Author:
Ramesh Parameswaran,N Vidhya,B Panjavarnam,M Shabana Parveen,A M B Deepak Athipan,P T V Bhuvaneswari
Abstract
INTRODUCTION: Around the world, individuals experience flooding more frequently than any other natural calamity.
OBJECTIVES: The motivation behind this research is to provide an Internet of Things (IoT)-based early warning assistive system to enable monitoring of water logging levels in flood-affected areas. Further, the SSD-MobiNET V2 model is used in the developed system to detect and classify the objects that prevail in the flood zone.
METHODS: The developed research is validated in a real-time scenario. To enable this, a customized embedded module is designed and developed using the Raspberry Pi 4 model B processor. The module uses (i) a pi-camera to capture the objects and (ii) an ultrasonic sensor to measure the water level in the flood area.
RESULTS: The measured data and detected objects are periodically ported to the cloud and stored in the cloud database to enable remote monitoring and further processing.
CONCLUSION: Also, whenever the level of waterlogged exceeds the threshold, an alert is sent to the concerned authorities in the form of an SMS, a phone call, or an email.
Publisher
European Alliance for Innovation n.o.
Reference20 articles.
1. Rafael, M, Javier, G, Antonio, S.: A Real-time measurement system for long life flood monitoring and warning applications. Sensors (Basel). 2012. Vol. 12, pp. 4213-4236.
2. Jiuxiang, G, Zhenhua, W, Jason, K, Lianyang, M.: Recent advances in Convolutional Neural Networks. Pattern Recogniton. 2018. Vol. 77, pp. 354-377.
3. MathuraBai, B, Vishnu, P, Maddali, C, Devineni, S.: Object Detetcion using SSD-MobileNet. International Research Journal of Engineering and Technology. 2022. Vol. 9, pp. 2668-2771.
4. Xiaolong, X, Lei, Z, Stelios, S, Eleana, A.: CLOTHO: A Large-Scale Internet of Things based Crowd Evacuation Planning System for Disaster Management. IEEE Internet of Things Journal. 2018. Vol. 5, pp. 3559-3568.
5. Lung, E, KarAnn, T, Yun, W, Wang, J.: DEWS: A Live Visual Surveillance System for Early Drowning Detection at Pool. IEEE Transactions on Circuits and Systems for Video Technology Journal.2008. Vol. 18, pp. 25-38.