Garg, S, Aryal, J, Wang, H, Shah, T, Kecskemeti, G and Ranjan, R (2017) Cloud computing based bushfire prediction for cyber-physical emergency applications. Future Generation Computer Systems. ISSN 0167-739X

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Abstract

In the past few years, several studies proposed to reduce the impact of bushfires by mapping their occurrences and spread. Most of these prediction/mapping tools and models were designed to run either on a single local machine or a High performance cluster, neither of which can scale with users' needs. The process of installing these tools and models their configuration can itself be a tedious and time consuming process. Thus making them, not suitable for time constraint cyber-physical emergency systems. In this research, to improve the efficiency of the fire prediction process and make this service available to several users in a scalable and cost-effective manner, we propose a scalable Cloud based bushfire prediction framework, which allows forecasting of the probability of fire occurrences in different regions of interest. The framework automates the process of selecting particular bushfire models for specific regions and scheduling users' requests within their specified deadlines. The evaluation results show that our Cloud based bushfire prediction system can scale resources and meet user requirements. © 2017 Elsevier B.V.

Item Type:	Article
Uncontrolled Keywords:	0805 Distributed Computing, 0806 Information Systems
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science & Mathematics
Publisher:	Elsevier
Date Deposited:	16 May 2017 09:53
Last Modified:	04 Sep 2021 11:35
DOI or ID number:	10.1016/j.future.2017.02.009
URI:	https://researchonline.ljmu.ac.uk/id/eprint/6427

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