The Experts below are selected from a list of 5241 Experts worldwide ranked by ideXlab platform

Yi Qian - One of the best experts on this subject based on the ideXlab platform.

  • datanet deep learning based encrypted network traffic classification in sdn Home Gateway
    IEEE Access, 2018
    Co-Authors: Pan Wang, Xuejiao Chen, Yi Qian
    Abstract:

    A smart Home network will support various smart devices and applications, e.g., Home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart Home access the Internet through a Home Gateway (HGW). In this paper, we propose a software-defined-network (SDN)-HGW framework to better manage distributed smart Home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart Homes. Our proposed SDN-HGW extends the control to the access network, i.e., a smart Home network, for better end-to-end network management. Specifically, the proposed SDN-HGW can achieve distributed application awareness by classifying data traffic in a smart Home network. Most existing traffic classification solutions, e.g., deep packet inspection, cannot provide real-time application awareness for encrypted data traffic. To tackle those issues, we develop encrypted data classifiers (denoted as DataNets) based on three deep learning schemes, i.e., multilayer perceptron, stacked autoencoder, and convolutional neural networks, using an open data set that has over 200 000 encrypted data samples from 15 applications. A data preprocessing scheme is proposed to process raw data packets and the tested data set so that DataNet can be created. The experimental results show that the developed DataNets can be applied to enable distributed application-aware SDN-HGW in future smart Home networks.

Hussein T Mouftah - One of the best experts on this subject based on the ideXlab platform.

  • management of phev batteries in the smart grid towards a cyber physical power infrastructure
    International Conference on Wireless Communications and Mobile Computing, 2011
    Co-Authors: Melike Erolkantarci, Hussein T Mouftah
    Abstract:

    Information and Communication Technologies (ICT) are playing a key role in converting the traditional power grid into a smart power grid, and hence, they provide a number of opportunities to develop novel applications for the new cyber-physical power infrastructure. Interconnection of the smart appliances, consumer devices, Plug-In Hybrid Electric Vehicles (PHEV) and local renewable energy generation resources with the smart grid enables energy and demand management for the cyber-physical power infrastructure. In this paper, we employ a Home Gateway and Controller (HGC) device that communicates with the PHEV and controls its charging and discharging profile. HGC can also communicate with the controller of the solar power generation unit in the smart Home, and it can schedule the consumption of the smart appliances accordingly. Moreover, since PHEVs draw large amount of electricity, simultaneous charging in a neighborhood can overload the utility transformers in the distribution substations and risk the resilience of the power grid. To avoid this, HGC communicates with the other HGC devices in the neighborhood and coordinates PHEV loads. Our simulation results show that, efficiency of a PHEV as a storage unit increases as it is plugged for longer periods. Moreover, when renewable energy resources are not available, a larger portion of the PHEV battery can be used for storing energy during off-peak hours, and discharging during peak hours to accommodate the household demand. Thus, we show that HGC is able to provide savings for the consumers and it can also coordinate the power supply such that the availability of solar power increases the efficiency and reduces the utilization of PHEV battery.

  • tou aware energy management and wireless sensor networks for reducing peak load in smart grids
    Vehicular Technology Conference, 2010
    Co-Authors: Melike Erolkantarci, Hussein T Mouftah
    Abstract:

    The electricity grid is undergoing a major renovation and becoming a smart grid by integrating the advances in Information and Communication Technologies (ICT). Current applications in energy generation, power distribution and its consumption need improvement in several ways, such as, making efficient use of green energy, increasing automation in distribution and enabling residential energy management. The existing grid does not provide sufficient mechanisms to manage the residential electricity consumption. However, interconnecting consumer devices with the Home area networks, and at the same time, communicating with the utility networks through a Home Gateway facilitate residential energy management in smart grids. Residential energy management uses utility-driven price signals which vary depending on the time of the day. This is called as Time Of Use (TOU) pricing. In TOU pricing, electricity consumption during peak hours costs more than electricity consumption during off-peak hours. TOU prices reflect the variation in the actual cost of power during one day. Utilities run bas plants to supply power for the base load. In peak hours, demands of the consumers rise, and utilities bring peaker plants online to supply additional power. Peaker plants have higher operating costs and higher GreenHouse Gas (GHG) emission rates than base plants. Therefore, reducing peak load decreases the expenses for energy generation and it decreases the GHG emissions. Wireless sensor networks can play a key role in reducing the demand of the consumers in peak hours. In this paper, we employ TOU-aware energy management in a smart Home with wireless sensor Home area network and analyze the impact of this schemes on the peak load. We show that our scheme decreases the use of the appliances in peak hours and reduces the energy bills for consumers.

Pan Wang - One of the best experts on this subject based on the ideXlab platform.

  • datanet deep learning based encrypted network traffic classification in sdn Home Gateway
    IEEE Access, 2018
    Co-Authors: Pan Wang, Xuejiao Chen, Yi Qian
    Abstract:

    A smart Home network will support various smart devices and applications, e.g., Home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart Home access the Internet through a Home Gateway (HGW). In this paper, we propose a software-defined-network (SDN)-HGW framework to better manage distributed smart Home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart Homes. Our proposed SDN-HGW extends the control to the access network, i.e., a smart Home network, for better end-to-end network management. Specifically, the proposed SDN-HGW can achieve distributed application awareness by classifying data traffic in a smart Home network. Most existing traffic classification solutions, e.g., deep packet inspection, cannot provide real-time application awareness for encrypted data traffic. To tackle those issues, we develop encrypted data classifiers (denoted as DataNets) based on three deep learning schemes, i.e., multilayer perceptron, stacked autoencoder, and convolutional neural networks, using an open data set that has over 200 000 encrypted data samples from 15 applications. A data preprocessing scheme is proposed to process raw data packets and the tested data set so that DataNet can be created. The experimental results show that the developed DataNets can be applied to enable distributed application-aware SDN-HGW in future smart Home networks.

Kyungbin Song - One of the best experts on this subject based on the ideXlab platform.

  • an optimal power scheduling method for demand response in Home energy management system
    IEEE Transactions on Smart Grid, 2013
    Co-Authors: Zhuang Zhao, Won Cheol Lee, Yoan Shin, Kyungbin Song
    Abstract:

    With the development of smart grid, residents have the opportunity to schedule their power usage in the Home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a Home area network (HAN) based on the smart grid and then propose an efficient scheduling method for Home power usage. The Home Gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the Home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

Zhuang Zhao - One of the best experts on this subject based on the ideXlab platform.

  • an optimal power scheduling method for demand response in Home energy management system
    IEEE Transactions on Smart Grid, 2013
    Co-Authors: Zhuang Zhao, Won Cheol Lee, Yoan Shin, Kyungbin Song
    Abstract:

    With the development of smart grid, residents have the opportunity to schedule their power usage in the Home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a Home area network (HAN) based on the smart grid and then propose an efficient scheduling method for Home power usage. The Home Gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the Home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

  • an optimal power scheduling method applied in Home energy management system based on demand response
    Etri Journal, 2013
    Co-Authors: Zhuang Zhao, Won Cheol Lee, Yoa Shi, Kyungbi Song
    Abstract:

    In this paper, we first introduce a general architecture of an energy management system in a Home area network based on a smart grid. Then, we propose an efficient scheduling method for Home power usage. The Home Gateway (HG) receives the demand response (DR) information indicating the real-time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the Home operate automatically in the most cost-effective way possible. In our research, to avoid the high peak-to-average ratio (PAR) of power, we combine the real-time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.