Indoor positioning and distance-aware graph-based semi-supervised learning method

The growing interest for location-based services motivates many researchers to study different localization techniques for indoor environments. The main objective of these studies is to find a balance point between the accuracy of the scheme and its deployment/training cost. RSS-based schemes and in particular Graph-based Semi-Supervised Learning (G-SSL) constitute a group of techniques which has low setup cost and good localization accuracy. In this paper, we analyze the G-SLL scheme and show that, despite its high performance, the G-SSL method (in its original format) is not a very accurate model for a localization problem. Based on this observation and to improve the accuracy of localization, we propose an alternative approach which incorporates our knowledge of wireless signal propagation into the label propagation mechanism. Experimental results are then used to evaluate the performance of the proposed scheme compared to the original G-SSL.