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In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economic losses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefited from the established development of smart city construction. And the IoT is visibly sensitive to the management and monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to data storage and data analysis. This article develops a new and much more general framework for disaster emergency management under the IoT environment. The framework is a bottom-up integration of highly scalable Raw Data Storages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology for emergency disasters. Experimental results show that hybrid index and query technology have better performance under the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrieval for the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-application system in this framework were performed based on the GPS trajectory data of 35,000 Beijing taxis and the volumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability and higher utility.
Ding et al. (Wed,) studied this question.