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An LSTM-Based Method for Automatic Reliability Prediction of Cognitive Radio Vehicular Ad Hoc Networks

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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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Availability of Data and Materials

The datasets generated during the current study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Computer Engineering, Sirjan University of Technology, Sirjan, 78137-33385, Iran

    Somayeh Bahramnejad & Amineh Naseri

  2. Faculty of Computer Engineering, University of Isfahan, Hezarjarib, Isfahan, 8174673441, Isfahan, Iran

    Naser Movahhedinia

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SB and NM wrote and reviewed the manuscript. AN performed the experiments and provided the tables and diagrams in the experimental results section.

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Correspondence to Somayeh Bahramnejad.

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Bahramnejad, S., Movahhedinia, N. & Naseri, A. An LSTM-Based Method for Automatic Reliability Prediction of Cognitive Radio Vehicular Ad Hoc Networks. SN COMPUT. SCI. 5, 291 (2024). https://doi.org/10.1007/s42979-024-02603-z

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