Development of a Multi-point IoT Based Ship Draft Monitoring System for Barge Mounted Power Plants

Elisabeth Tansiana Mbitu, Marceau A. F. Haurissa, Fatima Zahra

Abstract


Continuous monitoring of ship draft is essential for maintaining vessel stability, operational safetakay, and fuel distribution efficiency, particularly in Barge Mounted Power Plants (BMPP), where load variations directly influence the vessel’s balance. At the PLTMG BMPP Nusantara-1, draft measurements are currently performed through manual visual inspection of draft marks, making the process susceptible to human error and unsuitable for continuous real time monitoring. This study proposes the development of a multipoint Internet of Things (IoT) based ship draft monitoring system employing six waterproof JSN-SR04T ultrasonic sensors integrated with an Arduino Mega, ESP32 communication module, RTC DS3231, and Arduino IoT Cloud platform. The proposed system continuously measures draft values at the port and starboard sides along the bow, midship, and stern, enabling comprehensive monitoring of vessel trim and heel conditions. Measurement data are transmitted in real time to a cloud dashboard and automatically stored in Google Sheets for historical data logging. Ten latency trials produced a mean end to end response time of 30.00 s. In addition, the system incorporates an automatic warning mechanism that detects overdraft, minimum draft, and vessel imbalance conditions based on predefined operational thresholds. Experimental evaluation using a laboratory scale prototype of the BMPP Nusantara-1 demonstrates that the proposed system successfully performs continuous draft monitoring, provides real time visualization with an observed 30 s response interval, records historical measurement data, and generates timely warning notifications under various operating scenarios, including overdraft, heel, and minimum draft conditions. The developed system offers a practical IoT based solution for improving operational safety and supporting digital monitoring of floating power plants.

Keywords


Internet of Things; Ship draft monitoring; Ultrasonic sensor; Barge Mounted Power Plant; Real time monitoring

Full Text:

Link Download

References


Alexiou, K., Pariotis, E. G., & Leligou, H. C. (2023). Sensor Data Quality in Ships: A Time Series Forecasting Approach to Compensate for Missing Data and Drift in Measurements of Speed through Water Sensors. Designs, 7(2). https://doi.org/10.3390/designs7020046

Artono, B. (2023). Smart Solar Tracker and Energy Control Based on Internet of Things (IoT). Telematika, 16(1). https://doi.org/10.35671/telematika.v16i1.2576

Azhar, A. R., Setiawan, D. A., Yasmin, N. A. A., Putri, T. A., & Nama, G. F. (2024). Sistem Monitoring Kapasitas Air Dan Pengisian Otomatis Berbasis Iot Menggunakan Modul ESP8266. Jurnal Informatika Dan Teknik Elektro Terapan, 12(1). https://doi.org/10.23960/jitet.v12i1.3966

Barron, A., Sanchez-Gallegos, D. D., Carrizales-Espinoza, D., Gonzalez-Compean, J. L., & Morales-Sandoval, M. (2022). On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments. Sensors, 22(18). https://doi.org/10.3390/s22187016

Briguglio, G., & Crupi, V. (2024). Review on Sensors for Sustainable and Safe Maritime Mobility. In Journal of Marine Science and Engineering (Vol. 12, Number 2). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/jmse12020353

Cai, T., Liu, P., Niu, D., Shi, J., & Li, L. (2021). The Embedded IoT Time Series Database for Hybrid Solid-State Storage System. Scientific Programming, 2021. https://doi.org/10.1155/2021/9948533

Campbell, R., Terziev, M., Tezdogan, T., & Incecik, A. (2022). Computational fluid dynamics predictions of draught and trim variations on ship resistance in confined waters. Applied Ocean Research, 126. https://doi.org/10.1016/j.apor.2022.103301

Cil, A. Y., Abdurahman, D., & Cil, I. (2022). Internet of Things enabled real time cold chain monitoring in a container port. Journal of Shipping and Trade, 7(1). https://doi.org/10.1186/s41072-022-00110-z

De Barrena Sarasola, T. F., García, A., & Ferrando, J. L. (2024). IIoT Protocols for Edge/Fog and Cloud Computing in Industrial AI: A High Frequency Perspective. International Journal of Cloud Applications and Computing, 14(1), 1–30. https://doi.org/10.4018/IJCAC.342128

Gerakoudi, K., Kokosalakis, G., & Stavroulakis, P. J. (2024). A machine learning approach towards reviewing the role of ‘Internet of Things’ in the shipping industry. Journal of Shipping and Trade, 9(1). https://doi.org/10.1186/s41072-024-00177-w

Gupta, P., Kim, Y. R., Steen, S., & Rasheed, A. (2023). Streamlined semi-automatic data processing framework for ship performance analysis. International Journal of Naval Architecture and Ocean Engineering, 15. https://doi.org/10.1016/j.ijnaoe.2023.100550

Himaya, A. N., & Sano, M. (2023). Course-Keeping Performance of a Container Ship with Various Draft and Trim Conditions under Wind Disturbance. Journal of Marine Science and Engineering, 11(5). https://doi.org/10.3390/jmse11051052

Hosamo, H., & Mazzetto, S. (2025). Performance Evaluation of Machine Learning Models for Predicting Energy Consumption and Occupant Dissatisfaction in Buildings. Buildings, 15(1). https://doi.org/10.3390/buildings15010039

Kim, Y., Gupta, P., & Steen, S. (2025). A comprehensive review of data processing for ship performance analysis. In Applied Ocean Research (Vol. 162). Elsevier Ltd. https://doi.org/10.1016/j.apor.2025.104737

Kusuma, B. (2023). Smart Farming System for Monitoring and Optimizing Paddy Field with Internet of Things Technology. Telematika, 16(1). https://doi.org/10.35671/telematika.v16i1.2183

Lanfranchi, G., Crupi, A., & Cesaroni, F. (2025). Internet of Things (IoT) and the Environmental Sustainability: A Literature Review and Recommendations for Future Research. In Corporate Social Responsibility and Environmental Management (Vol. 32, Number 6, pp. 7648–7670). John Wiley and Sons Ltd. https://doi.org/10.1002/csr.70098

Luo, H., Wang, X., Xu, Z., Liu, C., & Pan, J. S. (2022). A software-defined multi-modal wireless sensor network for ocean monitoring. International Journal of Distributed Sensor Networks, 18(1). https://doi.org/10.1177/15501477211068389

Majumder, A., Losito, M., Paramasivam, S., Kumar, A., & Gatto, G. (2024). Buoys for marine weather data monitoring and LoRaWAN communication. Ocean Engineering, 313. https://doi.org/10.1016/j.oceaneng.2024.119521

Maritime Organization, I. (2009). RESOLUTION MSC.269(85) (adopted on 4 December 2008) ADOPTION OF AMENDMENTS TO THE INTERNATIONAL CONVENTION FOR THE SAFETY OF LIFE AT SEA, 1974, AS AMENDED.

Musulin, M., Mihanović, L., Balić, K., & Musulin, H. N. (2024). The Impact of Container Ship Trim on Fuel Consumption and Navigation Safety. Journal of Marine Science and Engineering, 12(9). https://doi.org/10.3390/jmse12091658

Ølberg, J. T., Bohlinger, P., Breivik, Ø., Christensen, K. H., Furevik, B. R., Hole, L. R., Hope, G., Jensen, A., Knoblauch, F., Nguyen, N. T., & Rabault, J. (2024). Wave measurements using open source ship mounted ultrasonic altimeter and motion correction system during the one ocean circumnavigation. Ocean Engineering, 292. https://doi.org/10.1016/j.oceaneng.2023.116586

Prayetno, E., Maritim, U., Ali, R., Tanjungpinang, H., Riau, K., Nadapdap, T., Susanti, A. S., & Miranda, D. (2021). PLTD Engine Tank Oil Volume Monitoring System using HC-SR04 Ultrasonic Sensor Based on Internet of Things (IoT). In International Journal of Electrical, Energy and Power System Engineering (Vol. 4, Number 1). http://www.ijeepse.ejournal.unri.ac.id

Setiawan, W., Dlukha Nurcholiq, S., Mursid Nugraha Arifuddin, A., Ika Wulandari, A., & Muhammad Uswah Pawara, dan. (2024). Freeboard And Trim Measurement: A Case Study Of Landing Craft Tank Conversion To Ship Power Plan-SPASI-TIMES NEW ROMAN 11 ITALIC--spasi-Times New Roman 11 Italic-*Suardi-spasi-Times New Roman 11 Italic--spasi-Times New Roman 11 Italic-si-Times New Roman 11 Italic (Vol. 5, Number 1). https://journal.unhas.ac.id/index.php/zonalautZONALAUT

Tusa, F., Clayman, S., Buzachis, A., & Fazio, M. (2024). Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics. Future Generation Computer Systems, 155, 204–218. https://doi.org/10.1016/j.future.2024.02.006

Zhang, B., Li, J., Tang, H., & Liu, X. (2024). Smart Ship Draft Reading by Dual-Flow Deep Learning Architecture and Multispectral Information. Sensors, 24(17). https://doi.org/10.3390/s24175580

Zhang, B., Yin, Y., Ma, K., & Wang, H. (2025). Multi-scale feature fusion keypoint detection network for ship draft line localization. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-10594-x

Zhong, C., & Nie, X. (2024). A novel single-channel edge computing LoRa gateway for real-time confirmed messaging. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-59058-8

Zhu, K., Liu, J., & Zhang, Y. (2025). A Quasi-Bonjean Method for Computing Performance Elements of Ships Under Arbitrary Attitudes. Systems, 13(7). https://doi.org/10.3390/systems13070571




DOI: http://dx.doi.org/10.35671/telematika.v19i2.3414

Refbacks

  • There are currently no refbacks.


 



Indexed by:

   

Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License .