## 1 Introduction  
Currently, human daily life is becoming highly integrated with the Internet of Things (IoT), as the Internet attracts much attention with respect to the outlook of future life and rapidly increasing communication networks. One of the key technologies of IoT is localization, as it is a means to develop keen environments and situation-awake services [1; 2]. Additionally, the rapid developments of technologies have demands to increase localization services. According to [3], localization playsimportant roles by providing information about the position of mobile users. Location mindfulness is one of the empowering tools for the coming smart and green cities, by notifying people of occupied places and times. Moreover, localizations add value and offer various services, such as healthcare monitoring, emergency management, personal tracking, context-dependent information services, and advertisements. Localization can be applied to different areas, such as universities, military systems, and hospitals. Therefore, an accurate location scheme is a key issue for realizing the localization applications in various areas. In indoor environments; Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and Galileo are not practical because they lack line of sight (LoS) between the satellites and the receivers, which is easily affected by the physical layout of equipment and is sensitive to occlusion [3; 4; 5]. Therefore, indoor localization becomes common in indoor environments to offer convenient services. According to [2; 5], one of the enabling key factors for the future IoT archetype is applying accurate indoor localization. As pointed out in [6], indoor localization can be applied for robotics, ambient assisted living, health applications, location-based services (LBS), and to address challenges in 5G networks. Hence, indoor localization should be applied carefully to provide high accuracy, low complexity, and short operational time. Based on the dependency of range measurements, indoor localization can be classified as range-based and range-free localization schemes [7]. Range-based localization uses the measured distance or angle to estimate the location. Under these schemes, received signal strength (RSS), time-of-arrival (ToA), time-difference-of-arrival (TDoA), or angle-of-arrival (AoA) are common approaches. On the other hand, the range-free schemes use connectivity or pattern matching methods to estimate one's location. Range-free schemes include the centroid algorithm, the Distance Vector Hope (DV-hop) algorithm, the Multi-dimensional Scaling-MAP (MDS-MAP) algorithm, and convex programming approaches. Generally, range-based schemes are more accurate [8], simple, and low-cost [7] than range free schemes. In wireless communication, indoor localization can be applied using technologies such as wireless local area network (WLAN) or Wi-Fi, infrared (IR), radio frequency identification (RFID), ultrasound, Bluetooth, or a combination of these technologies [3]. Wi-Fi is predominantly used for indoor location because it can be installed in different areas without any new infrastructure. It is also easy to measure the required data from cheap Wi-Fi devices like Access Points (APs) [9]. Wi-Fi-based localization is required for location awareness services in places such as large shopping malls and university campuses. Such complex and hierarchical environments require mountable system designs for indoor localization. Wi-Fi based localization is commonly adopted based on RSS, ToA, TDoA, and AoA. RSS-based indoor localization in a Wi-Fi environment is characterized by minimum cost, less complexity for the localization system, and ease of characterizing and mapping the indoor environment into distinguishable areas [9]. Additionally, almost every Android mobile is equipped with a Wi-Fi adapter, so it is easy to find required information from APs. Thus, it is possible to obtain basic localization information to provide specific position estimations without additional costs of expensive sensors.