## 2 Related Works  
It is difficult to use traditional learning approaches for scalable localization in different complex and hierarchical environments because shallow learning has limitations of localization accuracy due to data size and the learning performance of the algorithms. It is highly affected by different environmental factors, such as multipath fading and the attenuation of objects. Currently, DNN is applied to handle shallow learning problems by avoiding the needs of hand engineering for extracting feature representations, and easily learning high-level features from a large set of noise samples. As discussed in [9], DNN shows immunity against signal fluctuations, noise effects, device dependency, and the elimination of time-consuming manual parameter tuning. According to [10; 14], DNN helps to lower the workforce burden of localization. DNN can provide accurate Wi-Fi-based indoor localization due to the ability to learn signal fluctuations through time and environmental dynamicity because of its deeper functions that map the input to the output [4; 9; 10; 15; 16]. In [4], a stacked denoising auto-encoder (SDA) was used to reduce the dimensions, and then the hidden Markov model (HMM) was applied to refine the localization. The root mean square (RMS) was compared with respect to different sample sizes, and shows that using SDA and HMM has better accuracy than using DNN and HMM alone. They showed that it is possible to boost the localization time complexity using SDA and HMM. However, scholars evaluate their system in different data samples rather than comparing their proposed system with other algorithms. In [9], DNN was used for building and floor label positioning using RSS values. The classification accuracy is only 92% for floor-level localization. The proposed system was evaluated in terms of various epoch sizes. In [10], a DNN system was used for building and floor classification on the publicly-available UJIIndoorLoc dataset. This work used a stacked auto-encoder for feature reduction. Researchers reached the conclusion that the deep learning paradigm can provide a global location recognition solution from Wi-Fi data at a significantly reduced effort in hierarchical problem types. Nevertheless, the work did not consider localization accuracy evaluation at each testing point. In [15], channel state information was used for indoor localization. Researchers compared the accuracy of the proposed system performance in laboratories and living rooms and found that the system performs differently, even in environments which are nearly the same. This work has no way to determine the performance of the system in different scenarios and testing points. In [16], a feedforward neural network (FFNN) classifier was used for multi-building and multi-floor indoor localization, and could achieve 99.82% and 91.27% accuracy, respectively. The experimental scenario was performed in a building, while their system was evaluated through a simulation in a multi-building setting. However, in real world situations, it is very difficult to evaluate a system through datasets collected from different environments in different approaches. In [17], building-level classification was done by selecting the strongest RSS values from the measured fingerprints. Here, it is difficult for the system to discriminate buildings whenever there is similar signal strength. In [18], a deep-learning-based indoor fingerprinting system using channel state information (CSI) was used for indoor localization. In this technique, the system performance was evaluated through clustering, which has a lack of target values making it difficult to evaluate the system performance. In [19], MLP was used for indoor localization using the mitigation of the ground reflection effect for position estimation. This work was done through the aid of a wireless sensor networks. The localization has been done through AoA data sources that have LoS problems. The work focused on locating the position of each AP no matter the position of the Wi-Fi users in the environment.