Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices
Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices
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DOI：
10.3390/s20030764
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发表时间：
2020-02-01
期刊：
影响因子：
3.9
通讯作者：
Lee, Hyung Gyu
中科院分区：
文献类型：
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作者：
Park, Jaehyun;Bhat, Ganapati;Lee, Hyung Gyu
可穿戴的物联网（IoT）设备可以实现各种生物医学应用，例如手势识别，健康监测和人类活动跟踪。尺寸和重量限制限制了电池容量，这会导致频繁的充电要求和用户不满意。最大程度地减少能源消耗不仅减轻了这个问题，而且还为在收获能源上运作的自动设备铺平了道路。本文考虑了在能量收获设备上运行的能量最佳的手势识别应用。我们首先提出一个优化问题，以最大程度地提高能源预算和准确性限制时所承认的手势数量。接下来，我们使用可穿戴的物联网设备原型从功耗测量中得出一个分析能量模型。然后，我们证明，最大化公认的手势数量等于最大程度地减少手势识别的持续时间。最后，我们利用此结果来构建一种优化技术，该技术在满足识别精度要求的同时最大程度地提高了能源预算限制下认识的手势数量。我们广泛的评估表明，与手动优化相比，提出的分析模型对于可穿戴物联网应用有效，并且优化方法将公认的手势数量提高了2.4倍。
Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4x compared to a manual optimization.