Fault Tolerant Fingerprint-Based Positioning

Abstract

The increasing demand for indoor location-based services has motivated the development of positioning methods that exploit the existing wireless network infrastructure. Accuracy is an important requirement, however fault tolerance is also highly desirable in case of failures or malicious attacks. We investigate the fault tolerance of fingerprint-based methods under a variety of fault or attack scenarios. We study the Subtract on Negative Add on Positive (SNAP) algorithm and modify it appropriately for the WLAN setup. Our results indicate that SNAP achieves adequate accuracy with very low computational complexity and exhibits smoother performance degradation in the presence of faults compared to other methods.

Towards planet-scale localization on smartphones with a partial radiomap

“…and select any of the available positioning methods from the Algorithms configuration panel, which includes the deterministic K-Nearest Neighbor (KNN) and Weighted K-Nearest Neighbor (WKNN) algorithms [1], as well as the probabilistic Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE) algorithms [19]. The Airplace platform additionally supports two state-of-the-art methods developed in-house, namely the Radial Basis Function (RBF) networks [12] and the Subtract on Negative Add on Positive (SNAP) [13] approaches.…”