# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 2 Related Work  
Current research into I/O detection is very limited and many studies in this field are primarily based on image processing, which is not suitable for mobile applications due to the complex calculation capacity and large memory required for input data [6; 7; 8]. However, there are practical methods to deal with this problem. One such method is based on collecting data using various sensors that are available on mobile devices [1; 9; 10; 11; 12]. Another is based solely on collecting data using a GPS sensor [13; 14]. In a previous study [9], the authors proposed an I/O detection method that used a set of mobile sensors, including a light sensor, a cellular module, and a magnetic sensor for I/O detection. That proposed method promised high accuracy and energy efficiency. This combination of sensors provided high accuracy (88%-90%) for both indoor and outdoor environments with average power consumption. Although the input data is available with mobile, this method requires complex calculations and a large amount of input data to work well. In another study, the author used even more sensors and data for I/O detection, including an activity recognition Application Programming Interface (API), barometric pressure, a light sensor, cloud coverage data, a timer, Global System for Mobile Communications (GSM) signal strength, an accelerometer, a magnetometer, and ambient noise [10]. These sets of input data increase I/O detection accuracy to more than 98%; however, calculation and data collection are complicated and consume significant power. Harsh testing conditions also affect I/O detection accuracy. In [11], the authors used the same input dataset with previous study [10], including an accelerometer, cellular radio, light sensor, magnetometer, and proximity sensor data; however, the accuracy is only 80%. Power consumption is also greater compared to a previous study [9]. Primarily based on light sensor data and a Wi-Fi sub-detector with a machine learning approach, another [12] obtained more than 90% accuracy. However, the training model is very complicated and difficult to implement. Similarly, reference [1] uses a machine learning approach for data processing and obtained high accuracy. Again, data consumption and a complicated calculation are disadvantages of this approach. Another approach is using only GPS signal because it is always available and provides significant information for I/O detection. In addition, this approach consumes less power compared to Wi-Fi and 3G signals. In a study about I/O detection based only on sparse GPS positioning information, [PERSON] et al. obtained 98% accuracy for I/O detection [14]. Although the test sample was very small (53 samples) and the approach required significant time and a complex calculation, this study promised highly accurate I/O detection based on GPS signals and a machine learning classifier. Based on a direct analysis of GPS signals, [PERSON] et al. provided three GPS-based techniques for I/O detection [13]. The technique that provided the best performance was SatProbe, which searches the raw signals from all available GPS satellites to identify input data patterns. With this technique, using a sizable dataset, accuracy reached 86% and it worked much better than the traditional method. Although the accuracy is not too high, as well as the complex calculation, this study also emphasizes the importance of raw GPS signals in I/O detection problems. A breakthrough approach for the indoor classification and localization is the combination of GPS and Wi-Fi signal, which is a flexible method to deal with emergency situations, where the GPS signal becomes unstable and weaker. In particular emergency situations, when the transmission of the GPS signal was obstructed, the localization process is impossible with GPS signal only, the Wi-Fi combination will be helpful in both indoor/outdoor detection and localization.