Abstract
Indoor localization systems assume that the user’s current building is known by the GPS (Global Positioning System). However, such assumptions do not hold true in GPS denied environments or where the GPS cannot determine the user’s definite location. We present a novel solution to identify the building where the user is present now. The proposed building identification method works on the pervasive magnetic field using a smartphone. The accelerometer data determines the user’s activity of being stationary or walking. An Artificial Neural Network is used to identify the user’s activities and it shows good results. The magnetometer data is used to identify the user’s current building using the fingerprinting approach. Contrary to a traditional fingerprinting approach which stores intensity values, we utilize the patterns formed by the magnetic field strength in the form of a Binary Grid (BG). The BG approach overcomes the limitation of Dynamic Time Warping (DTW) whose performance is degraded when the magnitude of the magnetic data is changed. The experiments are performed with Samsung Galaxy S8 for eight various buildings with different altitudes and number of floors in Yeungnam University, Korea. The results demonstrate that the proposed building identification method can potentially be deployed for building identification. The precision, UAR (Unweighted Average Recall), F score, and Cohen’s Kappa values are used to determine the performance of the proposed system. The proposed systems shows very promising results. The system operates without any aid from any infrastructure dependent technologies like GPS or WiFi. Furthermore, we performed many experiments to investigate the impact of isolated points data to build fingerprint database on system’s accuracy with 1 m and 2 m distance. Results illustrate that by trading off a minor accuracy, survey labor can be reduced by 50 percent.
Highlights
In the modern era of ubiquitous computing where the demand for Location Based Services (LBS)is increasing exponentially, positioning requires two fundamental characteristics: pervasive coverage and infrastructure independence
The technique is founded on the built-in sensors of the smartphone and leverages the accelerometer and magnetometer data only
Contrary to a traditional fingerprinting approach which stores magnetic magnitude as the fingerprint, we adopt the Binary Grid (BG) approach which transforms the magnetic magnitude into magnetic patterns and stores them
Summary
In the modern era of ubiquitous computing where the demand for Location Based Services (LBS). Is increasing exponentially, positioning requires two fundamental characteristics: pervasive coverage and infrastructure independence. Outdoor localization is mostly based on the GPS technology as it can provide an estimated location as accurate as 5 m. In many situations where the user location is surrounded by high-rise buildings or cloudy environments, the positioning accuracy can drop drastically to as low as 50 to 76 meters[1,2]. GPS, as well as Russian GLONASS constellation, provide similar positioning accuracy [3]. For the indoor environment, it does not fulfill the first requirement of being pervasive due to the signal attenuation caused by reflection and refraction, and lower signal-to-noise ratio (SNR)
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