Zone-based Indoor Localization using Neural Networks: a View from a Real Testbed

Precise indoor localization is of great importance to automatically track people or objects indoors and plays a vital role in modern life. Despite a number of innovative research present in the literature indoor localization still remains an open problem. To trace the main reason we identify that in the present literature the tendency is to pinpoint the exact coordinates of a target device although most of the location based services (LBSs) do not require exact coordinates. To support LBS, one can simply divide the area of interest into several zones and perform "zone-fencing", i.e., find under which zone the user is currently located at. In this paper, we propose a zone-based indoor localization scheme using neural networks. With the results from real world indoor settings, we show that a number of empty clusters is generated when the traditional counter propagation network (CPN) is applied as is. But a slight modification to the CPN reduces the number of empty clusters significantly and provides promising accuracy. The proposed scheme outperforms "k-Nearest Neighbor algorithm" (k-NN) and its promising accuracy makes it suitable for real-world deployment.