People Help People: A Pattern-matching Localization with Inputs from User Community

Abstract

Location-based services are regarded as a killer application of mobile networks. Among all RF-based localization techniques, the pattern-matching scheme is probably the most widely accepted approach. A key factor to its success is the accuracy concern and the calibration efforts to collect its training data. In this paper, we propose a community-based approach to reduce the calibration effort. We show how to get some volunteers (called co-trainers) to help add more training data to our location database. We also show how to rate the credit level of a co-trainer and the trust level of a piece of training data contributed by a co-trainer. We believe that our framework can greatly reduce the calibration effort of the pattern-matching localization scheme.