Grouping multi-duolateration localization using partial space information for indoor wireless sensor networks

Hojae Lee, Sanghoon Lee, Yeonsoo Kim, Hakjin Chong

Research output: Contribution to journalArticle

20 Citations (Scopus)


Recently, sensor network technologies are being applied to more places such as intelligent home, offices and universities. Localization algorithms are required for indoor wireless sensor network (WSN) applications because the identification of real positions can upgrade the importance of sensing information. In this paper, a sensor network, where small-sized, low-cost and low-rate sensors are uniformly deployed in an indoor WSN environment, is taken into account. We introduce efficient algorithms, termed multiduolateration localization (MDL) and grouping multiduolateration localization (GMDL), which can improve the accuracy of location identification by employing jumper setting. The MDL algorithm can estimate 2-dimensional coordinates with high accuracy by the acquisition of edge information from the setting. In addition, the GMDL algorithm can be applied to the estimation of 3-dimensional coordinates by the acquisition of edge and surface information from the setting. The proposed algorithms perform localization more accurately than trilateration and faster than multidimensional scaling (MDS). The results from a MATLAB simulation show the outperformance of the proposed algorithms and demonstrate the possibility of 3-dimensional localization1.

Original languageEnglish
Article number5373755
Pages (from-to)1950-1958
Number of pages9
JournalIEEE Transactions on Consumer Electronics
Issue number4
Publication statusPublished - 2009 Nov 1

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering

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