• DocumentCode
    188277
  • Title

    Decision Fusion Using Three-Level Censoring Scheme in Sensor Networks under Rician Fading Channels

  • Author

    Shoujun Liu ; Kezhong Liu ; Wei Chen

  • Author_Institution
    Sch. of Inf. Eng., Wuhan Univ. of Technol., Wuhan, China
  • fYear
    2014
  • fDate
    13-15 Oct. 2014
  • Firstpage
    343
  • Lastpage
    348
  • Abstract
    This work considers the problem of decision fusion using the three-level censoring scheme in wireless sensor networks. An optimal fusion rule is derived for the model of Rician fading channel. However, the optimal fusion rule requires instantaneous channel state information (CSI) which may be too costly for resource constrained sensor networks. Hence, a sub-optimal alternative with the knowledge of Rician fading statistics is proposed. To further simplify the fusion rules, two low signal-to-noise approximations are also derived. Performance evaluation confirms that by using the three-level censoring scheme, the goal of energy saving and performance improvement can be achieved. The sub-optimal fusion rule based on channel statistics exhibits only slight performance degradation compared with the optimal fusion rule. In addition, the impact of Rician K-factor on the performance of decision fusion has also been discussed.
  • Keywords
    Rician channels; approximation theory; fading channels; wireless sensor networks; CSI; Rician K-factor; Rician fading channels; Rician fading statistics; channel state information; channel statistics; decision fusion; optimal fusion rule; resource constrained sensor networks; signal-to-noise approximations; suboptimal fusion rule; three level censoring scheme; wireless sensor networks; Approximation methods; Error probability; Fading; Rician channels; Signal to noise ratio; Wireless communication; Wireless sensor networks; Rician fading; censoring; decision fusion; wireless sensor networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2014 International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4799-6235-8
  • Type

    conf

  • DOI
    10.1109/CyberC.2014.66
  • Filename
    6984330