An IoT-Based Intelligent Monitoring System for Cargo Loss Detection in FPSO-Tanker Oil Transfer

Authors

  • Habibi Palippui Department of Ocean Engineering, Hasanuddin University, Indonesia https://orcid.org/0000-0001-7396-1847
  • Daniel Mohammad Rosyid Department of Ocean Engineering, Institut Teknologi Sepuluh Nopember, Indonesia https://orcid.org/0009-0007-6956-2457
  • Silvianita Silvianita Department of Ocean Engineering, Institut Teknologi Sepuluh Nopember, Indonesia https://orcid.org/0000-0001-5487-5521
  • Juswan Sade Department of Ocean Engineering, Hasanuddin University, Indonesia

DOI:

https://doi.org/10.47852/bonviewJCCE62028129

Keywords:

IoT, cargo loss detection, intelligent monitoring system, FPSO-tanker offloading, maritime safety

Abstract

This study presents the development of an Internet of Things-based monitoring system for detecting cargo loss during oil transfer operations between Floating Production Storage and Offloading units and receiving tankers. The proposed system integrates an ESP32S microcontroller, ultrasonic sensors, GPS modules, and a web-based dashboard to enable real-time monitoring and detect anomalies. The system is designed to reduce measurement errors and response delays that may lead to cargo discrepancies during transfer operations. Performance evaluation was conducted using prototype-based simulations to assess the system accuracy, responsiveness, and communication reliability. The results demonstrate high measurement accuracy (≥99.8%), fast response time (~180 ms), low latency (~210 ms), and reliable data transmission with a success rate of 98.7%. The system is also capable of detecting abnormal flow conditions or potential leakage within less than 1 min. Compared with conventional SCADA-based systems, the proposed approach offers improved real-time responsiveness, modularity, and adaptability to dynamic offshore environments. The developed system provides a scalable and flexible platform for maritime monitoring applications and establishes a foundation for the future integration of machine learning techniques to enable predictive and adaptive anomaly detection.



Received: 8 November 2025 | Revised: 9 March 2026 | Accepted: 15 April 2026



Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.



Data Availability Statement

Data are available from the corresponding author upon reasonable request.



Author Contribution Statement

Habibi Palippui: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Funding acquisition. Daniel Mohammad Rosyid: Conceptualization, Resources, Writing – review & editing, Supervision, Project administration. Silvianita Silvianita: Methodology, Validation, Formal analysis, Writing – review & editing. Juswan Sade: Resources, Writing – review & editing.

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Published

2026-05-14

Issue

Section

Research Articles

How to Cite

Palippui, H., Rosyid, D. M., Silvianita, S., & Sade, J. (2026). An IoT-Based Intelligent Monitoring System for Cargo Loss Detection in FPSO-Tanker Oil Transfer. Journal of Computational and Cognitive Engineering. https://doi.org/10.47852/bonviewJCCE62028129

Funding data