Reliable Geofence Activation with Sparse and Sporadic Location Measurements: Extended Version

Abstract

Geofences are a fundamental tool of location-based services. A geofence is usually activated by detecting a location measurement inside the geofence region. However, location measurements such as GPS often appear sporadically on smartphones, partly due to weak signal, or privacy preservation, because users may restrict location sensing, or energy conservation, because sensing locations can consume a significant amount of energy. These unpredictable, and sometimes long, gaps between measurements mean that entry into a geofence can go completely undetected. In this paper we argue that short term location prediction can help alleviate this problem by computing the probability of entering a geofence in the future. Complicating this prediction approach is the fact that another location measurement could appear at any time, making the prediction redundant and wasteful. Therefore, we develop a framework that accounts for uncertain location predictions and the possibility of new measurements to trigger geofence activations. Our framework optimizes over the benefits and costs of correct and incorrect geofence activations, leading to an algorithm that reacts intelligently to the uncertainties of future movements and measurements.