文献类型：期刊文献

英文题名：Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction

作者：Huang, Ru[1];Ma, Lei[1];Zhai, Guangtao[2];He, Jianhua[3];Chu, Xiaoli[4];Yan, Huaicheng[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Inst Image Commun & Informat Proc, Shanghai 200240, Peoples R China;[3]Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England;[4]Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England

年份：2020

卷号：8

起止页码：64857

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20201708569100);WOS:【SCI-EXPANDED(收录号:WOS:000530832200186)】；

基金：This work was supported in part by the Shanghai Science and Technology Development Foundation under Grant 17511108604, in part by the National Natural Science Foundation of China under Grant 61501187, Grant 61673178, and Grant 61922063, in part by the Shanghai Natural Science Foundation under Grant 17ZR1444700, and in part by the Shanghai Shuguang Project under Grant 16SG28.

语种：英文

外文关键词：Routing; Wireless sensor networks; Prediction algorithms; Complex networks; Deep learning; Reliability engineering; Deep learning; link prediction; routing; wireless sensor networks; reliability

摘要：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 & x0025; 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.