Authors : Ifeoma B. Asianuba; Okeke R. Obinna

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/awrxyjya

Scribd : https://tinyurl.com/2rk4r9yt

DOI : https://doi.org/10.38124/ijisrt/25mar708

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : Real-time Diesel Level Monitoring System presents a solution to observe diesel levels at off grid remote base transceiver stations (BTS). Leveraging Internet of Things (IoT) technology, the system employs an Arduino Uno, sensor for data collection, interfaced with a capacitance sensor to measure fuel levels accurately. Additionally, an ESP8266 Wi-Fi module is used to facilitate remote connections, enabling seamless communication between the sensor and a central server. The operational flow is triggered by initializing the hardware, and then collection of data from the relevant sensors. The ESP8266 Wi-Fi module establishes a remote connection, transmitting the collected data to the server. At the server end, the data is received, processed and the information is stored in MySQL database, facilitating efficient retrieval and analysis. The frontend visualization provides a user-friendly interface, displaying real-time diesel level data on a dashboard. Users can interact with the system by predicting fuel levels through the "Predict" feature. This prediction was actualized using the linear regression algorithm to estimate the time left to exhaust the diesel at the BTS site. The system's architecture harmonizes hardware components, server-side processing, and frontend visualization to create an integrated and efficient fuel monitoring solution. The essence of this work can be traced to its ability to reduce down time that may arise due to fuel shortage, improve energy management and enhance operational efficiency at remote BTS sites providing valuable insights for monitoring and managing diesel resources.

Keywords : Sensors, Real Time Prediction, Diesel Level Monitoring, Wi-Fi Module, Base Transceiver Station, Linear Regression.

References :

1. Ahmed Sofia , Tsegamlak Terefe, Dereje Hailemariam, Machine learning for base transceiver stations power failure prediction: A multivariate approach,e-Prime - Advances in Electrical Engineering, Electronics and Energy, Volume 10, 2024, 100814, ISSN 2772-6711, https://doi.org/10.1016/j.prime.2024.100814.
2. Antonio Spagnuolo, Antonio Petraglia, Carmela Vetromile, Roberto Formosi, Carmine Lubritto, (2015). Monitoring and optimization of energy consumption of base transceiver stations, Energy, Volume 81, 2015, Pages 286-293, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2014.12.040.
3. Arnaldo G. Leal-Junior, Carlos Marques, Anselmo Frizera, Maria José Pontes, (2018). Multi-interface level in oil tanks and applications of optical fiber sensors.
4. Capacitance measurement with the Arduino Uno, (2014). PIC Tutorials. https://wordpress.codewrite.co.uk/pic/2014/01/21/cap-meter-with-arduino-uno/index.html
5. Chetan Patil and Channabasappa Baligar. Base Transceiver Station (BTS) Safety and Fault Management. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075 (Online), Volume-3 Issue-7, December 2013. Pp 48-52.
6. Fuel Management Systems, (2023). Banlaw group. https://www.banlaw.com/fuel-management-systems/
7. Geoffrey M. Hohn, (2011). Computerized Fuel Management System for a Foss Tugboat. A thesis submitted in University of Washington graduate school.
8. Łukasz Kufel, (December 2016). Tools for Distributed Systems Monitoring. Retrieved from https://www.researchgate.net/publication/311863266_Tools_for_Distributed_Systems_Monitoring
9. Mingwei Lin, Zhiqiang Yao, Fei Gao, Yang Li (2016). A Virtual Machine Instance Anomaly Detection System for IaaS Cloud Computing. Retrieved from https://www.researchgate.net/publication/302922016_A_Virtual_Machine_Instance_Anomaly_Detection_System_for_IaaS_Cloud_Computing
10. Muhammad Tahir, Syed Muhammad Nabeel Mustafa, Rabia Enam, Najma Ismat, (August 2022).  Real Time Monitoring and Control of Electrical Diesel Generator through Internet of Things. Retrieved from https://www.researchgate.net/publication/362964974_Real_Time_Monitoring_and_Control_of_Electrical_Diesel_Generator_through_Internet_of_Things
11. Murugesan C. and Marimuthu C. N. (2018). Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers.
12. Pankaj Kumar, Nitai Pal, Himanshu Sharma, (2022). Optimization and techno-economic analysis of a solar photo-voltaic/biomass/diesel/battery hybrid off-grid power generation system for rural remote electrification in eastern India.
13. Serafeim Zanikolas, Rizos Sakellariou, (2005). A taxonomy of grid monitoring systems. Retrieved from https://www.researchgate.net/publication/222511463_A_taxonomy_of_grid_monitoring_systems
14. Tavengwa Masamha, Clayton Gwava, Blessing Mapinge and Jacqueline Kiwa C.                  Predictive maintenance of base transceiver station power system using XGBoost algorithm: A case study of Econet Wireless, Zimbabwe The 9th African Conference on Information Systems and Technology 2023 pp1-7.
15. Terfera Y. Y., T. Kibatu, B. S. Shawel and D. H. Woldegebreal, "Recurrent Neural Network-based Base Transceiver Station Power Supply System Failure Prediction," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-7, doi: 10.1109/IJCNN48605.2020.9206978.
16. Xinghua Qi, Bahadar Nawab Khattak, Sara Saeedi, (2023). Optimal energy modeling and planning in the power system via a hybrid firefly and cuckoo algorithm in the presence of renewable energy sources and electric vehicles.