iUpdater: Low Cost RSS Fingerprints Updating for Device-free Localization
Liqiong Chang, Jie Xiong, Yu Wang, Xiaojiang Chen, Junhao Hu and Fang Dingyi
Northwest University, Singapore Management University, University of North Carolina at Charlotte, Northwest University, Northwest University, Northwest University

While most existing indoor localization techniques are device-based, many emerging applications such as intruder detection and elderly care drive the needs of device-free localization, in which the target can be localized without any device attached. Among the diverse techniques, received signal strength (RSS) fingerprint-based methods are popular because of the wide availability of RSS readings in most commodity hardware. However, current fingerprint-based systems suffer from high human labor cost to updates of the fingerprint database and low accuracy due to large degree of RSS variations. In this paper, we propose a fingerprint-based device-free localization system named iUpdater to significantly reduce the labor cost and increase the accuracy. We present a novel selfaugmented regularized singular value decomposition (RSVD) method integrating the sparse attribute with unique properties of the fingerprint database. iUpdater is able to accurately update the whole database with RSS measurements at a small number of reference locations, thus reducing the human labor cost. Furthermore, iUpdater observes that although the RSS readings varies a lot, the RSS differences between both the neighboring locations and adjacent wireless links are relatively stable. This unique observation is applied to overcome the short-term RSS variations to improve the localization accuracy. Extensive experiments in three different environments over 3 months demonstrate the effectiveness and robustness of iUpdater.