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 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.
College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China. wang.wp2010@gmail.com.
Weiping
W
Wang
College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China. changqiang@nudt.edu.cn.
Qiang
Q
Chang
College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China. liqun@nudt.edu.cn.
Qun
Q
Li
College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China. nudtshizesen@126.com.
Zesen
Z
Shi
College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China. weichen@nudt.edu.cn.
Wei
W
Chen
GSM; indoor/outdoor detection; machine learning; seamless positioning
Sensors (Basel). 2015 Aug 28;15(9):21219-38
26343665
Sensors (Basel). 2016 Mar 19;16(3):null
27007379