Range-Free ranking in sensors networks and its applications to localization

Zvi Lotker, Marc Martinez De Albeniz, Stéphane Pérénnes

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Scopus citations

Abstract

We address the question of finding sensors' coordinates, or at least an approximation of them, when the sensors' abilities are very weak. In a d dimensional space, we define an extremely relaxed notion of coordinates along dimension i. The ranki of a sensor s is the number of sensors with ith-coordinate less than the i-coordinate of s. In this paper we provide a theoretical foundation for sensor ranking, when one assumes that a few anchor sensors know their locations and that the others determine their rank only by exchanging information. We show that the rank problem can be solved in linear time in ℝ and that it is NP-Hard in ℝ2. We also study the usual localization problem and show that in general one cannot solve it; unless one knows a priori information on the sensors distribution.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsIoanis Nikolaidis, Michel Barbeau, Evangelos Kranakis
PublisherSpringer Verlag
Pages158-171
Number of pages14
ISBN (Print)9783540286349
DOIs
StatePublished - 1 Jan 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3158
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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