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.

中文翻译：

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利用人群大数据实现灾后智能组网


一旦发生重大灾害，通信基础设施将遭到严重破坏，导致灾区进一步混乱和损失。无线/移动通信的快速恢复是灾难响应中最关键的问题之一。通过部署低成本中继的无线多跳网络是一种很有前途的解决方案，可以在发生大规模灾难后有效地将网络服务扩展到受破坏地区的人们。准确估计灾后人口分布并在此基础上明智地放置有限数量的中继节点以最大化人口覆盖率具有重要意义。在本文中，我们提出了一种利用人群动态的智能灾后网络方法。首先，我们提出了一种基于长短期记忆的神经网络来预测灾难后的时空人口分布。使用 2016 年熊本地震期间收集的真实人群动态数据集来训练神经网络。然后，基于细粒度的人口估计结果，我们提出了三种简单的算法来解决预算受限的人口感知中继放置问题。所提出的方法在现实场景中进行了评估。结果表明，与回归模型相比，人口分布的估计误差降低了56%~69%，并且可以通过有限数量的中继有效覆盖大部分人口。