Impact of the Number of Access Points in Indoor Fingerprinting Localization Juraj MACHAJ, Peter BRIDA, Barbora TATAROVA Dept. of Telecommunications and Multimedia, FEE, University of Žilina, Slovakia machaj@fel.uniza.sk, brida@fel.uniza.sk Abstract. In the paper the solution for indoor positioning based on IEEE 802.11 platform is proposed and experimentally verified. The paper investigates an impact of the number of access points on the localization accuracy in fingerprinting method. The solution is based on deterministic approach. The properties of the solution are tested by experimental measurements in real environment. Keywords Fingerprinting, indoor localization, experimental results. 1. Introduction The number of LBS (Location Based Services) is rising very fast in last years [1]. Basic requirement for LBS is to know user location. That can be achieved by various ways in dependency on environment, e.g. GPS (Global Positioning System) in outdoor. On the other hand, there can be problem with high signal attenuation in indoor environment and dense urban environments. Therefore alternative positioning solutions have to be used. Generally, they utilized various wireless communication platforms, e.g. cellular networks or IEEE 802.11x etc. There are numerous methods or algorithms that can be considered for implementation in wireless position location systems. The methods are utilized for MS (Mobile Station) position estimation. Positioning methods can be also divided into two basic categories: range based and range free [2]. The range based methods are based on the indirect measurements of distance or angle between network points [3, 4]. Range free solutions estimate MS location either by exploiting the radio connectivity information among neighboring mobile devices, or by exploiting the sensing capabilities of the mobile devices. Therefore the range free positioning solutions are cost- effective alternative to more expensive range based approaches. Positioning based on cellular networks is often used as alternative solution in urban environment. In urban environment, A-GPS can be also used to estimate location. Problem with localization is even bigger in indoor environment. Signal fluctuations are large because of multipath signal propagation. Many indoor localization algorithms and systems [5] based on Bluetooth [6], Zig- Bee [7], UWB (Ultra Wide Band) [8, 9], RFID [10] and IEEE 802.11x [11, 12] were developed. The most popular algorithms used in indoor environment are based on standard IEEE 802.11 [13, 14] also called Wi-Fi. Most of these algorithms use RSSI (Received Signal Strength Information) and are based on fingerprinting algorithm. The biggest advantage is that they do not need to create new infrastructure, only need utilized information about surrounding networks. Another advantage of fingerprinting algorithm seems to be multipath propagation resistance. Fingerprinting algorithm can be implemented in various ways from mathematical point of view. They can be divided into deterministic and probabilistic algorithms. In our work we deal with fingerprinting based on deterministic algorithm - nearest neighbor method (NN), which can perform as well as more complicated method, when density of radio map is high enough [15]. We deal with IEEE 802.11x, because our solution is implemented in indoor. Rest of paper is organized as follows. Section 2 describes fingerprinting localization algorithms. In Section 3 our fingerprinting algorithm and experimental environment is described. Experimental results are presented in Section 4. Finally, Section 5 concludes the paper and provides directions for future work. 2. Fingerprinting Algorithms In this section fingerprinting localization algorithms will be described. The fingerprinting algorithm has two phases - offline and online phase. In the offline phase radio map is created and recorded in database. In online phase, localization of mobile node is performed with use of radio map stored on server. Radio map construction starts by dividing area of interest into cells [16]. Each cell is represented by one reference point. In this point RSSI value from all transmitters in range – fingerprint is measured for certain 2010 20th International Conference Radioelektronika 978-1-4244-6321-3/10/$26.00 ©2010 IEEE