Jun-geun Park

Implications of device diversity for organic localization

Many indoor localization methods are based on the association of 802.11 wireless RF signals from wireless access points (WAPs) with location labels. An “organic” RF positioning system relies on regular users, not dedicated surveyors, to build the map of RF fingerprints to location labels. However, signal variation due to device heterogeneity may degrade localization performance. We analyze the diversity of those signal characteristics pertinent to indoor localization — signal strength and AP detection — as measured by a variety of 802.11 devices. We first analyze signal strength diversity, and show that pairwise linear transformation alone does not solve the problem. We propose kernel estimation with a wide kernel width to reduce the difference in probability estimates. We also investigate diversity in access point detection. We demonstrate that localization performance may degrade significantly when AP detection rate is used as a feature for localization, and correlate the loss of performance to a device dissimilarity measure captured by Kullback-Leibler divergence. Based on this analysis, we show that using only signal strength, without incorporating negative evidence, achieves good localization performance when devices are heterogeneous.

Online pose classification and walking speed estimation using handheld devices

We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.

Mole: A scalable, user-generated WiFi positioning engine

We describe the design, implementation, and evaluation of Mole, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Mole uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Mole aims for a more flexible and scalable point in the design space of localization systems. Mole employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable “cloud”-based fingerprint distribution system. Moles localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a places 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 localizers. We also show that non-experts can use Mole to quickly survey a building, enabling room-grained location-based services for themselves and others.

Molé: a scalable, user-generated WiFi positioning engine

We describe the design, implementation, and evaluation of Molé, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Mole 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 localization systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable “cloud”-based fingerprint distribution system. Molés localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a places 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 localizers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.