<![CDATA[Oil pipeline leaks pose a serious challenge due to their potential to cause significant economic losses and severe environmental damage. These incidents can disrupt industrial operations and endanger nearby ecosystems and communities. Early detection and real-time monitoring are therefore essential for minimizing adverse impacts and enabling rapid response. This research develops an Internet of Things (IoT)-based oil pipeline leak monitoring system using integrated multi-sensor data collected from field-simulated scenarios, providing a realistic evaluation of system performance under near-operational conditions. The system incorporates an ultrasonic sensor (HC-SR04) to measure fluid levels, a temperature sensor (DS18B20) to detect thermal anomalies, and a pressure sensor to identify internal pressure fluctuations. Sensor data are wirelessly transmitted via a NodeMCU ESP32 microcontroller to a web-based dashboard for remote monitoring, while local readings are simultaneously displayed on an LCD screen for on-site observation. The system was evaluated through controlled experiments simulating variations in pressure, temperature, and induced leak conditions. Results showed that the system achieved over 95% accuracy in leak detection, with a response time of less than 60 seconds upon leak initiation. The flow rate deviations under leak conditions exceeded the ±3% detection threshold, triggering real-time alerts. In non-leak scenarios, flow rates remained steady between 1.5–2.1 L/min, with tank level variations within 1 cm, confirming strong mass balance and stability. Overall, the developed IoT-based monitoring platform demonstrated high reliability and effectiveness in real-time leak detection, enabling faster response and significantly reducing potential environmental and operational impacts.]]>
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