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Vol. 157, Issue 10, October 2013, pp. 101-107

 

Bullet

 

Efficient Implementation of Distributed Maximum Likelihood Estimation Method in Clustered Wireless Sensor Networks
 
1 X. F. ZHANG, 2 H. Y. LIU

1 College of Information Technology of Beijing Union University, 100101 Beijing, China

2 Institute of Microelectronics of Chinese Academy of Sciences, 100029, Beijing, China

1 Tel.: 15901568198

1 E-mail: 173661702@qq.com

 

Received: 11 July 2013   /Accepted: 25 September 2013   /Published: 31 October 2013

Digital Sensors and Sensor Sysstems

 

Abstract: The problem of efficient implementation of maximum-likelihood (ML) estimation of an unknown deterministic vector parameter in a clustered Wireless Sensor Network (WSN) is considered in this paper. In our previous work, we developed a distributed estimation manner through the combination of Lagrangian multiplier and block coordinate descent methods. In this paper, we give rigorous proof that the distributed estimation approach converges to the ML estimation result assuming ideal communication links. We also perform simulations to demonstrate the performance of the approach under additive-white Gaussian noise channel. Then we propose the efficient implementation of the most complicated step of the approach based on the preconditioned conjugate gradient method. Moreover, we present a hierarchical architecture for the implementation of the approach, which suggest our distributed estimation approach is favorable for practical uses.

 

Keywords: Wireless Sensor Network (WSN), Distributed Estimation, Maximum Likelihood Estimation, Conjugate Gradient Method Multiple.

 

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