, it can be decided correctly. Then, we can predict Network Traffic accurately. Simulation results show that our approach is effective and promising.
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A STFT-Based Traffic Acquirement Algorithm to Remote Smart Managements of Optical Fiber Cores
2019 IEEE International Conference on Industrial Internet (ICII), 2019Co-Authors: Fanbo Meng, Bin Lu, Huan Li, Jianhong Kong, Zhibin YangAbstract:Network Traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, Network operators need to perform the accurate and real-time acquirement for Network Traffic. However, to quickly and accurately estimate Network Traffic is an open problem in current communication Networks including power communication Networks. This paper propose an accurate acquirement approach to obtain Network Traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end Network Traffic in power telecommunications. Secondly, based on the STFT method, Network Traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model Network Traffic. The corresponding model method is used to construct and Capture Network Traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain Network Traffic. Simulation results show that our approach is promising.