Efficient Sensor Localization Method with Classifying Environmental Sensor Data

Sensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the received signal strength (RSS) of wireless sensors are used instead of a global p...

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Үндсэн зохиолчид: Ae-cheoun Eun, Young-guk Ha
Формат: Өгүүллэг
Хэл сонгох:English
Хэвлэсэн: Wiley 2012-12-01
Цуврал:International Journal of Distributed Sensor Networks
Онлайн хандалт:https://doi.org/10.1155/2012/417830
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author Ae-cheoun Eun
Young-guk Ha
author_facet Ae-cheoun Eun
Young-guk Ha
author_sort Ae-cheoun Eun
collection DOAJ
description Sensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the received signal strength (RSS) of wireless sensors are used instead of a global positioning system because of the lack of availability of a global positioning system for indoor environments. However, there is a problem in determining sensor locations from the RSS: radio signal interference occurs because of the presence of indoor obstacles. To avoid this problem, we propose a novel localization method that uses environmental data recorded at each sensor location and a data classification technique to identify the location of sensor nodes. In this study, we used a wireless sensor node to collect data on various environmental parameters—temperature, humidity, sound, and light. We then extracted some features from the collected data and trained the location data classifier to identify the location of the wireless sensor node.
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spelling doaj.art-f1fec7e3bfd842baaa2b17d38686ee632025-02-03T05:48:36ZengWileyInternational Journal of Distributed Sensor Networks1550-14772012-12-01810.1155/2012/417830Efficient Sensor Localization Method with Classifying Environmental Sensor DataAe-cheoun EunYoung-guk HaSensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the received signal strength (RSS) of wireless sensors are used instead of a global positioning system because of the lack of availability of a global positioning system for indoor environments. However, there is a problem in determining sensor locations from the RSS: radio signal interference occurs because of the presence of indoor obstacles. To avoid this problem, we propose a novel localization method that uses environmental data recorded at each sensor location and a data classification technique to identify the location of sensor nodes. In this study, we used a wireless sensor node to collect data on various environmental parameters—temperature, humidity, sound, and light. We then extracted some features from the collected data and trained the location data classifier to identify the location of the wireless sensor node.https://doi.org/10.1155/2012/417830
spellingShingle Ae-cheoun Eun
Young-guk Ha
Efficient Sensor Localization Method with Classifying Environmental Sensor Data
International Journal of Distributed Sensor Networks
title Efficient Sensor Localization Method with Classifying Environmental Sensor Data
title_full Efficient Sensor Localization Method with Classifying Environmental Sensor Data
title_fullStr Efficient Sensor Localization Method with Classifying Environmental Sensor Data
title_full_unstemmed Efficient Sensor Localization Method with Classifying Environmental Sensor Data
title_short Efficient Sensor Localization Method with Classifying Environmental Sensor Data
title_sort efficient sensor localization method with classifying environmental sensor data
url https://doi.org/10.1155/2012/417830
work_keys_str_mv AT aecheouneun efficientsensorlocalizationmethodwithclassifyingenvironmentalsensordata
AT younggukha efficientsensorlocalizationmethodwithclassifyingenvironmentalsensordata