Design and Implementation of Disaster Information Alert System Using Python in Ubiquitous Environment
With the rising occurrence of disasters at home and abroad, the disasters threatening the public’s life and health, such as natural disasters, have become larger and more complicated. It is very essential to establish the disaster management cooperation s
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Abstract With the rising occurrence of disasters at home and abroad, the disasters threatening the public’s life and health, such as natural disasters, have become larger and more complicated. It is very essential to establish the disaster management cooperation system joined by citizens and civic groups. In conventional disaster management system, Disaster Information (DI) is provided by 3G mobile and DMB service. However, it is urgent to build a disaster management system which can respond to disasters effectively depending on their patterns. This study proposed a DI alert system in ubiquitous environments, which provides DI in ubiquitous technology and the network technology supporting Beacon and smart mobile. To do that, this study used the Python-based to design DI web service and implemented the system that collects DI generated in disaster area and Beacon information around the area and provides a DI alert to the smart mobile devices of DI service users. Keywords Ubiquitous · Disaster · Beacon · Flask · SNS · Crowd sensing
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Introduction
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Globally, natural disasters, such as volcanic eruptions, earthquakes, seismic waves, typhoons, floods, and forest fires, have been on the rise. As a result, a lot of personal injuries and property damage have occurred [1]. An accident occurring naturally has led into safety ignorance which has caused a large scale accident. Therefore, since such a accident was recognized to be a social issue, the interest of the government and the voluntary participation of each civic group have resulted in much active research on disaster prevention [2]. According to Emergency J.-P. Lee · J.-G. Lee · E.-s. Mo · J.-h. Lee · J.-K. Lee() Department of Computer Engineering, Hannam University, Daejeon, Korea e-mail: {jplee,jglee,esmo,jhlee}@netwk.hnu.kr, [email protected] © Springer Science+Business Media Singapore 2015 D.-S. Park et al. (eds.), Advances in Computer Science and Ubiquitous Computing, Lecture Notes in Electrical Engineering 373, DOI: 10.1007/978-981-10-0281-6_58
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Events Database (EM-DAT Database) of the Centre for Research on the Epidemiology of Disasters (CRED) in 2013 at least 330 natural disasters claimed 21,610 lives, made 96.5 million victims, and caused the property losses worth 118.6 billion dollars in 108 countries. The biggest single source disaster that took the most number of lives was Haiyan, the typhoon hitting the Philippines, which killed 7,354 persons. The next biggest one was an Indian flood that had 6,054 persons dead [3]. In the aspect, it is very significant to find the characteristics of temporal and spatial distributions of disasters and analyze their determinants in terms of preparation and strategy of disasters. In the US, big data about weather and climate have been applied more to the public fields, including establishment of climate prediction and disaster response real-time network system using satellite image data [4]. In Korea, since the big accidents including collapses of Seongsu Bridge and Sampung Department Store, di
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