Edge Computing Architecture Sensor-based Flood Monitoring System: Design and Implementation

Authors

DOI:

10.33395/sinkron.v8i3.13874

Keywords:

Edge Computing, Flood Monitoring System, Sensor Integration, Machine Learning, Real-time Data Processing

Abstract

The purpose of this research is to develop and execute a system for monitoring floods using sensors and edge computing architecture. The goal is to make flood detection and prediction more accurate and faster. The growing frequency and severity of flood disasters in different parts of the world has prompted the necessity for a better system to track these events. The primary goal of this study is to design a system that can reduce network load and latency by processing sensor data locally at edge devices before sending it to the cloud. To detect and anticipate flood events, the research method incorporates several environmental sensors that measure things like soil moisture, water level, and rainfall. These readings are subsequently processed by edge nodes using machine learning algorithms. Compared to more conventional methods that depend only on cloud computing, the results demonstrate that the system can deliver quicker and more accurate predictions. Results showed a detection and prediction accuracy of 98.95% for floods. Edge computing also succeeded in drastically cutting down on bandwidth consumption and communication latency. This research concludes that edge computing architecture based on sensors can effectively monitor floods and has excellent potential for use in many different areas prone to flooding. Improving the prediction algorithm and investigating its potential integration with a more thorough early warning system should be the focus of future research.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Ahmad, S., Umirzakova, S., Jamil, F., & Whangbo, T. K. (2022). Internet-of-things-enabled serious games: A comprehensive survey. Future Generation Computer Systems, 136, 67–83. https://doi.org/10.1016/j.future.2022.05.026

Bakhtiari, V., Piadeh, F., Behzadian, K., & Kapelan, Z. (2023). A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management. Sustainable Cities and Society, 99(April). https://doi.org/10.1016/j.scs.2023.104958

Bakhtiari, V., Piadeh, F., Chen, A. S., & Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management : A critical review. Expert Systems With Applications, 236(September 2023). https://doi.org/10.1016/j.eswa.2023.121426

Dong, Z., Wang, G., Obiri, S., Amankwah, Y., Wei, X., & Hu, Y. (2021). Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks. International Journal of Applied Earth Observations and Geoinformation, 102. https://doi.org/https://doi.org/10.1016/j.jag.2021.102400

Ghosh, S., Kumar, D., & Kumari, R. (2022). Cloud-based large-scale data retrieval , mapping , and analysis for land monitoring applications with Google Earth Engine (GEE). Environmental Challenges, 9(May). https://doi.org/10.1016/j.envc.2022.100605

Hindarto, D. (2023a). Battle Models : Inception ResNet vs . Extreme Inception for Marine Fish Object Detection. 8(4), 2819–2826.

Hindarto, D. (2023b). Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. Sinkron, 8(4), 2810–2818. https://doi.org/10.33395/sinkron.v8i4.13124

Izquierdo, T., Rivera, A., Gallardo, D., Aparicio, O., Buylaert, J., Ruiz, F., & Abad, M. (2024). Historical catastrophic floods at the southern edge of the Atacama Desert : A multi-archive reconstruction of the Copiap´o river river extreme events. Global and Planetary Change, 236(March). https://doi.org/10.1016/j.gloplacha.2024.104411

Kahl, D. T., Schubert, J. E., Jong-levinger, A., & Sanders, B. F. (2022). Grid edge classification method to enhance levee resolution in dual-grid flood inundation models. Advances in Water Resources, 168(August). https://doi.org/10.1016/j.advwatres.2022.104287

Lawler, S., Zhang, C., Raheem, A., Lindemer, C., Rosa, D., Lehman, W., Ferreira, C., & Di, L. (2024). Leveraging OGC API for cloud-based flood modeling campaigns. Environmental Modelling and Software, 171(July 2023).

Sahoo, A., Satyapragnya, S., Samantaray, S., & Prakash, D. (2024). Daily fl ow discharge prediction using integrated methodology based on LSTM models : Case study in Brahmani-Baitarani basin. HydroResearch, 7, 272–284. https://doi.org/10.1016/j.hydres.2024.04.006

Sonkoly, B., Haja, D., Németh, B., Szalay, M., Czentye, J., Szabó, R., Ullah, R., Kim, B. S., & Toka, L. (2020). Scalable edge cloud platforms for IoT services. Journal of Network and Computer Applications, 170(August). https://doi.org/10.1016/j.jnca.2020.102785

Sze, E., Hindarto, D., & Wirayasa, I. K. A. (2022). Performance Comparison of Ultrasonic Sensor Accuracy in Measuring Distance. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 7(4), 2556–2562. https://doi.org/10.33395/sinkron.v7i4.11883

Xue, H., Chen, D., Zhang, N., Dai, H. N., & Yu, K. (2023). Integration of blockchain and edge computing in internet of things: A survey. Future Generation Computer Systems, 144, 307–326. https://doi.org/10.1016/j.future.2022.10.029

Zuo, J., Jiang, W., Li, Q., & Du, Y. (2024). Remote sensing dynamic monitoring of the fl ood season area of Poyang Lake over the past two decades. Natural Hazards Research Journal, 4(December 2023), 8–19. https://doi.org/10.1016/j.nhres.2023.12.017

Downloads


Crossmark Updates

How to Cite

Hindarto, D. (2024). Edge Computing Architecture Sensor-based Flood Monitoring System: Design and Implementation. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1758-1769. https://doi.org/10.33395/sinkron.v8i3.13874

Most read articles by the same author(s)

1 2 3 4 > >>