Data-centric knowledge discovery strategy for a safety-critical sensor application

Nilamadhab Mishra, Hsien Tsung Chang*, Chung Chih Lin

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

8 引文 斯高帕斯(Scopus)

摘要

In an indoor safety-critical application, sensors and actuators are clustered together to accomplish critical actions within a limited time constraint. The cluster may be controlled by a dedicated programmed autonomous microcontroller device powered with electricity to perform in-network time critical functions, such as data collection, data processing, and knowledge production. In a data-centric sensor network, approximately 3-60% of the sensor data are faulty, and the data collected from the sensor environment are highly unstructured and ambiguous. Therefore, for safety-critical sensor applications, actuators must function intelligently within a hard time frame and have proper knowledge to perform their logical actions. This paper proposes a knowledge discovery strategy and an exploration algorithm for indoor safety-critical industrial applications. The application evidence and discussion validate that the proposed strategy and algorithm can be implemented for knowledge discovery within the operational framework.

原文英語
文章編號172186
期刊International Journal of Antennas and Propagation
2014
DOIs
出版狀態已出版 - 2014

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