WLAN Based Position Estimation System Using Classification Fuzzy K-Nearest Neighbor (FK-NN)

International Conference on SMART CITY Innovation 2018

Malik, R.F.^1 ; Mardiah^1 ; Farissi, A.^1 ; Stiawan, D.^1 ; Zulfahmi, R.^1 ; Ahmad, M.R.^2 ; Khirbeet, A.S.^2

Communication Network and Information Security Research Lab, Faculty of Computer Science, Universitas Sriwijaya Jalan Srijaya Negara Bukit Besar, Bukit Lama, Ilir Bar. I, Kota Palembang, Sumatera Selatan

30128, Indonesia^1

Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Jalan Hang Tuah Jaya, Durian Tunggal, Melaka

76100, Malaysia^2

关键词: Accuracy percentages;    Classification methods;    Emerging technologies;    Fuzzy k nearest neighbor (FKNN);    K-nearest neighbours;    Multiple access points;    Position estimation system;    Received signal strength;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/248/1/012003/pdf
DOI  :  10.1088/1755-1315/248/1/012003

来源: IOP

【 摘 要 】

Increasing the number of public hotspots using Wi-Fi technology is one of opportunity to gain advantage for proposing many new technologies. One of emerging technology is an estimation system to locate the object/person position using Wi-Fi. The object estimation position is the technology to estimate object position accuracy, using signal Received Signal Strength (RSS) from Wi-Fi Access Point. The RSS is an information about the strength of the signal indicates the distance between the access point device. Through the Indoor Positioning System (IPS), RSS value information from multiple access points are processed in order to provide position information. In this study, the IPS using Fuzzy K-Nearest Neighbour (FK-NN) classification method which is a combination of Fuzzy algorithm and K-NN to increase the accuracy of the object estimation position based on the learning data as reference point. Through hybridization from the algorithm is expected to calculate the position estimation more effectively and accurately and minimize errors in estimation. The results show that the algorithm FK-NN obtain the average location error of 2.4 meters with an accuracy percentage of 76%.