Abstract
The health state of edge layer nodes significantly affects the reliability of the calculation and the development of related applications. Edge layer nodes health assessment is adopted to forecast node state to arrange calculation and application reasonably. The existing multidimensional data evaluation algorithms have achieved good predictive performance. However, with a small scale of training data, those algorithms could easily encounter overfitting and poor robustness. Therefore, we propose an evaluation algorithm based on the multidimensional operation data of edge layer nodes in this study. In order to solve the problem above, we propose an improved Long Short Term Memory (LSTM) model to implement the evaluation. We add feature discretization and annealing processes to the model to reduce the risk of model overfitting. Compared with typical time series prediction models, the proposed LSTM model has stronger applicability and better accuracy in the evaluation of network delay of edge layer nodes in our experiment.
Q. Sun and Z. Wang—Contribute equally to this work.
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Sun, Q., Wang, Z., Zhang, J., Liu, Q., Zhang, X. (2024). A Health Evaluation Algorithm for Edge Nodes Based on LSTM. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_21
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DOI: https://doi.org/10.1007/978-981-99-8126-7_21
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