Monitoring of Crude Oil Pipeline Using Long-Range Network
Abstract
This study presents a crude oil pipeline monitoring system using Long Range
(LoRa) networks in Internet of Things (IoT)-based technology. Pipeline leakages,
caused by aging pipes, rusting, and vandalism, remain a major challenge in crude
oil distribution. While existing IoT-based solutions use short-range technologies
like Bluetooth, WiFi, and ZigBee for real-time monitoring, they struggle with longdistance
communication in remote areas with poor network coverage. This study
applies LoRa technology to transmit leakage data over long distances to a remote
monitoring point. An experimental testbed was designed to simulate pipeline
scenarios, using pressure readings to detect leaks. Results showed that in 9 seconds,
leak pressure at point 1 (P1) near the supply rose from 0 Psi to 11.74 Psi, while
pressures at P2 (0.325m from P1) and P3 (0.55m from P2) increased to 10.48 Psi
and 9.79 Psi, respectively. This indicates that leak pressures build up more near
the supply source, and increasing the leak opening results in higher pressure loss.
The findings highlight the effectiveness of LoRa-based IoT systems for real-time,
long-range pipeline monitoring and early leak detection.