Research Article
Indoor Positioning Using Adaptive KNN Algorithm Based Fingerprint Technique
@INPROCEEDINGS{10.1007/978-3-030-05195-2_2, author={Mahmood Mosleh and Raed Abd-Alhameed and Osama Qasim}, title={Indoor Positioning Using Adaptive KNN Algorithm Based Fingerprint Technique}, proceedings={Broadband Communications, Networks, and Systems. 9th International EAI Conference, Broadnets 2018, Faro, Portugal, September 19--20, 2018, Proceedings}, proceedings_a={BROADNETS}, year={2019}, month={1}, keywords={Indoor Positioning System K-Nearest neighbors Fingerprint}, doi={10.1007/978-3-030-05195-2_2} }- Mahmood Mosleh
Raed Abd-Alhameed
Osama Qasim
Year: 2019
Indoor Positioning Using Adaptive KNN Algorithm Based Fingerprint Technique
BROADNETS
Springer
DOI: 10.1007/978-3-030-05195-2_2
Abstract
In this paper, an experiment of the indoor position location is applied to one floor of the selected building which is chosen as a case study. Four Access Points (APs) of 2.4 GHz are mounted on the experimented area. Their locations are determined using Ekahau Site Survey software to ensure the building is fully covered. A fingerprinting method is utilized as a localization algorithm to estimate the coordinate of the user. This method consists of two stages, namely disconnected data preparing stage and the on-line situating stage. The first one is applied by creating a Radio Map (RM) with 58 Reference Point (RP) in the tested area. A database included the Received Signal Strength (RSS) from all directions of each RP is recorded using Net Surveyor 0.2 Package. In the second phase, K-Nearest Neighbor (KNN) method with fix value of K is applied to estimate the position location. The results show that the average absolute error between actual and estimated coordination equal to 1.796044 m and average elapsed time equal 0.030439 s, which is unacceptable in our opinion because the localization system must be more accurate. To address this problem, a proposed improvement on KNN algorithm with a variable K is presented in this paper. The idea is to vary the value of K according to the difference between the measured signals and the corresponding value of the stored database. The results show that the adapted algorithm led to a significant decrease of 46% and 52% for absolute error and elapsed time respectively.
