Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns

In ubiquitous sensor networks (USN), sensor node localization is normally performed with global positioning system (GPS) or radio signal strength (RSS) between a target node and reference nodes. Because GPS is not available in indoor environments, RSS-based approach is commonly used for indoor local...

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Main Authors: Young-guk Ha, Ae-cheoun Eun, Yung-cheol Byun
Format: Article
Language:English
Published: Hindawi - SAGE Publishing 2013-04-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2013/701259
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author Young-guk Ha
Ae-cheoun Eun
Yung-cheol Byun
author_facet Young-guk Ha
Ae-cheoun Eun
Yung-cheol Byun
author_sort Young-guk Ha
collection DOAJ
description In ubiquitous sensor networks (USN), sensor node localization is normally performed with global positioning system (GPS) or radio signal strength (RSS) between a target node and reference nodes. Because GPS is not available in indoor environments, RSS-based approach is commonly used for indoor localization. However, RSS-based approach is hard to be applied to real indoor environments (e.g., home) because of signal interferences with various indoor obstacles such as walls, doors, furniture, and electric appliances. In this paper, we propose an efficient indoor localization method for Zigbee sensor nodes by classifying link quality indicator (LQI) patterns between a target node and multiple reference nodes rather than using calculation with RSS values. And we also present the results of indoor localization experiments in our ubiquitous home network test bed using the proposed localization method.
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spelling doaj.art-4e409e177eab4df7beb0585afe0389ce2023-09-02T18:03:12ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772013-04-01910.1155/2013/701259Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality PatternsYoung-guk Ha0Ae-cheoun Eun1Yung-cheol Byun2 Department of Computer Science & Engineering, Konkuk University, Seoul, Republic of Korea Department of Computer Science & Engineering, Konkuk University, Seoul, Republic of Korea Department of Computer Engineering, Jeju National University, Jeju-si, Jeju, Republic of KoreaIn ubiquitous sensor networks (USN), sensor node localization is normally performed with global positioning system (GPS) or radio signal strength (RSS) between a target node and reference nodes. Because GPS is not available in indoor environments, RSS-based approach is commonly used for indoor localization. However, RSS-based approach is hard to be applied to real indoor environments (e.g., home) because of signal interferences with various indoor obstacles such as walls, doors, furniture, and electric appliances. In this paper, we propose an efficient indoor localization method for Zigbee sensor nodes by classifying link quality indicator (LQI) patterns between a target node and multiple reference nodes rather than using calculation with RSS values. And we also present the results of indoor localization experiments in our ubiquitous home network test bed using the proposed localization method.https://doi.org/10.1155/2013/701259
spellingShingle Young-guk Ha
Ae-cheoun Eun
Yung-cheol Byun
Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns
International Journal of Distributed Sensor Networks
title Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns
title_full Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns
title_fullStr Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns
title_full_unstemmed Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns
title_short Efficient Sensor Localization for Indoor Environments Using Classification of Link Quality Patterns
title_sort efficient sensor localization for indoor environments using classification of link quality patterns
url https://doi.org/10.1155/2013/701259
work_keys_str_mv AT younggukha efficientsensorlocalizationforindoorenvironmentsusingclassificationoflinkqualitypatterns
AT aecheouneun efficientsensorlocalizationforindoorenvironmentsusingclassificationoflinkqualitypatterns
AT yungcheolbyun efficientsensorlocalizationforindoorenvironmentsusingclassificationoflinkqualitypatterns