This paper introduces a novel algorithm for the location-awareness problem of estimating symbolic user location for indoor spaces using IEEE 802.11 (WiFi) wireless signals. The characteristic of the problem is that the signals fluctuate greatly, not only across perturbations in space, but also in time (diurnally), which leads to poor location estimation.

The nk-Nearest Neighbour (nk-NN) Algorithm is an instance-based learning algorithm which normalizes the sample data set of the WiFi signal strength and signal quality to achieve maximum correct result of symbolic user location at a room scale. The data normalization is found to play an important role in determining the quality of the training data-set which has direct impact on the estimation result. The algorithm has been compared to other k-Nearest Neighbour (k-NN) and shows promising results.