Exploiting Location Semantics for Realizing Cross-Referencing Proactive Location-Based Services

Abstract

Location-based Services (LBS) are one of the longest-standing value-added services in the mobile communications industry. The location of a user is the fundamental factor shaping such services and is usually computed solely in terms of the physical location relying on Reverse Geocoding APIs. It does not take into consideration the semantics of the location, but rather only the geographic spatial information, which significantly restricts the intelligibility of the provided LBS. In order to overcome the aforementioned limitations, we have introduced a Semantic Positioning Platform in a previous work being capable of providing semantically enriched self-referencing LBS. In this paper, we extend the platform by enabling cross-referencing proactive LBS (i.e., third-party tracking) based on semantically modeled user-specific location profiles (e.g., school or office) in combination with social relations among users. Furthermore, the independent platforms delivering the Semantic Positioning functionality (i.e., the Positioning Enabler and the Open Mobile Network) have been integrated into the Context Data Cloud, which is a context management ecosystem for delivering semantically enriched context-aware services. In addition, the Context Data Cloud for Android application including a Friend Tracker function has been implemented as a proof of concept. The evaluation in terms of battery consumption and positioning accuracy highlights the added value of our approach.