Detection of superfluous in channels using data fusion with wireless sensors and fuzzy interface algorithm

Abstract

Without integrating various sensor data sets, sensing information in the presence of leakage for large-scale pipeline systems is very challenging. A data fusion methodology, wherein more sensor data is merged to give relevant information, is necessary to transform the challenging process into a straightforward step-by-step operation. Ultrasonic sensors are used in stage 1 to identify any ambiguities in pipeline systems, and various sites are used to gauge the rate of leak detection. As a result, a novel model for estimating various types of gas leakage in pipeline systems is examined, put to the test, and contrasted. Five distinct scenarios are seen during the leakage testing procedure using data fusion, where the optimization is done using the fuzzy interface technique. This integration procedure detects leakage rates with high accuracy, and in every test instance, the best outcomes are obtained. Additionally, the predicted model can be used in real-time with a low failure rate of numerous sensors, with MATLAB being used to simulate the results.

Shitharth Selvarajan

Lecturer in Cyber Security

My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.