Organic Indoor Location Discovery Service


The goal of this project is to develop an organic indoor location discovery service, such that a mobile user with a wifi-enabled device can discover her/his location to roughly room granularity.


Project Lead: Prof. Seth Teller

Collaborators: Jonathan Ledlie (Nokia Research); Dorothy Curtis; Jamey Hicks (Nokia Research); Einat Minkov

Students: Jun-geun Park; Yoni Battat; Ben Charrow; David Lambeth; Dwayne Reeves; Russell Ryan; Ami Patel; Maria Frendberg


Online Pose Classification and Walking Speed Estimation using Handheld Devices


Jun-geun Park, Ami Patel, Dorothy Curtis, Jonathan Ledlie, Seth Teller

Proc. 14th International Conference on Ubiquitous Computing (UbiComp 2012), 2012

Molé: a Large-Scale, User-Generated Positioning Engine


Jonathan Ledlie, Jun-geun Park, Dorothy Curtis, André Cavalcante, Leonardo Camara, Robson Vieira

Proc. International Conference on Indoor Positioning and Indoor Navigation (IPIN '11), 2011

Implications of Device Diversity for Organic Localization


Jun-geun Park, Dorothy Curtis, Seth Teller, Jonathan Ledlie

Proc. the 30th IEEE International Conference on Computer Communications (INFOCOM '11), pp.3182 - 3190, 2011

Collaborative Future Event Recommendation


Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth Teller, Tommi Jaakkola

Proc. 19th ACM International Conference on Information and Knowledge Management (CIKM '10)

Growing an Organic Indoor Location System


Jun-geun Park, Ben Charrow, Jonathan Battat, Dorothy Curtis, Einat Minkov, Jamey Hicks, Seth Teller, Jonathan Ledlie

Proc. 8th International Conference on Mobile Systems, Applications, and Services (MobiSys '10), pp.271 - 284, 2010

Organic Indoor Location Discovery


Seth Teller, Jonathan Battat, Ben Charrow, Dorothy Curtis, Russell Ryan, Jonathan Ledlie, Jamey Hicks

MIT CSAIL Technical Report, MIT-CSAIL-TR-2008-075, 2008


The dataset used in Implications of Device Diversity for Organic Localization (INFOCOM '11) can be downloaded from here (970kB). This dataset contains 802.11 RF-scans from six heterogeneous devices for 18 locations.

The dataset used in Online Pose Classification and Walking Speed Estimation using Handheld Devices is available here (47MB). This dataset includes three rounds of experiments from 16 participants, capturing acceleration data with a handheld device.


We are grateful to Nokia Research Center for supplying WiFi-enabled phones as test devices for this project.