Reducing the Computational Cost of Ratio-Based Indoor Localization

With Received Signal Strength (RSS) based indoor localization gaining popularity, recent research work has explored many different aspects of such localization systems, for instance, accuracy and deployment cost. In this paper, we study the computational cost of a ratio-based localization algorithm. Specifically, we use the algorithm´s inherent distance ratio relationship to refine the physical region where positions need to be examined for localization. We evaluated our approach with data collected from our department building at Lafayette College. The experiments showed that the proposed approach achieved an average 74.1% reduction in the initial candidate region, which also effectively translated to an average 83.8% reduction in localization delay.