Energy efficient IoT-based cloud framework for early flood prediction

Flood is a recurrent and crucial natural phenomenon affecting almost the entire planet. It is a critical problem that causes crop destruction, destruction to the population, loss of infrastructure, and demolition of several public utilities. An effective way to deal with this is to alert the community from incoming inundation and provide ample time to evacuate and protect property. In this article, we suggest an IoT-based energy-efficient flood prediction and forecasting system. IoT sensor nodes are constrained in battery and memory, so the fog layer uses an energy-saving approach based on data heterogeneity to preserve the system's power consumption. Cloud storage is used for efficient storage. The environmental conditions such as temperature, humidity, rainfall, and water body parameters, i.e., water flow and water level, are being investigated for India's Kerala region to calibrate the flood phases. PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction. ANN (Artificial Neural Network) algorithm is used to predict the flood, and the simulation technique of Holt Winter is used to forecast the future flood. Data are obtained from the Indian government meteorological database, and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.