Software Simulation for Optimization k-NN Based Indoor Localisation Technique Using Spearman s Rank Correlation Coefficient

The reuse of existing Wireless Fidelity (Wi-Fi) setup for indoor localization using Wi-Fi Received Signal Strength Indicator (RSSI) is nowadays an active research domain. Over the period these Wi-Fi setups show degradation in performance owing to signal attenuation caused by multipath, along with environmental changes adversely affecting the functional efficiency. To optimize the indoor localization precision in the presence of the issues as mentioned earlier, I propose Spearman s Rank based Correlation Coefficient approach which finds the minimum distances and provides these distances to the original K-Nearest-Neighbor (k-NN) classifier which uses Euclidean distance. After the complete indoor Wi-Fi environment is simulated in Matrix Laboratory (Mat-lab) tool, the results so obtained are promising and on the higher side as compare to the original k-NN classifier performance. In case of distribution of cumulative errors the proposed method achieved low amount of localized errors of 2.7m for 80% tested samples. And as for shadow fading increase in value of 𝝈 improves the effect of the proposed method significantly.