Molé: a scalable, user-generated WiFi positioning engine

Abstract

We describe the design, implementation, and evaluation of Molé, a mobile organic localisation engine. Unlike previous work on crowd-sourced WiFi positioning, Molé uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Molé aims for a more flexible and scalable point in the design space of localisation systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighbouring places, a motion detector to reduce update lag, and a scalable ‘cloud’-based fingerprint distribution system. Molé's localisation algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localisers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.