Janko Vito, Luštrek Mitja
Department of Intelligent Systems, Jozef Stefan Institute, 1000 Ljubljana, Slovenia.
Sensors (Basel). 2021 Jan 24;21(3):766. doi: 10.3390/s21030766.
Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions-in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.
使用可穿戴设备进行上下文识别是一个成熟的研究领域，但它面临的最大问题之一是用于感测和处理数据的设备能耗过高。在这项工作中，我们提出了三种不同的方法来优化其能源使用。我们还展示了如何将这三种方法结合起来以进一步提高节能效果。这些方法通过使系统设置（使用的传感器、采样频率、占空比等）适应检测到的上下文以及直接适应传感器数据来发挥作用。这是通过对不同系统设置的影响进行数学建模并使用多目标优化来找到最佳设置来实现的。所提出的方法在四个不同的上下文识别任务上进行了测试，我们表明它可以生成准确的节能解决方案——在一个案例中，以仅降低四个百分点的准确率为代价，将能耗降低了95%。我们还表明该方法具有通用性，几乎不需要关于被优化领域的专业知识，并且它优于相关工作中的两种方法。
