SensOrchestra: Collaborative Sensing for Symbolic Location Recognition
Abstract
Symbolic location of a user, like a store name in a mall, is essential for context-based mobile advertising. Existing fingerprint-based localization using only a single phone is susceptible to noise, and has a major limitation in that the phone has to be held in the hand at all times. In this paper, we present SensOrchestra, a collaborative sensing framework for symbolic location recognition that groups nearby phones to recognize ambient sounds and images of a location collaboratively. We investigated audio and image features, and designed a classifier fusion model to integrate estimates from different phones. We also evaluated the energy consumption, band- width, and response time of the system. Experimental results show that SensOrchestra achieved 87.7% recognition accuracy, which reduces the error rate of single-phone approach by 2X, and eliminates the limitations on how users carry their phones. We believe general location or activity recognition systems can all benefit from this collaborative framework.
Suggested Citation
Heng-Tze Cheng, Feng-Tso Sun, Senaka Buthpitiya, and Martin L. Griss. "SensOrchestra: Collaborative Sensing for Symbolic Location Recognition" Proc. MobiCASE 2010 (2010).
Available at: http://works.bepress.com/martin_griss/8
Included in
Computer and Systems Architecture Commons, Data Storage Systems Commons, Digital Circuits Commons, Digital Communications and Networking Commons, Hardware Systems Commons, Other Computer Engineering Commons, Other Electrical and Computer Engineering Commons, Robotics Commons, Systems and Communications Commons, VLSI and circuits, Embedded and Hardware Systems Commons