Visualization-based monitoring in early warning systems with wireless sensor networks

With the impact of global climate change, natural disasters such as prolonged drought, earthquakes, and tsunamis, have constantly increased over recent decades, putting those living in these areas in great danger. A natural disaster warning system has been established as an indispensable need to minimize possible high risks that cause human casualties. Several current natural disaster warning systems focus on building wireless sensor networks for forecasting and monitoring disasters as well as natural phenomena. This paper aims to develop a comprehensive model that integrates data visualization operations to identify and simultaneously predict threat proceedings in natural disasters. This technique can handle big data based on sensing data from wireless sensor networks and shows overview graphs about disasters' variability, floods, and earthquakes, in the areas. Based on the results collected from data visualization techniques, the system can issue alerts about the interest of the region in real time. In addition, we propose some levels for the warning system in which the networks only focus on the area with essential data that must be warned. This can save energy consumption for other areas of safety. This work shows promising points of effectiveness.