• DocumentCode
    918247
  • Title

    Energy Planning for Progressive Estimation in Multihop Sensor Networks

  • Author

    Huang, Yi ; Hua, Yingbo

  • Author_Institution
    Dept. of Electr. Eng., Univ. of California, Riverside, CA, USA
  • Volume
    57
  • Issue
    10
  • fYear
    2009
  • Firstpage
    4052
  • Lastpage
    4065
  • Abstract
    Multihop sensor networks where transmissions are conducted between neighboring sensors can be more efficient in energy and spectrum than single-hop sensor networks where transmissions are conducted directly between each sensor and a fusion center. With the knowledge of a routing tree from all sensors to a destination node, we present a digital transmission energy planning algorithm as well as an analog transmission energy planning algorithm for progressive estimation in multihop sensor networks. Unlike many iterative consensus-type algorithms, the proposed progressive estimation algorithms along with their transmission energy planning further reduce the network transmission energy while guaranteeing any pre-specified estimation performance at the destination node within a finite time. We also show that digital transmission is more efficient in transmission energy than analog transmission if the available transmission time-bandwidth product for each link and each observation sample is not too limited.
  • Keywords
    estimation theory; iterative methods; telecommunication network planning; wireless sensor networks; analog transmission energy planning algorithm; destination node; digital transmission energy planning algorithm; fusion center; iterative consensus-type algorithm; multihop sensor networks; progressive estimation algorithms; single-hop sensor networks; Decentralized estimation; distributed estimation; energy scheduling and planning; incremental estimation; multi-hop sensor networks; power scheduling and planning; progressive estimation; wireless sensor networks;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/TSP.2009.2024023
  • Filename
    4982657