Context-aware services rely critically on accurate and energy-efficient location tracking. While GPS receivers offer high accuracy positioning, energy harvesting and storage constraints of battery-powered devices necessitate dutycycling of GPS to prolong the system lifetime. Furthermore, real-world dynamics dictate that the GPS sampling strategy adapts in real-time to achieve optimal positioning performance. We propose an information-based approach to solve the problem of online adaptive GPS sampling. We estimate the current positioning error through dead-reckoning and schedule a new GPS sample when the error exceeds a given threshold. The threshold adapts based on the current energy and movement trends to balance the expected information gain from a new GPS sample with its cost for longerterm tracking performance. We evaluate our approach on empirical traces from wild flying foxes and compare it to strategies that sample GPS using fixed and adaptive duty cycles. Our analysis shows...