Intelligent Post-Disaster Networking by Exploiting Crowd Big Data
Intelligent Post-Disaster Networking by Exploiting Crowd Big Data
复制标题
利用人群大数据实现灾后智能组网
DOI：
10.1109/mnet.011.1900389
复制
发表时间：
2020
期刊：
影响因子：
9.3
通讯作者：
and Guoliang Xue
中科院分区：
文献类型：
--
作者：
Xiaoyan Wang;Fangzhou Jiang;Lei Zhong;Yusheng Ji;Shigeki Yamada;Kiyoshi Takano;and Guoliang Xue
重大灾害会严重破坏通信基础设施，导致灾区进一步混乱和损失。快速恢复无线/移动的通信是救灾工作的最关键问题之一。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，可以在大规模灾害发生后有效地将网络服务扩展到受灾地区的人们。准确估计灾后的人口分布，并在此基础上合理地配置有限数量的中继节点，以最大化人口覆盖率，是非常重要的。在这篇文章中，我们提出了一个智能灾后网络的方法，利用人群动态。首先，我们提出了一个基于长短期记忆的神经网络来预测灾后人口的时空分布。该神经网络使用2016年熊本地震期间收集的真实的人群动态数据集进行训练。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世界的情况下进行评估。结果表明，与回归模型相比，种群分布的估计误差减小了56~ 69%，而且有限数量的中继站可以有效地覆盖大部分种群。
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.
DOI：
10.1109/pimrc.2016.7794910
发表时间：
2016-09
期刊：
2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
影响因子：
--
作者：
Xiaoyan Wang;Hao Zhou;L. Zhong;Yusheng Ji;K. Takano;S. Yamada;G. Xue
通讯作者：
Xiaoyan Wang;Hao Zhou;L. Zhong;Yusheng Ji;K. Takano;S. Yamada;G. Xue