• DocumentCode
    249360
  • Title

    Big Data Collection Gateway for Vision-Based Smart Meter Reading Network

  • Author

    Ching-Han Chen ; Ching-Yi Chen ; Chih-Hsien Hsia ; Guan-Xin Wu

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Taoyuan, Taiwan
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    266
  • Lastpage
    269
  • Abstract
    For building the big data collection infrastruction, a vision-based smart meter reading network and its gateway is provided to a community gas supply system based on traditional mechanical meters. In the network architecture, the gas meter readings are captured by embedded image sensor nodes and then are transmitted to a gateway designed herein for reading image recognition and are collected in the embedded database of gateway. Moreover, the web-based monitoring system designd in HTML5 is applicable to not only a mobile device by which a user can realize household gas consumption and history but also a gas company that develops an effective energy management system to analyze community users´ energy consumption models by means of big data collected in a database.
  • Keywords
    Big Data; computer vision; embedded systems; gas industry; image recognition; network servers; smart meters; Big Data collection gateway; HTML5; Web-based monitoring system; community gas supply system; embedded database; embedded image sensor nodes; energy consumption models; energy management system; household gas consumption; image recognition; vision-based smart meter reading network; Computer architecture; Image recognition; Logic gates; Meter reading; Radio frequency; Wireless communication; Wireless sensor networks; Big data; Gas meter; Gateway; HTML5; Smart meter reading network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2014 IEEE International Congress on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5056-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2014.47
  • Filename
    6906788