Abstract:
In many lifetime enhancement strategies for wireless sensor networks (WSNs) it is often necessary to identify the statistical model of the underlying physical field. We c...Show MoreMetadata
Abstract:
In many lifetime enhancement strategies for wireless sensor networks (WSNs) it is often necessary to identify the statistical model of the underlying physical field. We consider the problem of in-situ inference as an exemplary application and propose an in-situ model estimation algorithm that works in tandem with a parametric distributed filtering procedure. We demonstrate, via averaged-gradient analysis and simulations, that the resulting adaptive filter is stable, robust and, importantly, fully scalable. It compares favorably with kernel-regression inference, and typically significantly outperforms the latter when the spatio-temporal variations in the natural field are relatively rapid.
Date of Conference: 26-30 November 2007
Date Added to IEEE Xplore: 26 December 2007
ISBN Information:
Print ISSN: 1930-529X