A Novel Opportunistic Access Algorithm Based on GCN Network in Internet of Mobile Things
Xingqiang Cai, Jie Sheng, Yiming Wang, Bo Ai, Cheng Wu
Abstract
The Internet of Things (IoT) will be widely used in all areas of life and transportation as the 5th Generation (5G) communication technology matures and becomes commercially available. Especially in the field of railway transportation, the IoT technology can alleviate the challenge caused by insufficient wireless spectrum resources and improve the railway communication performance. However, the existing IoT is made up of a large heterogeneous network. In such a super-dense heterogeneous network scenario, how to allocate the most appropriate access point (AP) according to the needs of users has become a problem demanding prompt solution, which also brings additional challenges for the intelligent transportation system (ITS) to develop green and efficient network communication technology. Therefore, focusing on the selection and access of heterogeneous networks in the Railway IoT, this article studies the spatial characteristics of the intelligent spectrum situation of the Internet of Mobile Things in the railway scenario, and establishes the opportunistic access situation of Railway IoT based on the graph convolutional neural (GCN) network. Furthermore, we utilize the GCN network to mine the spatial correlation between different APs, and propose a railway communication AP decision algorithm based on the GCN network combined with the traditional heterogeneous network multiattribute decision algorithm. Our experimental results prove that the proposed algorithm can effectively reduce transmission delay and improve the throughput of the communication system.