Indoor Localization Using IPS with User Defined Privacy Preservation

Indoor Positioning System (IPS) has played a major part in using navigation inside an enclosed or indoor location. Predominant Smartphone as localization subsystems currently relies on server-side localization processes, allowing the service provider to know the location of a user at all time. Here we propose an algorithm to avoid the other sources from accessing personal data from the user hence avoiding data theft. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. Since the process of discovering, gathering patterns over large-scale trajectory databases can be quite lengthy, we further develop a set of well thought out techniques to improve the performance. We have evaluated our framework using a real prototype developed in Android and Hardtop HBase as well as realistic Wi-Fi traces scaling-up to several GBs. We can offer fine-grained localization in approximately four orders of magnitude less energy and number of Messages than competitive approaches.