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.

中文翻译：

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用于永久轨迹跟踪的能量和移动性感知调度

使用能量收集的电池供电设备进行节能位置跟踪需要 GPS 的占空比以延长系统寿命。我们提出了一种能源和移动感知调度框架，该框架适应现实世界的动态，以实现最佳的长期跟踪性能。为了预测能源，该框架使用指数加权移动平均滤波器来计算预测期剩余时间的虚拟能源预算。然后将虚拟能量预算用作我们提出的基于信息的 GPS 采样方法的输入，该方法通过航位推测法估计当前跟踪误差，并在误差超过给定阈值时安排新的 GPS 样本。为了提高长期跟踪性能，该阈值根据当前的能量和运动趋势进行调整，以平衡来自新 GPS 样本的预期信息增益与其能量成本。我们评估我们对野生飞狐的经验轨迹的方法，并将其与使用固定和自适应占空比以及使用具有固定阈值的航位推算的 GPS 采样策略进行比较。我们的分析表明，与现有方法相比，所提出的基于信息的 GPS 采样策略降低了平均跟踪误差，并接近最佳离线采样策略的性能。