JSM 2005 - Toronto

Abstract #302742

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 11
Type: Invited
Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #302742
Title: Distributed Localization in Sensor Networks Using Adaptive Multidimensional Scaling
Author(s): Jose A. Costa and Neal Patwari and Alfred O. Hero, III*+
Companies: University of Michigan and University of Michigan and University of Michigan
Address: 1301 Beal Avenue, Ann Arbor, MI, 48109-2122, USA
Keywords: Sensor Netwroks ; Distributed Optimization ; Multidimensional Scaling
Abstract:

Accurate, distributed localization algorithms are needed for a variety of wireless sensor network applications. For a network of thousands or even millions of sensors, the large scale precludes centralized location estimation. This paper introduces a scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function, and passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy efficiency of the proposed distributed method versus a centralized approach. For received signal-strength (RSS)-based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to the Cram\'er-Rao lower bound. Further, RSS and time-of-arrival (TOA) channel measurements are used to demonstrate performance as good as the centralized maximum-likelihood estimator (MLE) in a real-world sensor network.

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Revised March 2005