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Author: | Valarezo, José |
Title: | Enabling wireless sensors localization in dynamic indoor environments |
Publication type: | Master's thesis |
Publication year: | 2010 |
Pages: | vi + 71 s. + liitt. 26 Language: eng |
Department/School: | Tietoliikenne- ja tietoverkkotekniikan laitos |
Main subject: | Tietoliikennetekniikka (S-72) |
Supervisor: | Jäntti, Riku |
Instructor: | Nethi, Shekar |
Electronic version URL: | http://urn.fi/URN:NBN:fi:aalto-201203131437 |
OEVS: | Electronic archive copy is available via Aalto Thesis Database.
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Location: | P1 Ark Aalto 1534 | Archive |
Keywords: | localization path loss received signal strength wireless sensors networks multipath effect shadow fading optimization range |
Abstract (eng): | Wireless sensors networks localization is an important area that attracts significant research interest. Localization is a fundamental problem that must be solved in order to support location-aware applications. The growing demand of location-aware applications requires the development of application-oriented localization solutions with appropriate trade offs between accuracy and costs. The present thesis seeks to enhance the performance of simple and low-cost propagation based localization solutions in dynamic indoor environments. First, an overview of the different approaches in wireless sensors networks localization is provided. Next, sources of received signal strength variability are investigated. Then, the problems of the distance-dependant path loss estimation caused by the radio channel of dynamic indoor situations are empirically analyzed. Based on these previous theoretical and empirical analysis, the solution uses spatial and frequency diversity techniques, in addition to time diversity, in order to create a better estimator of the distance-dependent path loss by counteracting the random multipath effect. Furthermore, the solution attempts to account for the random shadow fading by using "shadowing-independent" path loss estimations in order to deduce distances. In order to find the unknown sensor's positions based on the distance estimates, the solution implements a weighted least-squares algorithm that reduces the impact of the distance estimates errors in the location estimate. |
ED: | 2010-06-09 |
INSSI record number: 39757
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