Passive IoT Optical Fiber Sensor Network for Water Level Monitoring with Signal Processing of Feature Extraction

Abstract

This paper presents a real-time remote water level monitoring system based on dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network for the application of the Internet of Things (IoT). This network employs a broadband light source based on amplified spontaneous emission (ASE) as a seed light. This ASE light is spectrum-sliced by an athermal type arrayed waveguide grating (200 GHz × 16 channel), then distributed towards multiple sensing units (SU). Here, 16 SUs are installed vertically at the specified height in the water pool according to the design specification (i.e., spatial resolution). Then, each SU reflects an optical spectrum having a different reflection coefficient depending on the surrounding medium (e.g., air or water). By measuring these reflected optical spectra with an optical spectrum analyzer, the water level can be easily recognized in real time. However, as the sensing distance increases, system performance is severely degraded due to the Rayleigh Back-Scattering of the ASE light. As a result, the remote sensing capability is limited at a short distance (i.e., <10 km). To overcome this limitation, we propose a simple signal processing technique based on feature extraction of received optical spectra, which includes embedding a peak detection algorithm with a signal validation check. For the specific, the proposed signal processing performs the peak power detection, signal quality monitoring, and determination/display of the actual water level through three function modules, i.e., data save/load module, signal processing module, and Human–Machine Interface display module. In particular, the signal quality of the remote sensing network can be easily monitored through several factors, such as the number of spectral peaks, the wavelength spacing between neighboring peaks and the pattern of detected peak power. Moreover, by using this validation check algorithm, it is also possible to diagnose various error types (such as peak detection error, loss of data and so on) according to the pattern of measured optical spectra. As a result, the IoT sensor network can recognize 17 different level statuses for the water level measurement from a distance of about 25 km away without active devices such as optical amplifiers (i.e., passive remote sensing).