Energy-efficient location tracking with battery-powered devices using energy harvesting necessitates duty-cycling of GPS to prolong the system lifetime. We propose an energy and mobility-aware scheduling framework that adapts to real-world dynamics to achieve optimal long-term tracking performance. To forecast energy, the framework uses an exponentially weighted moving average filter to compute a virtual energy budget for the remainder of the forecast period. The virtual energy budget is then used as input for our proposed information-based GPS sampling approach, which estimates the current tracking error through dead-reckoning and schedules a new GPS sample when the error exceeds a given threshold. In order to improve the long-term tracking performance, the threshold is adapted based on the current energy and movement trends to balance the expected information gain from a new GPS sample with its energy cost. 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 and by using dead-reckoning with a fixed threshold. Our analysis shows that the proposed information-based GPS sampling strategy reduces the mean tracking error compared to existing methods and approaches the performance of the optimal offline sampling strategy.

Long-term outdoor localisation with battery-powered devices remains an unsolved challenge, mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, but they only provide coarse-grained control over GPS activity. In this paper, we introduce our feature-rich lightweight Camazotz platform as an enabler of Multimodal Activity-based Localisation~(MAL), which detects activities of interest by combining multiple sensor streams for fine-grained control of GPS sampling times. Using the case study of long-term flying fox tracking, we characterise the tracking, connectivity, energy, and activity recognition performance of our module under both static and 3-D mobile scenarios. We use Camazotz to collect empirical flying fox data and illustrate the utility of individual and composite sensor modalities in classifying activity. We evaluate MAL for flying foxes through simulations based on retrospective empirical data. The results show that multimodal activity-based localisation reduces the power consumption over periodic GPS and single sensor-triggered GPS by up to 77% and 14% respectively, and provides a richer event type dissociation for fine-grained control of GPS sampling.