PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks
Fernando Perez-Cruz and Sanjeev Kulkarni
IEEE Signal Processing Letters Volume 17, Number 4, pp. 355-358, 2010. ISSN 1070-9908

Abstract

We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7543
Deposited By:Fernando Perez-Cruz
Deposited On:17 March 2011