Adaptive threshold triggering of GPS for long-term tracking in WSN

Long-term tracking is an expanding field with applications in logistics, ecology and wearable computing. The main challenge for longevity of tracking applications is the high energy consumption of GPS, which has been addressed by using low power sensors to trigger GPS activation upon detecting events of interest. While triggering can reduce power consumption, static thresholds can underperform in the longterm as context changes. This paper presents an auto-covariance based triggering algorithm that adapts trigger thresholds based on the incoming data and is effective with limited prior knowledge. We test the algorithm on empirical data from flying foxes and show that it outperforms static thresholding and existing adaptive algorithms from the literature.