Pipeline Monitoring Systems (PMS) benefits the most of recent developments in wireless remote monitoring since each pipeline would span for long distances which make conventional methods unsuitable. Precise monitoring and detection of damaging events requires moving large amounts of data between sensor nodes and base station for processing which require high bandwidth communication protocol. To overcome this problem, In-Situ processing can be practiced by processing the collected data locally at each node instead of the base station. However, this introduce a challenge to the limited resources available on the nodes. In this paper, a Wireless Sensor Network (WSN) was implemented for In-Situ Pipeline Monitoring System with proposed algorithms for event location estimation. The proposed algorithms include feature extraction (using ANOVA), dimensionality reduction using statistical procedure that is (Principle Component Analysis PCA) and data classification using supervised learning K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The proposed system was tested on pipelines in Al-Mussaib Gas Turbine Power Plant. During test, knocking events are applied at several distances relative to the nodes locations. Data collected at each node is filtered and processed locally in real time in each two adjacent nodes. The results of the estimation is then sent to the supervisor at base-station for display. The results show the proposed system ability to estimate the location of knocking event.

The purpose of a surface plasmon resonance (SPR)-based photonic crystal fiber (PCF) biosensor is to achieve a maximal sensitivity for the detection of unknown analytes. Outside of the PCF, the chemically stable and inert plasmonic material gold (Au) was utilized. A solid core photonic crystal fibre (SC-PCF) with an endlessly single-mode was designed in this work, with both the centred core and holes in the cladding structured by circles. The SC-PCF was designed with a single solid center core surrounded by a six-ring hexagonal cladding. SC-PCF was measured utilizing the finite element method (FEM) and the perfectly matched layer (PML) boundary condition. The FEM was used to investigate the performance using COMSOL Multiphysics software. The proposed biosensor shows  amplitude sensitivity of (167 to 886.38) RIU−1  within the sensing range of (1.3435 to 1.3471). Due to structural simplicity and excellent sensing properties, the proposed PCF can be regarded as a good biosensor.