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 rollout-based runtime energy-allocation framework that optimizes the utility of the target device under energy constraints. 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 state-of-the-art techniques, the proposed framework achieves 34.6% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has less than 0.1% energy overhead compared to iterative approaches with a negligible loss in utility.
翻译:能源采收为低能装置提供了有吸引力和有希望的动力机制。然而,光靠能源采收本身不足以实现能源中性操作,从而消除乏味的电池充电和更换要求。实现能源中性操作具有挑战性,因为收获能源的不确定性破坏了服务质量要求。为了应对这一挑战,我们提出了一个基于运行运行时间的能源分配框架,在能源限制的情况下优化目标装置的效用。拟议框架使用高效的迭代算法,在一天开始时计算初始能源分配。然后,对初步分配进行每间隔修正,以弥补与预期能源采收模式的偏差。我们利用太阳能和移动能源采收模式以及来自4772个不同用户的美国时间利用调查数据来评估这一框架。与最新技术相比,拟议框架即使在能源有限的情况下也实现了34.6%的更高效用。此外,对耗损率装置原型的测量显示,拟议的框架比迭代方法少得可怜,比迭代方法低0.1%的能源间接费用要小。