GPS-Based Location Extraction and Presence Management for Mobile Instant Messenger

Location is the most essential presence information for mobile users. In this paper, we present an improved time-based clustering technique for extracting significant locations from GPS data stream. This new location extraction mechanism is incorporated with Google Maps for realizing a cooperative place annotation service on mobile instant messenger (MIM). We also design an ontology-based MIM presence model for inferring the location clues of IM buddies, to support context-aware presence management in our MIM system. The GPS-based location extraction algorithm has been implemented on a Smartphone and evaluated using a real-life GPS trace. We show that the proposed clustering algorithm can achieve more accurate location extraction as it considers the time interval of intermittent location revisits. The incorporation of location information with the high-level contexts, such as mobile user’s current activity and their social relationship, can achieve more efficient presence management and context-aware communication.