PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Diffusion Approach to Network Localization
yaniv gur and Yosi Keller
IEEE Transactions on Signal Processing 2009.


The localization of nodes on a network is a challenging research topic. It naturally arises in a variety of applications such as communications and sensor network analysis. We propose a computational approach to recovering the positions of network nodes given partial and corrupted distance measurements, and the positions of a small subset of anchor nodes. First, we show how to derive geometrically adaptive diffusion bases defined over the entire network, given only partial distance measurements. Second, we propose to utilize several diffusion bases simultaneously to derive multiscale diffusion frames. These provide an additional diffusion scale adaption layer. Last, we utilize these bases to formulate a least squares extension of the anchor points coordinates to the entire network. We experimentally show that under a wide range of conditions our method compares favorably with state-of-the-art approaches.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5833
Deposited By:Yosi Keller
Deposited On:08 March 2010