## 2 Related Work  
In the literature of indoor positioning, there are various taxonomies. However, there is a common classification, which falls into two separate categories: Radio Frequency (RF)-based techniques, and non-RF-based techniques, which use another kind of sensors. WLAN, RFID, and Bluetooth are among the RF-based techniques. Audio, visual, ultra-sonic, Infra-Red (IR), laser sensors, and magnetic field are considered as non-RF-based methods [15; 16; 17; 18]. In this article, we mainly focus on RF-based techniques. Table 1 reports the main RF techniques that are utilized in indoor positioning. Recently, many types of research have mainly focused on Wi-Fi fingerprint-based IPS challenges, such as collecting fingerprinting signals and how these signals are processed through IPS techniques [19]. Dimensionality reduction is also an important challenge and has been studied through different techniques. Generally, there is a tradeoff between dimensionality reduction and the accuracy of classifiers [20]. In the online phase, the reduction of computational costs for Wi-Fi fingerprint-based IPS is very crucial in a real-time tracking system because large data require a long time to be processed. The roaming of a MS inside the building makes IPS unable to track the MS's location inside the building. Therefore, working on designing a light algorithm with low computational cost makes IPS more feasible. In this section, we review key techniques for positioning, which are used in dimensionality reduction. [PERSON] et al. [21] proposed a new approach for a Wi-Fi-based location fingerprinting system. The proposed method transforms RSS signals into Principal Components (PCs) intelligently, such that the effect of all APs is considered. Instead of the selection of APs, the proposed technique replaces the RSS of APs by a subset of PCs to improve accuracy and reduce the computational cost as well. To evaluate the proposed system, a realistic WLAN environment was chosen to conduct the experimental work. The results exhibit that the mean distance error was eliminated by 33.75% and the complexity by 40%, as compared to traditional approaches. [PERSON] et al. [22] proposed a novel technique for a WLAN-based location fingerprinting system. In the offline phase, the AOI was clustered into sub regions by fuzzy C-means and then the useful APs were selected to minimize the dimension of the fingerprint. In the online phase, the proposed system used a NN (Nearest Neighbor) method to choose subareas and to compute the coordinate of the target location by using relative distance fuzzy localization (RDFL) algorithms. The results showed the ability of the proposed system to reduce calculation time and improve positioning accuracy. [PERSON] et al. [23] implemented a WLAN- fingerprinting-based localization method, which is based on selecting the important APs. The APs with the strongest RSS power were selected as the informative APs. The results showed that the proposed system exhibits superior performance as compared with traditional techniques. [PERSON] et al. [24] modified a method, called Fast Orthogonal Search (FOS) method, to determine the singularity of RSS values, which are measured from various APs at each location point. In order to evaluate the modified FOS method, two important factors were taken into consideration, the amount \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Advantage** & **Disadvantage** \\ \hline
**Cell of origin** & Base stations are available and & Highly inaccurate \\  & never move & \\
**AoA** & High accuracy & Requires additional hardware \\
**ADoA** & High accuracy & Requires additional hardware \\
**ToA** & High accuracy & synchronization issue \\
**TDoA** & High accuracy & Requires additional hardware \\
**Fingerprinting- based location** & High accuracy & Computational and labor costs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RF Position Techniques [19]. of reduction, and the accuracy of IPS.