Geometric-based KNN localization using sensor dissimilarity information

Abstract

Location fingerprinting is a range-free approach to GPS-free localization. Conventionally, the fingerprint space is defined as a feature vector space where a fingerprint is a vector of location-sensitive measurements associated with a location. However, in practice, it is hard to find a quality feature space that is robust to device heterogeneity and environment and infrastructure dynamics. This paper advocates a fundamentally different model where a fingerprint is defined as a dissimilarity measurement associated with a pair of locations and proposes a localization approach based on geometric embedding.