## 1 Introduction
Based on discussions in [10], there are two methods to estimate Wi-Fi-based localization: Wi-Fi ranging and Wi-Fi fingerprinting. In Wi-Fi ranging, estimation is done directly with the distance towards the APs, which is impractical inside buildings due to multiple signal occlusions, wall reflections, and the overall influence of people. On the other hand, Wi-Fi fingerprinting methods focus on efficiently comparing achieved Wi-Fi scans to the prerecorded database. However, Wi-Fi fingerprinting still has problem as it is difficult to tune in the case of hierarchical buildings, floors, and rooms, since it requires a larger number of available datasets. Continuous Wi-Fi signal fluctuations are another problem to tackle for accurate localization using Wi-Fi fingerprinting approaches. To alleviate localization problems of fingerprinting approaches, machine-learning methods were proposed [1; 5; 8; 11; 12]. In [1], a radial basis function network was used to estimate location, and a particle filter was used to track the user's path in an indoor environment. RFID and IR were used as the source of data from sensors, which creates a high cost as it requires additional hardware and requires offensive calibration processes. In [5], support vector regression (SVR) and an artificial neural network (ANN) were used for indoor localization. The data was generated by propagation model in 2 GHz band. This work focused on locating APs in indoor environments, rather than localizing mobile users. In [11], authors used an ANN algorithm for indoor localization using ToA and AoA as data sources. However, this type of approach has operational complexity and is difficult to accurately localize because of lacks of LoS. In [13], K-nearest neighbors (KNN) was used to estimate the current position of a mobile user. They used historical data to estimate the current position of mobile users. Here, the time dependency of RSS values was not considered. In [12], KNN and a Kalman filter were used for localization performance using APs and other sensors. The accuracy was evaluated using mean square error. This work required extra hardware for data collection, and did not show system performances at different testing points. In this paper, we propose to use a deep neural network (DNN) with linear discriminate analysis (LDA) to accurately localize by cleaning noise and utilizing the benefits of a large number of datasets. In DNN, we focused on classification- and regression-based indoor localization schemes using real data collected from three buildings. In this particular work, we used the multilayer perceptron (MLP) algorithm using a rectified linear activation function in four hidden layers. Before applying localization in both schemes, we apply regression to fill the missed RSS values and LDA for dimensionality reduction. The proposed system is evaluated in different scenarios at both schemes. The performances of our proposed method are also compared with other algorithms in each scenario to validate our proposed approach. The proposed system is evaluated at extinctive datasets collected from 802.11 standards in our own institution. The main contribution of this paper is to apply integrations of LDA and MLP algorithms to improve localization accuracies and computational complexities. Moreover, to collect large and heterogeneous datasets from hierarchical and complex environments. We prepare class-independent projected vector to convert any complex features into smaller features without affecting information contents. Additionally, we practice the parallel implementation of classification and regression schemes in Graphical Processing Unit (GPU), which helps to improve performance rates in Wi-Fi environments. This helps to provide both bounded and specific positioning of Wi-Fi users in hierarchical and complex environments at the same instance. The integration of LDA and MLP has benefits of the usage of larger datasets and more accurate localization. It also improves computational times by reducing from complex into simpler features.