ECO：通过对收集的能量进行合理分配，实现能量中性IoT设备
ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy
摘要：
能量收集提供了一种有吸引力的和有前途的机制，为低能量设备供电。然而，它本身不足以实现能量中性操作，这可以消除繁琐的电池充电和更换要求。实现能源中性运营具有挑战性，因为所收集能源的不确定性破坏了服务质量要求。为了解决这一挑战，我们提出了一个运行时的能量分配框架，优化能源约束下的目标设备的效用，使用推出算法，这是一个顺序的方法来解决动态优化问题。所提出的框架使用一种有效的迭代算法来计算初始能量分配在一天的开始。然后，在每个间隔处校正初始分配，以补偿与预期能量收集模式的偏差。我们使用太阳能和运动能量收集模式和来自4772个不同用户的美国时间使用调查数据来评估这个框架。与现有技术相比，即使在能源有限的情况下，所提出的框架也实现了高达35%的高效用。此外，对可穿戴设备原型的测量表明，所提出的框架具有比迭代方法小1000倍的能量开销，并且效用损失可以忽略不计。
英文摘要：
Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a runtime energy-allocation framework that optimizes the utility of the target device under energy constraints using a rollout algorithm, which is a sequential approach to solve dynamic optimization problems. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users. Compared to prior techniques, the proposed framework achieves up to 35% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has 1000x smaller energy overhead than iterative approaches with a negligible loss in utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
