A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role...

Full description

Saved in:
Bibliographic Details
Main Authors: Sharly Joana Halder, Wooju Kim
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Journal of Computer Networks and Communications
Online Access:http://dx.doi.org/10.1155/2012/790374
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832565729573994496
author Sharly Joana Halder
Wooju Kim
author_facet Sharly Joana Halder
Wooju Kim
author_sort Sharly Joana Halder
collection DOAJ
description Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.
format Article
id doaj-art-9420db1abe204457a9d33b8b4e4d8296
institution Kabale University
issn 2090-7141
2090-715X
language English
publishDate 2012-01-01
publisher Wiley
record_format Article
series Journal of Computer Networks and Communications
spelling doaj-art-9420db1abe204457a9d33b8b4e4d82962025-02-03T01:06:56ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2012-01-01201210.1155/2012/790374790374A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive SmoothersSharly Joana Halder0Wooju Kim1Department of Information and Industrial Engineering, College of Engineering, Yonsei University, Seoul 120-749, Republic of KoreaDepartment of Information and Industrial Engineering, College of Engineering, Yonsei University, Seoul 120-749, Republic of KoreaDue to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.http://dx.doi.org/10.1155/2012/790374
spellingShingle Sharly Joana Halder
Wooju Kim
A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers
Journal of Computer Networks and Communications
title A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers
title_full A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers
title_fullStr A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers
title_full_unstemmed A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers
title_short A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers
title_sort fusion approach of rssi and lqi for indoor localization system using adaptive smoothers
url http://dx.doi.org/10.1155/2012/790374
work_keys_str_mv AT sharlyjoanahalder afusionapproachofrssiandlqiforindoorlocalizationsystemusingadaptivesmoothers
AT woojukim afusionapproachofrssiandlqiforindoorlocalizationsystemusingadaptivesmoothers
AT sharlyjoanahalder fusionapproachofrssiandlqiforindoorlocalizationsystemusingadaptivesmoothers
AT woojukim fusionapproachofrssiandlqiforindoorlocalizationsystemusingadaptivesmoothers