Frens 2.0: a mobile application for flood and road eye navigation system using react native framework, internet of things (iot) and deep learning/
Alliyah Jeanne A. Balatico, Randee Samuel B. Comia, Ivan Dave A. Gloriani, Windyll P. Meniembra, Justine Mae F. Neri, and Ike Philip B. Villareal.--
- Manila: Technological University of the Philippines, 2025.
- 224pages: 29cm.
Bachelor's thesis
College Of Engineering.--
Includes bibliographic references and index.
Urban flooding poses significant challenges to traffic safety and mobility, especially in cities like Manila. This study presents FRENS 2.0, a flood and road navigation system developed using the React Native framework, integrated with an Internet of Things (IoT) sensor network and machine learning. The system collects real-time data from water level sensors, rain gauges, barometric sensors, and computer vision cameras using the YOLOv8 model to monitor flood and traffic conditions. This data, combined with historical records, enables dynamic route rerouting and accurate Estimated Time of Arrival (ETA) predictions through a mobile application enhanced with Google Maps and an AI chatbot powered by LangChain. FRENS 2.0 significantly improves performance with refresh rates between 192 and 219 milliseconds under normal conditions and 510 to 582 milliseconds during floods, outperforming previous web-based versions. Its intelligent rerouting algorithm effectively avoids flooded and hazardous areas, ensuring safer and more efficient travel compared to standard navigation apps. A key hardware upgrade includes replacing lead-acid batteries with Lithium Iron Phosphate (LiFePO4) batteries, extending the system’s operation to over 34.5 hours without sunlight, enhancing sustainability and durability. Comprehensive testing showed high user satisfaction with an overall rating of 4.45 out of 5, highlighting strong functionality and portability. The results demonstrate FRENS 2.0’s potential to improve urban navigation safety and efficiency during adverse weather. Future recommendations include enhancing hardware security, improving data clarity, refining the algorithm, developing an iOS app, and strengthening system security for broader application and reliability.