Abstract:
Indoor localization has become inevitable in the technological sector especially when a
majority of stand-alone as well as commercial applications require accurate location
estimate for the users. Since GPS signals are insufficient for indoor localization of object
Therefore, Wi-Fi based fingerprinting have received great attention for such localization,
among the available Wi-Fi fingerprint-based localization techniques, K-Nearest Neighbor
(KNN) is most popular. However, existing techniques failed to consider the fact that equal
RSS spaces at different RSS level may not mean identical geometric distances in complex
indoor environment in calculating the signal distance among the different RSS vectors.
To address this issue, Feature-Scaling-based K-Nearest Neighbor (FS-KNN) algorithm is
proposed for achieving improved localization accuracy.
In this research, we have proposed the Genetic algorithm (GA) based localization to
minimize the error distance between the estimated position and actual position. Genetic
algorithm solves problems through natural selection and evolutionary processes based on
genes. It works with a population of solutions and attempts to guide the research toward
improvement, using the survival of the fitness, which can improve the accuracy, stability
and robustness of the localization gadget receiving RF signal from access point. A series
of cumulative distribution plots rectify the performance measurement of this research and
show that our proposed technique give better results in comparison to the previous
techniques. The simulations were performed in MATLAB
