Measurement of optical fiber sensors for intrusion detection and warning systems fortified with intelligent false alarm suppression

This research explores innovations in the measurement of optical fiber sensors for intrusion detection, focusing on mitigating false alarms through an intelligent framework. The sensing technique involves tracking light scattered by nanoparticles, utilizing backscattering illustrated by Rayleigh’s backscattering. The study integrates parametric intrusion detection and warning system (PIDWS) with intelligent false alarm suppression and minimization techniques, using FFT for efficient detection. A hybrid approach involving neural networks is proposed for reducing false alarms in dynamic network settings. The research emphasizes the intersection of the Internet of Things (IoT) and various intrusion detection systems for long-distance data transfer. The design of the PIDWS is detailed, highlighting efforts to achieve high sensitivity with FFT utilization. Results are showcased through a waterfall, illustrating real-time situations. The fiber Health Report, generated by fiber OTDR, provides insights into optical fiber conditions. The activity detector algorithm is presented as a flexible and robust detection method. In summary, the research contributes valuable insights into advancing optical fiber-based intrusion detection systems and minimizing false alarms. The research is applicable in avoiding intruders in oil pipelines.