Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction
Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction
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DOI：
10.1109/access.2020.2984593
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发表时间：
2020-04
期刊：
影响因子：
3.9
通讯作者：
Ru Huang;Lei Ma;Guangtao Zhai;Jianhua He;Xiaoli Chu;Huaicheng Yan
中科院分区：
文献类型：
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作者：
Ru Huang;Lei Ma;Guangtao Zhai;Jianhua He;Xiaoli Chu;Huaicheng Yan
无线传感器网络在物联网系统和服务中发挥着重要作用，但容易受到通信信道质量差和网络攻击的影响。在本文中，我们的动机是提出弹性路由算法的无线传感器网络。其主要思想是利用链路的可靠性沿着与其他传统的路由算法设计的度量。我们首先提出了一种新的基于深度学习的链接预测模型，该模型联合利用Weisfeiler-Lehman内核和双卷积神经网络（WL-DCNN）进行轻量级子图提取和标记。利用该算法提高了拓扑特征挖掘的自学习能力，具有较强的通用性。实验结果表明，WL-DCNN在6个开放复杂网络数据集上的性能优于所有研究的9个基线方案。AUC（受试者工作特征曲线下面积）的性能平均提高了16%。此外，将WL-DCNN模型应用于无线传感器网络弹性路由设计中，能够自适应地捕捉拓扑特征，以确定目标链路的可靠性，尤其是在路由表遭受攻击并对局部链路社区造成不同程度破坏的情况下。实验结果表明，与其他经典路由算法相比，该路由算法在保持高能效的同时，能够有效提高传感器网络的弹性.
Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks. In this paper we are motivated to propose resilient routing algorithms for wireless sensor networks. The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design. We proposed firstly a novel deep-learning based link prediction model, which jointly exploits Weisfeiler-Lehman kernel and Dual Convolutional Neural Network (WL-DCNN) for lightweight subgraph extraction and labelling. It is leveraged to enhance self-learning ability of mining topological features with strong generality. Experimental results demonstrate that WL-DCNN outperforms all the studied 9 baseline schemes over 6 open complex networks datasets. The performance of AUC (Area Under the receiver operating characteristic Curve) is improved by 16% on average. Furthermore, we apply the WL-DCNN model in the design of resilient routing for wireless sensor networks, which can adaptively capture topological features to determine the reliability of target links, especially under the situations of routing table suffering from attack with varying degrees of damage to local link community. It is observed that, compared with other classical routing baselines, the proposed routing algorithm with link reliability prediction module can effectively improve the resilience of sensor networks while reserving high-energy-efficiency.