Distributed weighted-multidimensional scaling for node localization in sensor networks

  • Authors:

  • Jose A. Costa;Neal Patwari;Alfred O. Hero, III

  • Affiliations:

  • University of Michigan, Ann Arbor, Ann Arbor, MI;University of Michigan, Ann Arbor, Ann Arbor, MI;University of Michigan, Ann Arbor, Ann Arbor, MI

  • Venue:
  • ACM Transactions on Sensor Networks (TOSN)
  • Year:
  • 2006

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Abstract

Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article 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 then 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 Cramér-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.