A Framework for disaster management using fuzzy bat clustering in fog computing

Disaster monitoring and prediction is one of the most important stages in disaster management. Critical crowdsourced Internet of Things data collected from various geographic resources (such as sensors, mobile devices, vehicles, humans, etc.) are evaluated and analyzed to effectively predict natural disasters. Cloud computing is a widely used technology for analyzing crowdsourced data in specific geographic areas. However, the time it takes to analyze these data can be long, huge end-end delay, and Quality of Service degradation. It also increases the loss of a large number of people during the disaster. Hence, fog computing is used to analyze these critical crowd sourced data, that is, for latency sensitive applications. This paper uses an efficient FBC algorithm in the fog computing platform, and proposes a fog-based disaster monitoring framework. The terminal device at the end user layer does not perform any processing or FBC clustering on the data. On the contrary, the fog node in the fog layer and the cloud server in the cloud computing layer perform FBC clustering, which helps to predict disasters in time. The proposed scheme is evaluated in terms of latency, response time and bandwidth, and the proposed scheme performs better than the centralized and distributed schemes.