A fast and effective multidimensional scaling approach for node localization in wireless sensor networks

Georgios Latsoudas, Nicholas D. Sidiropoulos

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Given a set of pairwise distance estimates between nodes, it is often of interest to generate a map of node locations. This is an old nonlinear estimation problem that has recently drawn interest in the signal processing community, due to the emergence of wireless sensor networks. Sensor maps are useful for estimating the spatial distribution of measured phenomena, and for routing purposes. We propose a two-stage algorithm that combines algebraic initialization and gradient descent. In particular, we borrow an algebraic solution known as Fastmap from the database literature, adapt it to the sensor network context, and motivate the placement of anchor/pivot nodes on the edges of the network. When all nodes can estimate their distance from the anchors, the overall algorithm offers very competitive performance at low complexity (quadratic in the number of nodes).

Original languageEnglish (US)
Pages (from-to)5121-5127
Number of pages7
JournalIEEE Transactions on Signal Processing
Volume55
Issue number10
DOIs
StatePublished - Oct 2007

Keywords

  • Multidimensional scaling
  • Node localization
  • Sensor networks

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