An LSTM-Based Method for Automatic Reliability Prediction of Cognitive Radio Vehicular Ad Hoc Networks
Abstract
Reliability is a critical issue in vehicular networks. A deep learning (DL) method is proposed in this study to automatically predict the reliability of cognitive radio vehicular networks (CR-VANETs) ignored in the previous research. First, a dataset is generated based on a previously proposed method for the reliability assessment of CR-VANETs. Then, a model is proposed to predict the networks’ reliability using the DL method and compared with other machine learning methods. While machine learning methods have been applied in vehicular networks, they have not been used for reliability prediction. The proposed DL model is utilized in this research to predict CR-VANETs’ reliability. Based on the results, the DL model outperforms other machine learning methods for reliability prediction. The correlation coefficient and root mean square error of the test data for the DL model are 0.9862 and 0.0381, respectively. These results indicate the CR-VANETs’ reliability prediction accurately using the proposed method.
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Published: 26 February 2024
Accepted: 02 January 2024
Received: 18 June 2023
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