Data-Centric Knowledge Discovery Strategy for a Safety-Critical Sensor Application

• Main Authors: Nilamadhab Mishra, Hsien-Tsung Chang, Chung-Chih Lin
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: International Journal of Antennas and Propagation
• Online Access: http://dx.doi.org/10.1155/2014/172186

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.