WSNB:基于物联网环境基站的神经网络可穿戴传感器

Alaa Mheisn, M. Shurman, A. Alma'aitah
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引用次数: 0

摘要

物联网(IoT)是最近引入的一种系统范式,它包括不同的智能设备和应用,特别是在智能城市中,例如;制造业,家庭和办公室。为了提高它们的感知能力,在它们的框架中添加更多的传感器是有吸引力的。在本文中,我们提出增加一种新的传感器,作为可穿戴传感器与位于基站(WSNB)上的神经网络无线连接。WSNB使添加的传感器能够通过主动学习来改进它们的标签。新型传感器平均精度达到93.81%,比现有方法提高了4.5%,消除了人工支持,在基站中采用神经网络方法增加了传感器的生命周期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WSNB: Wearable Sensors with Neural Networks Located in a Base Station for IoT Environment
The Internet of Things (IoT) is a system paradigm that recently introduced, which includes different smart devices and applications, especially, in smart cities, e.g.; manufacturing, homes, and offices. To improve their awareness capabilities, it is attractive to add more sensors to their framework. In this paper, we propose adding a new sensor as a wearable sensor connected wirelessly with a neural network located on the base station (WSNB). WSNB enables the added sensor to refine their labels through active learning. The new sensors achieve an average accuracy of 93.81%, which is 4.5% higher than the existing method, removing human support and increasing the life cycle for the sensors by using neural network approach in the base station.
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