CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

Abstract EN

Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge.

Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models.

Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required.

Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information.

We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs).

Principal component analysis was applied for dimension reduction of raw data.

Conducted experimentation demonstrates that it provides 97% accuracy for room prediction.

However, artificial neural networks, k-nearest neighbors, K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively.

It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.