文献类型：期刊文献

英文题名：Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks

作者：Huang, Ru[1];Guan, Wenfan[1];Zhai, Guangtao[2];He, Jianhua[3];Chu, Xiaoli[4]

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：2022

卷号：12

期号：4

外文期刊名：APPLIED SCIENCES-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000778159000001)】；

基金：This work was supported in part by National Natural Science Foundation of China under Grant 61673178 and 61922063; in part by Natural Science Foundation of Shanghai under Grant 20ZR1413800; in part by European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 824019 and 101022280.

语种：英文

外文关键词：software-defined wireless sensor network; intelligent routing control; deep reinforcement learning; graph convolutional network

摘要：Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.