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IoT 21(26): e2

Research Article

Technology of Internet of Things Responding to Natural Disasters

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  • @ARTICLE{10.4108/eai.8-11-2021.172000,
        author={Shu-Wen Chen and Jia-Qi Shao and Hui-Sheng Zhu},
        title={Technology of Internet of Things Responding to Natural Disasters},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={7},
        number={26},
        publisher={EAI},
        journal_a={IOT},
        year={2021},
        month={11},
        keywords={IoT, natural disaster, monitoring, early warning},
        doi={10.4108/eai.8-11-2021.172000}
    }
    
  • Shu-Wen Chen
    Jia-Qi Shao
    Hui-Sheng Zhu
    Year: 2021
    Technology of Internet of Things Responding to Natural Disasters
    IOT
    EAI
    DOI: 10.4108/eai.8-11-2021.172000
Shu-Wen Chen1,2,*, Jia-Qi Shao1, Hui-Sheng Zhu1
  • 1: School of Math and Information Technology, Jiangsu Second Normal University, Nanjing 211200, China
  • 2: State Key Laboratory of Millimetre Waves, Southeast University, Nanjing 210096, China
*Contact email: chenshuwen@126.com

Abstract

Numerous countries have been affected by natural disasters, the seriousness of which consequences is self-evident, such as power failure, building collapse and communication interruption. With the rapid development of science and technology, the Internet of Things (IoT) has become one of the most important part of current information technology. More and more countries and regions have realized the significance of IoT in dealing with natural disasters and are making efforts to put IoT into use. This paper summarizes the latest progress of IoT with fog computing, swarm robots and other advanced technologies in coping with natural disasters on four aspects. It focuses on how the IoT and other information technologies can solve problems encountered in natural disasters to a certain extent.

Keywords
IoT, natural disaster, monitoring, early warning
Received
2021-09-22
Accepted
2021-10-28
Published
2021-11-08
Publisher
EAI
http://dx.doi.org/10.4108/eai.8-11-2021.172000

Copyright © 2021 Shu-Wen Chen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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