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

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.

[1]  Cai Yingkun.  An Improved DBSCAN Algorithm which is Insensitive to Input Parameters , 2004 .

[2]  Guo Ji-dong,et al.  A statistical information-based clustering approach in distance space , 2005, Journal of Zhejiang University-SCIENCE A.

[3]  Fred W. Glover,et al.  A Tabu search based clustering algorithm and its parallel implementation on Spark , 2017, Appl. Soft Comput..

[4]  Mustapha Lebbah,et al.  Micro-Batching Growing Neural Gas for Clustering Data Streams Using Spark Streaming , 2015, INNS Conference on Big Data.

[5]  Prasanta K. Jana,et al.  A Prototype-Based Modified DBSCAN for Gene Clustering , 2012 .

[6]  Antonello Cutolo,et al.  An Intrusion Detection System for the Protection of Railway Assets Using Fiber Bragg Grating Sensors , 2014, Sensors.

[7]  M. Tyers,et al.  BoxPlotR: a web tool for generation of box plots , 2014, Nature Methods.

[8]  Sudipto Guha,et al.  Streaming-data algorithms for high-quality clustering , 2002, Proceedings 18th International Conference on Data Engineering.

[9]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[10]  Wei Hu,et al.  Twitter spammer detection using data stream clustering , 2014, Inf. Sci..

[11]  S. Hinckley,et al.  Optical Fiber Bragg grating based intrusion detection systems for homeland security , 2013, 2013 IEEE Sensors Applications Symposium Proceedings.

[12]  Minghong Yang,et al.  Ultra-weak FBG and its refractive index distribution in the drawing optical fiber. , 2015, Optics express.

[13]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[14]  Jun Yang,et al.  An adaptive clustering segmentation algorithm based on FCM , 2017, Turkish J. Electr. Eng. Comput. Sci..

[15]  Quan Wang,et al.  Modified DBSCAN algorithm on oculomotor fixation identification , 2016, ETRA.

[16]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[17]  M. Ganjali,et al.  Sensitive electrochemical immunosensor for citrus bacterial canker disease detection using fast Fourier transformation square-wave voltammetry method , 2018, Journal of Electroanalytical Chemistry.

[18]  Vasileios Karyotis,et al.  A hyperbolic space analytics framework for big network data and their applications , 2016, IEEE Network.

[19]  Reynold Xin,et al.  Apache Spark , 2016 .

[20]  Shehroz S. Khan,et al.  Cluster center initialization algorithm for K-means clustering , 2004, Pattern Recognit. Lett..

[21]  Bo Dong,et al.  A Large Serial Time-Division Multiplexed Fiber Bragg Grating Sensor Network , 2012, Journal of Lightwave Technology.

[22]  Jesús Arias-Fisteus,et al.  Benchmarking real-time vehicle data streaming models for a smart city , 2017, Inf. Syst..

[23]  Deming Liu,et al.  A large capacity sensing network with identical weak fiber Bragg gratings multiplexing , 2012 .

[24]  Chan-Gun Lee,et al.  Lazy Approaches for Interval Timing Correlation of Sensor Data Streams , 2010, Sensors.

[25]  H. Peter Hofstee,et al.  Auto-tuning Spark big data workloads on POWER8: Prediction-based dynamic SMT threading , 2016, 2016 International Conference on Parallel Architecture and Compilation Techniques (PACT).

[26]  Scott Shenker,et al.  Discretized streams: fault-tolerant streaming computation at scale , 2013, SOSP.

[27]  Liu Shuang,et al.  An Incremental Density Based Spatial Clustering of Application with Noise Algorithm Based on Partition Index , 2016 .

[28]  Ge Lindong,et al.  Adaptive DBSCAN-based algorithm for constellation reconstruction and modulation identification , 2004, 2004 Asia-Pacific Radio Science Conference, 2004. Proceedings..