SensOrchestra: Collaborative Sensing for Symbolic Location Recognition

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, bandwidth, 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.