Abstract
Critical crowdsourced, Internet of things (IoT) data collected from various geographical sources such as sensors, mobile devices, vehicles, and humans are assessed and analyzed timely for the effective disaster management. Cloud computing is a widely used technology to analyze the crowdsourced data of a particular geographic region. The time taken to analyze these data is large, large end–end delay, and quality of service (QoS) degradation. Hence, fog computing is used to analyze these critical crowdsourced data, i.e., for latensive-sensitive applications. This paper highlights the disaster monitoring system that analyzes the crowdsourced data using fog computing, which avoids the latency and delay jitter for time-critical applications. The proposed framework demonstrates that it achieves very low execution time and less delay than conventional cloud computing model.
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Raja Sree, T. (2021). A Framework for Disaster Monitoring Using Fog Computing. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1335. Springer, Singapore. https://doi.org/10.1007/978-981-33-6984-9_39
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DOI: https://doi.org/10.1007/978-981-33-6984-9_39
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