Article: No-calibration localisation for indoor wireless sensor networks Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2014 Vol.15 No.1/2/3 pp.200 - 214 Abstract: In this paper, we propose and evaluate an algorithm and a few variations to exclude aberrant landmarks before performing a maximum likelihood estimator in order to evaluate a mobile node position in a 1 hop indoor wireless network more accurately. We exclude data coming from landmarks based on a global likelihood to alleviate the effects of multipath propagation and replace this data with a constant bias.We compare the cases when one, two or three landmarks are excluded with the classical unbiased maximum likelihood estimator over three different testbeds. We then evaluate a heuristic approach that does not need to examine all possible subsets of landmarks but selects excluded landmarks based on a threshold on the bias. Results indicate that, depending on the testbed characteristics, one strategy or the other can lead to better results, but in most cases the performance is improved w.r.t. classical maximum likelihood estimation. Inderscience Publishers - linking academia, business and industry through research

Title: No-calibration localisation for indoor wireless sensor networks

Authors: N'deye Amy Dieng; Claude Chaudet; Laurent Toutain; Tayeb Ben Meriem; Maurice Charbit

Addresses: Institut Mines-Télécom/Télécom Bretagne/IRISA, Technopôle Brest-Iroise, CS 83818, 29238 Brest Cedex 3, France ' Institut Mines-Télécom/Télécom ParisTech/CNRS LTCI UMR 5141, 23 Avenue d'Italie, CS 51237 – 75214 Paris Cedex 13, France ' Institut Mines-Télécom/Télécom Bretagne/IRISA, Technopôle Brest-Iroise, CS 83818, 29238 Brest Cedex 3, France ' Orange Lab Network Carrier/R&D 38-40 rue du Général Leclerc, 92794 Issy-les-Moulineaux, France ' Institut Mines-Télécom / Télécom ParisTech / CNRS LTCI UMR 5141 46 rue Barrault, 75634 Paris Cedex 13, France

Abstract: In this paper, we propose and evaluate an algorithm and a few variations to exclude aberrant landmarks before performing a maximum likelihood estimator in order to evaluate a mobile node position in a 1 hop indoor wireless network more accurately. We exclude data coming from landmarks based on a global likelihood to alleviate the effects of multipath propagation and replace this data with a constant bias.We compare the cases when one, two or three landmarks are excluded with the classical unbiased maximum likelihood estimator over three different testbeds. We then evaluate a heuristic approach that does not need to examine all possible subsets of landmarks but selects excluded landmarks based on a threshold on the bias. Results indicate that, depending on the testbed characteristics, one strategy or the other can lead to better results, but in most cases the performance is improved w.r.t. classical maximum likelihood estimation.

Keywords: wireless localisation; RSSI; received signal strength indicator; path loss; MLE; maximum likelihood estimation; log-normal shadowing; experimentation; no-calibration localisation; indoor WSNs; wireless sensor networks; wireless networks.

DOI: 10.1504/IJAHUC.2014.059908

International Journal of Ad Hoc and Ubiquitous Computing, 2014 Vol.15 No.1/2/3, pp.200 - 214

Received: 04 Jan 2013
Accepted: 29 Jul 2013
Published online: 19 Mar 2014 *

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Title: No-calibration localisation for indoor wireless sensor networks

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