EHDC: An Energy Harvesting Modeling and Profiling Platform for Body Sensor Networks
EHDC: An Energy Harvesting Modeling and Profiling Platform for Body Sensor Networks
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DOI：
10.1109/jbhi.2017.2733549
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发表时间：
2018
影响因子：
7.7
通讯作者：
Dawei Fan;Luis Lopez Ruiz;Jiaqi Gong;J. Lach
中科院分区：
文献类型：
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作者：
Dawei Fan;Luis Lopez Ruiz;Jiaqi Gong;J. Lach
能量收集是一个有前途的解决方案，有限的电池寿命的身体传感器节点。能够准永久运行的自供电传感器系统使临床之外的患者真正连续监测成为可能。然而，在现实世界中收获的不连续性和动态特性及其对自供电系统的设计和操作的影响还没有得到很好的理解。本文提出了一个移动的能量收集和数据收集（EHDC）平台，旨在提供更深入的了解能量收集动态。EHDC平台监测和记录穿戴式采集器产生的瞬时可用功率，同时还收集人类活动和环境数据，以提供对身体传感器网络常见的两种能量采集模式的全面真实评估：太阳能和热电。该平台最初通过台式测试进行了验证，后来在两个主题上进行了实际部署。7小时长的多模态能量收集配置文件的生成，和环境和行为数据被用来扩展先前开发的基于卡尔曼滤波器的能量收集预测的数学模型。结果证实了EHDC平台和收集模型的有效性，建立了长期监测能量收集特性的潜力，从而为自供电身体传感器网络的设计和操作提供了信息。
Energy harvesting is a promising solution to the limited battery lifetimes of body sensor nodes. Self-powered sensor systems capable of quasi-perpetual operation enable the possibility of truly continuous monitoring of patients beyond the clinic. However, the discontinuous and dynamic characteristics of harvesting in real-world scenarios—and their implications for the design and operation of self-powered systems—are not yet well understood. This paper presents a mobile energy harvesting and data collection (EHDC) platform designed to provide a deeper understanding of energy harvesting dynamics. The EHDC platform monitors and records the instantaneous usable power generated by body-worn harvesters, while also collecting human activity and environmental data to provide a comprehensive real-world evaluation of two energy harvesting modalities common to body sensor networks: solar and thermoelectric. The platform was initially validated with benchtop tests and later with real-world deployments on two subjects. 7-h-long multimodal energy harvesting profiles were generated, and the environmental and behavioral data were used to expand upon previously developed Kalman filter based mathematical models for energy harvesting prediction. Results confirm the validity of the EHDC platform and harvesting models, establishing the potential for longer term monitoring of energy harvesting characteristics; thus, informing the design and operation of self-powered body sensor networks.