ESP32–LoRa–Edge AI Architecture for Predictive Flood Early Warning in Remote Smart Villages
Abstract
Floods are among the most disastrous of natural disasters that cut across rural and remote communities where the traditional infrastructure to monitor the flood is unavailable. In this paper, an ESP32-LoRa-Edge AI architecture will be proposed as a low-cost and energy-efficient algorithm to predictive early warning of floods in remote smart villages. The proposed system is integrated with long-range (LoRa) communication, distributed water-level and rainfall sensor, and edge-intelligence on a device to predict floods in real-time. Edge based machine learning can be used to identify anomalies at the earliest stage as well as predict floods with a low latency and reduced dependence on the network. It has been shown by the experiment that the system can be used to provide disaster management in rural areas at a minimum of 50-100 km by improving the accuracy of prediction, the low end-to-end latency, and ensuring the delivery of data with a high level of reliability.