The development of disaster preparedness and forecasting system integrating real-time sensor data and time stringent computing is becoming increasingly important for mitigating the negative effects of disasters. In this paper, a service oriented IoT architecture for an early warning system (EWS) through implementation of machine learning (ML) algorithm in a cloud server is presented. Unlike other EWS approaches, our implementation includes layer level IoT architecture with a triple modular redundancy fault tolerant scheme for sensor network to guarantee availability of reliable and fault free sensor data to the cloud for accurate predictions. A ML algorithm is implemented for three sensor parameters viz., MQ4, MQ7 and force sensing resistor on the AWS cloud. The absolute error percentage between the actual and the predicted values are found to be 6.18%, 3.03% and 3.65%, respectively, for a set of values at 20 different time intervals.
Dr. Anju Pillai S., Chandraprasad, G. S., Khwaja, A. S., and Anpalagan, A., “A Service Oriented IoT Architecture for Disaster Preparedness and Forecasting System”, Internet of Things, p. 100076, 2019.