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CSI fingerprinting based localization system is one of the most promising techniques for indoor positioning. CSI phase sanitization and distance measure between the fingerprint and the test point are two important operations, both of which have significant influence on the localization performance. However, there are potential disadvantages of the two operations which limit the system performance in conventional algorithms. In this paper, we propose a novel CSI phase sanitization method and a new distance metric. The two methods are both tested using the CSI measured in office environment. Compared with the conventional methods, they achieve much better performance.

Massive amounts of geo-tagged and textually annotated images are provided by online photo services such as Flickr and Zommr. However, most existing image retrieval engines only consider text annotations. We present I2RS, a system that allows users to view geo-textual images on Google Maps, find hot topics within a specific geographic region and time period, retrieve images similar to a query image, and receive recommended images that they might be interested in. I2RS is a distributed geo-textual image retrieval and recommendation system that employs SPB-trees to index geo-textual images, and that utilizes metric similarity queries, including top- m spatio-temporal range and k nearest neighbor queries, to support geo-textual image retrieval and recommendation. The system adopts the browser-server model, whereas the server is deployed in a distributed environment that enables efficiency and scalability to huge amounts of data and requests. A rich set of 100 million geo-textual images crawled from Flickr is used to demonstrate that, I2RS can return high-quality answers in an interactive way and support efficient updates for high image arrival rates.