Pallas: Self-Bootstrapping Fine-Grained Passive Indoor Localization Using WiFi Monitors

Abstract

Passive indoor localization for smartphones requires no explicit cooperation of the smartphone and enables a new spectrum of applications such as passive user tracking, mobility monitoring, social pattern analysis, etc. However, existing passive localization methods either achieve coarse-grained localization accuracy or require expensive infrastructure support. In this paper, we present Pallas, a self-bootstrapping system for fine-grained passive indoor localization using non-intrusive WiFi monitors. Pallas uses off-the-shelf access point hardware to opportunistically capture WiFi packets to infer the location of smartphones in the indoor environment. The key novelty of Pallas lies in that the passive fingerprint database for localization is automatically constructed and updated without any active participation of WiFi devices or manual calibration. To achieve this, Pallas first identifies passive landmarks that are present in WiFi RSS traces. Given the knowledge of the indoor floor plan and the location of WiFi monitors, Pallas statistically maps the collected RSS traces to specific indoor pathways. With sufficient mapping opportunistically detected, Pallas is able to bootstrap a fine-grained passive fingerprint database and build Gaussian processes for localization automatically without requiring any additional calibration effort.