EMI: Energy Management Meets Imputation in Wearable IoT Devices

被引:0
作者
Hussein, Dina [1 ]
Yamin, Nuzhat [1 ]
Bhat, Ganapati [1 ]
机构
[1] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99164 USA
基金
美国国家科学基金会;
关键词
Smart agriculture; Accuracy; Electromagnetic interference; Stochastic processes; Transforms; Turning; Imputation; Sensors; Internet of Things; Biomedical monitoring; Energy harvesting; energy management; wearable devices; wearable sensors;
D O I
10.1109/TCAD.2024.3448379
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Wearable and Internet of Things (IoT) devices are becoming popular in several applications, such as health monitoring, wide area sensing, and digital agriculture. These devices are energy-constrained due to limited battery capacities. As such, IoT devices harvest energy from the environment and manage it to prolong operation of the system. Stochastic nature of ambient energy, coupled with small battery sizes may lead to insufficient energy for obtaining data from all sensors. As a result, sensors either have to be duty cycled or subsampled to meet the energy budget. However, machine learning (ML) models for these applications are typically trained with the assumption that data from all sensors are available, leading to loss in accuracy. To overcome this, we propose a novel approach that combines data imputation with energy management (EM). Data imputation aims to substitute missing data with appropriate values so that complete sensor data are available for application processing, while EM makes energy budget decisions on the devices. We use the energy budget to obtain complete data from as many sensors as possible and turn off other sensors instead of duty cycling all sensors. Then, we use a low-overhead imputation technique for unavailable sensors and use them in ML models. Evaluations with six diverse datasets show that the proposed EM with imputation approach achieves 25%-55% higher accuracy when compared to duty cycling or subsampling without using additional energy.
引用
收藏
页码:3792 / 3803
页数:12
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