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, the authors 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.