Signal Processing for Time Domain Wavelengths of Ultra-Weak FBGs Array in Perimeter Security Monitoring Based on Spark Streaming

• Main Authors: Zhenhao Yu, Fang Liu, Yinquan Yuan, Sihan Li, Zhengying Li
• Format: Article
• Language: English
• Published: MDPI AG 2018-09-01
• Series: Sensors
• Subjects: FBGs signal processing; perimeter security monitoring; spark streaming; AP-DBSCAN
• Online Access: http://www.mdpi.com/1424-8220/18/9/2937

To detect perimeter intrusion accurately and quickly, a stream computing technology was used to improve real-time data processing in perimeter intrusion detection systems. Based on the traditional density-based spatial clustering of applications with noise (T-DBSCAN) algorithm, which depends on manual adjustments of neighborhood parameters, an adaptive parameters DBSCAN (AP-DBSCAN) method that can achieve unsupervised calculations was proposed. The proposed AP-DBSCAN method was implemented on a Spark Streaming platform to deal with the problems of data stream collection and real-time analysis, as well as judging and identifying the different types of intrusion. A number of sensing and processing experiments were finished and the experimental data indicated that the proposed AP-DBSCAN method on the Spark Streaming platform exhibited a fine calibration capacity for the adaptive parameters and the same accuracy as the T-DBSCAN method without the artificial setting of neighborhood parameters, in addition to achieving good performances in the perimeter intrusion detection systems.