Next Article in Journal
Secure Nearest Neighbor Query on Crowd-Sensing Data
Previous Article in Journal
Strain Sharing Assessment in Woven Fiber Reinforced Concrete Beams Using Fiber Bragg Grating Sensors
Open AccessArticle

Indoor-Outdoor Detection Using a Smart Phone Sensor

College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
Author to whom correspondence should be addressed.
Academic Editor: Fan Ye
Sensors 2016, 16(10), 1563;
Received: 27 June 2016 / Revised: 12 September 2016 / Accepted: 19 September 2016 / Published: 22 September 2016
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: seamless positioning; indoor/outdoor detection; machine learning; GSM seamless positioning; indoor/outdoor detection; machine learning; GSM
Show Figures

Figure 1

MDPI and ACS Style

Wang, W.; Chang, Q.; Li, Q.; Shi, Z.; Chen, W. Indoor-Outdoor Detection Using a Smart Phone Sensor. Sensors 2016, 16, 1563.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop