Title :
The value of clustering in distributed estimation for sensor networks
Author :
Son, Sung-Hyun ; Chiang, Mung ; Kulkarni, Sanjeev R. ; Schwartz, Stuart C.
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
Abstract :
Energy efficiency, low latency, high estimation accuracy, and fast convergence are important goals in distributed estimation algorithms for sensor networks. One approach that adds flexibility in achieving these goals is clustering. In this paper, we extend the framework of distributed estimation by allowing clustering amongst the nodes. The general class of distributed in-cluster algorithms considered includes the distributed in-network algorithm, recently proposed by Rabbatand Nowak (2004), as a special case. The distributed parameter estimation problem is posed as a convex optimization problem involving a social cost function and data from the sensor nodes. An in-cluster algorithm is then derived using the incremental subgradient method. Sensors in each cluster successively update a cluster parameter estimate based on local data, which is then passed on to a fusion center for further processing. We also prove convergence results for the distributed in-cluster algorithm, and provide simulations for least squares and robust estimation.
Keywords :
convergence of numerical methods; distributed algorithms; gradient methods; least squares approximations; parameter estimation; wireless sensor networks; convergence; convex optimization; distributed estimation; distributed estimation algorithms; distributed in-cluster algorithms; incremental subgradient method; least squares method; sensor networks; Clustering algorithms; Convergence; Cost function; Delay; Iterative algorithms; Least squares approximation; Parameter estimation; Robustness; Sensor fusion; Wireless sensor networks;
Conference_Titel :
Wireless Networks, Communications and Mobile Computing, 2005 International Conference on
Print_ISBN :
0-7803-9305-8
DOI :
10.1109/WIRLES.2005.1549544