Indoor-Outdoor Detection Using a Smart Phone Sensor

In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environ...

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Main Authors: Weiping Wang, Qiang Chang, Qun Li, Zesen Shi, Wei Chen
Format: Article
Language:English
Published: MDPI AG 2016-09-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/16/10/1563
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author Weiping Wang
Qiang Chang
Qun Li
Zesen Shi
Wei Chen
author_facet Weiping Wang
Qiang Chang
Qun Li
Zesen Shi
Wei Chen
author_sort Weiping Wang
collection DOAJ
description In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environments. Selecting different positioning techniques for different environments is an alternative method. Detecting the users’ current environment is crucial for this technique. In this paper, we proposed to detect the indoor/outdoor environment automatically without high energy consumption. The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station’s signal strength in different environments, and identified the users’ current context by signal pattern recognition. We tested the algorithm in four different environments. The results showed that the proposed algorithm was capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using the signal strength of four nearby GSM stations. The required hardware and signal are widely available in our daily lives, implying its high compatibility and availability.
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spelling doaj.art-8936d7d0947740679753cc86f72287132022-12-22T01:56:33ZengMDPI AGSensors1424-82202016-09-011610156310.3390/s16101563s16101563Indoor-Outdoor Detection Using a Smart Phone SensorWeiping Wang0Qiang Chang1Qun Li2Zesen Shi3Wei Chen4College of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaCollege of Information Systems and Management, National University of Defense Technology, Changsha 410073, ChinaIn the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention. No single positioning technology was capable of meeting the various positioning requirements in different environments. Selecting different positioning techniques for different environments is an alternative method. Detecting the users’ current environment is crucial for this technique. In this paper, we proposed to detect the indoor/outdoor environment automatically without high energy consumption. The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station’s signal strength in different environments, and identified the users’ current context by signal pattern recognition. We tested the algorithm in four different environments. The results showed that the proposed algorithm was capable of identifying open outdoors, semi-outdoors, light indoors and deep indoors environments with 100% accuracy using the signal strength of four nearby GSM stations. The required hardware and signal are widely available in our daily lives, implying its high compatibility and availability.http://www.mdpi.com/1424-8220/16/10/1563seamless positioningindoor/outdoor detectionmachine learningGSM
spellingShingle Weiping Wang
Qiang Chang
Qun Li
Zesen Shi
Wei Chen
Indoor-Outdoor Detection Using a Smart Phone Sensor
Sensors
seamless positioning
indoor/outdoor detection
machine learning
GSM
title Indoor-Outdoor Detection Using a Smart Phone Sensor
title_full Indoor-Outdoor Detection Using a Smart Phone Sensor
title_fullStr Indoor-Outdoor Detection Using a Smart Phone Sensor
title_full_unstemmed Indoor-Outdoor Detection Using a Smart Phone Sensor
title_short Indoor-Outdoor Detection Using a Smart Phone Sensor
title_sort indoor outdoor detection using a smart phone sensor
topic seamless positioning
indoor/outdoor detection
machine learning
GSM
url http://www.mdpi.com/1424-8220/16/10/1563
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AT qiangchang indooroutdoordetectionusingasmartphonesensor
AT qunli indooroutdoordetectionusingasmartphonesensor
AT zesenshi indooroutdoordetectionusingasmartphonesensor
AT weichen indooroutdoordetectionusingasmartphonesensor