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Top1. Introduction
Nowadays, various location-based services have appeared in indoor and outdoor environments, such as medical facilities, shopping malls, and airports (Huang, 2018). Then, indoor localization systems using short-range wireless signals have been actively explored since the global positioning system (GPS) does not offer sufficient accuracy in identifying the floor or room of a user (Curran, 2011). For them, various wireless technologies including RFID, ultra wide-band (UWB), IEEE 802.11Wi-Fi, and Bluetooth (Ogun, 2018; Yao, 2017; Blasio, 2017 have been studied along with various localization techniques such as time of arrival (ToA) and time difference of arrival (TDoA), angle of arrival (AoA), trilateration, and pattern matching for solving the indoor positioning problem (Brena, 2017).
Among localization techniques, the fingerprinting method has gained the most attention due to its ability to achieve reasonable accuracy (Davidson, 2016). It does not require any additional device such as the directional antenna in AoA, the precise time synchronization in ToA and TDoA, or the complex distance calculation using the propagation models (Ammar, 2014).
This method achieves advantages by adopting the radio map pattern matching that consists of offline calibration and online detection phases. In the calibration phase, the radio map for every localization point in the target field is made by measuring the received signal strength (RSS) when the user is there. Then, in the detection phase, the current measured signal strengths are compared with every fingerprint in the radio map, and the closest one is selected as the current position.
The IEEE 802.11 protocol has been most popular among wireless technologies because it has been extensively deployed worldwide to offer wireless local-area networks (WLANs) for Internet access services. A smartphone can be used for the user-side transmission device, and an access point (AP) can be used as the system-side reception device, where a huge number of APs have been allocated in indoor environments.
However, the user-side device in this protocol consumes much energy to offer high-speed data communications, which needs an expensive and heavy battery. Then, it becomes difficult to always keep and activate the device during the long-time service. In addition, the detection accuracy depends on the type or brand of the device, which may transmit or receive the different levels for RSS (Alshami, 2017).