The indoor location market is forecasted by ABI Research to reach approximately $10B by 2020. However, this emerging area, suffers from limited positioning accuracy induced by the many existing indoor sensing platforms. We present novel noise-reduction and data-smoothing algorithms, designed to cleanse Wi-Fi-based indoor positioning data. An empirical evaluation of these algorithms demonstrated up to 75.5% improvement in the data accuracy. This improved accuracy opens new market opportunities in the retail and travel and transportation domains.