Intelligent post-disaster networking by exploiting crowd big data

Xiaoyan Wang, Fangzhou Jiang, Lei Zhong, Yusheng Ji, Shigeki Yamada, Kiyoshi Takano, Guoliang Xue

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


A major disaster would damage the communication infrastructure severely, resulting in further chaos and loss in the disaster stricken area. Rapid restoration of wireless/mobile communications is one of the most critical issues for disaster response. Wireless multihop networking by deploying low-cost relays is a promising solution to effectively extend network services to people in the disrupted areas after large-scale disasters have occurred. It is of great importance to accurately estimate the population distribution after a disaster and, based on that, judiciously place a limited number of relay nodes to maximize the population coverage ratio. In this article we present an intelligent post-disaster networking approach by exploiting crowd dynamics. First, we present a long short-term memory based neural network to predict the spatio-temporal population distribution after a disaster. The neural network is trained by using a real crowd dynamics dataset collected during the Kumamoto earthquake in 2016. Then, based on the fine-grained population estimation result, we present three simple algorithms for the budget-constrained population-aware relay placement problem. The proposed approach is evaluated in real-world scenarios. The results show that the estimation error for population distribution is reduced by 56~69 percent compared to the regressive models, and a large proportion of the population could be efficiently covered by a limited number of relays.

Original languageEnglish (US)
Article number9146415
Pages (from-to)49-55
Number of pages7
JournalIEEE Network
Issue number4
StatePublished - Jul 1 2020

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications


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