Adaptive Duty Cycling for Energy Harvesting Systems

Jason Hsu, S. Zahedi, A. Kansal, M. Srivastava, V. Raghunathan
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引用次数: 243

Abstract

Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely: (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment; (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint; and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used
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能量收集系统的自适应占空循环
从环境中收集能量在许多应用中是可行的,以改善传感器网络中的能量限制。在本文中,我们提出了一种自适应占空比算法,该算法允许能量收集传感器节点根据环境中的能量可用性自主调整其占空比。该算法有三个目标,即:(a)实现能量中性运行,即能耗不超过环境提供的能量;(b)基于受上述能量中性约束的应用实用新型的系统性能最大化;(c)适应能源运行时的动态变化。我们提出了一个模型,使收集传感器节点能够根据历史数据预测未来的能源机会。我们还推导出了最大可实现性能的上限,假设对能源的未来行为有充分的了解。使用从原型太阳能收集平台收集的数据对我们的方法进行了评估,我们表明,与不使用收集感知电源管理的情况相比,我们的算法可以利用高达58%的环境能源
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