# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 2 Related Work
The proposed system works on 3D models and supports indoor localization from photo-based 3D models. The reported accuracy is under 4 m for iMoon. Authors in [54] build a system called WAIPO. The system takes advantage of Wi-Fi and magnetic fingerprints, as well as, the image matching and people co-occurrence. Initial estimation is made using Wi-Fi fingerprints which is further refined by image matching and Bluetooth beacons. The final position is then calibrated using the magnetic data. The accuracy of WAIPO is under 2 m at 98 percent. The discussed research works which are based on the fusion of technologies use Wi-Fi as a module in the system. Wi-Fi is either used to make the initial location estimation or refine the location. Wi-Fi based localization is limited by a number of factors. The scanning for available APs requires increased time as compared to other smartphone sensors including accelerometer, gyroscope, and camera, etc. Our experiments reveal that Wi-Fi AP's scanning requires 3 to 4 s on average. This raises latency concerns in real time localization systems and reduces the robustness. Additionally, the received signal strength indicator (RSSI) is prone to error and may indicate different locations based on similar RSSI. The dynamic factors including the presence of obstacles and people may cause huge fluctuations in the collected RSSI during the localization process which increases the error [55; 56]. Similarly, Wi-Fi based localization is highly sensitive to the wall separations, and floor plans and so they are vulnerable to random noise, path loss, multipath interference, shadowing, and so on [57]. Wi-Fi based systems have a dependence on the location of the installed APs as well and any change in their position requires the recalibration of the fingerprinting database [58]. One important point to consider is that approximately 75.39% of total smartphones operate on Android while other 22.35% on iOS operating systems, respectively [59]. The iOS does not provide Wi-Fi information which implies that the Wi-Fi localization systems cannot work on iOS-based phones. The limitations of Wi-Fi based localization urged researchers to find alternative technologies which are less prone to infrastructural changes and provide more accurate location estimations. The magnetic field has proven a reliable localization technology and has been a potential area of research during the last decade [8]. For example, authors in [60] present an approach which utilizes magnetic landmarks to perform indoor localization. Landmarks are the points where local minima/maxima exist in magnetic samples of eight-connected neighborhoods. The features of'recurrence plot', 'trend' of the peaks, and peak-to-peak 'length' from the magnetic data are used to train DNNs. The proposed approach achieves the best classification accuracy of 80.8% where the accuracy is the correct classification of magnetic landmarks. The proposed approach does not perform meter level localization, rather it focuses on specific landmarks classification which in some cases are several meters apart. For example, the length of magnetic data is set as seven meters to extract features which is a long data. In any case, the magnetic data collection is done using a smartphone placed on the robot. For pedestrian tracking, the results may vary significantly with the proposed approach as the pedestrian's holding the smartphone causes slight movements which affect the magnetic data. Authors in [61] present a system which utilizes the magnetic field data for localization using a smartphone. The proposed method is based on the smartphone sensors and utilizes the Wi-Fi and magnetic fingerprinting for localization. The fingerprint collection also involves image capturing at each step. The captured images are later used for scene recognition which is done using a Caffe trained CNN model. The smartphone camera is utilized for the initial estimation of the scene. The initial location helps Wi-Fi and magnetic positioning.