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An Internet of Things based scalable framework for disaster data management

Journal of Safety Science and Resilience 2022年第2期3卷 136-152页

作者： Zhiming Ding Shan Jiang Xinrun Xu Yanbo Han Institute of Software Chinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China North China University of Technology Beijing 100144China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream data BeijingChina

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economiclosses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefitedfrom the established development of smart city construction. And the IoT is visibly sensitive to the managementand monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to datastorage and data analysis. This article develops a new and much more general framework for disaster emergencymanagement under the IoT environment. The framework is a bottom-up integration of highly scalable Raw dataStorages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology foremergency disasters. Experimental results show that hybrid index and query technology have better performanceunder the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrievalfor the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-applicationsystem in this framework were performed based on the GPS trajectory data of 35,000 Beijing taxis and thevolumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability andhigher utility.

关键词： disaster data management IoT disaster detection Big data Artificial intelligence

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