Characteristic analysis of fingerprint datasets from a pragmatic view of indoor localization using machine learning approaches

Due to their availability on commercial smartphones, WiFi, Bluetooth, and magnetometer are commonly utilized for indoor localization as indoor spaces are GPS deprived. Indoor localization falls into the category of data-intensive applications. In this domain, most of the recent solution approaches deploy machine learning (ML) and deep learning (DL) techniques on the data collected through the sensors. However, the publicly available benchmark datasets on indoor localization suffer from certain issues requiring complicated and customized data preprocessing techniques for each dataset for applying a common ML/DL technique. Thus, a fair comparison of the ML/DL methods for indoor localization datasets and hence to check for the generality of a solution that spans across different indoor regions become infeasible. In this comparative study, we have investigated three key challenges of fingerprint datasets that should be addressed for real-life localization applications, namely (i) repetitive site survey, (ii) device heterogeneity, and (iii) granularity and subregion-specific performance variation. To demonstrate how these attributes might impact localization performance, experimental analysis is performed using five benchmark datasets. The novelty of the work is that it not only highlights the challenges but also analyzes the feasibility of possible future directions to address these challenges through implementation results. Formulating the application of a generative adversarial network to address the issue of repetitive site surveys has been discussed with implementation results.