Energy measurement of computer devices, which are widely used in the Internet of Things (IoT), is an important yet challenging task. Most of these IoT devices lack ready-to-use hardware or software for power measurement. A cost-effective solution is to use low-end consumer-grade power meters. However, these low-end power meters cannot provide accurate instantaneous power measurements. In this paper, we propose an easy-to-use approach to derive an instantaneous software-based energy estimation model with only low-end power meters based on data-driven analysis through machine learning. Our solution is demonstrated with a Jetson Nano board and Ruideng UM25C USB power meter. Various machine learning methods combined with our smart data collection method and physical measurement are explored. Benchmarks were used to evaluate the derived software-power model for the Jetson Nano board and Raspberry Pi. The results show that 92% accuracy can be achieved compared to the long-duration measurement. A kernel module that can collect running traces of utilization and frequencies needed is developed, together with the power model derived, for power prediction for programs running in real environment.

翻译：物联网（IoT）中广泛使用的计算机设备的能量测量是一项重要且具有挑战性的任务。大多数此类物联网设备缺乏可直接用于功率测量的硬件或软件。一种经济有效的解决方案是使用低端消费级功率计。然而，这些低端功率计无法提供精确的瞬时功率测量。本文提出了一种易于使用的方法，仅基于低端功率计，通过机器学习的驱动数据分析，推导出瞬时的基于软件的能耗估计模型。我们的解决方案以Jetson Nano开发板和睿登UM25C USB功率计为例进行演示。我们探索了多种机器学习方法，结合我们提出的智能数据收集方法和物理测量。通过基准测试评估了为Jetson Nano开发板和树莓派推导出的软件功耗模型。结果表明，与长时间测量相比，该模型可达到92%的准确率。我们开发了一个内核模块，用于收集所需的利用率和频率运行轨迹，并结合推导出的功耗模型，对真实环境中运行的程序进行功耗预测。