Deep Neuronal Based Classifiers for Wireless Multi-hop Network Mobility Models

Abstract

Mobility plays an important role in the performance of wireless multi-hop networks. Since communications are established in a multi-hop fashion, the mobility of nodes can cause a significant degradation of the performance. Therefore, the analysis of nodes' mobility is relevant to improve the performance of the applications implemented over wireless multi-hop networks. This work evaluates two neuronal network models, such as fully connected or multi-layer perceptron and 1D convolutional models, for the classification of up to four widely used mobility models for wireless multi-hop networks. Several architectures are evaluated and parametrized for both models. The results indicate a considerable better performance of an architecture with 1D convolutional layers. The test results show that the best convolutional 1D model is able to reach an accuracy level of 0.91, outperforming the best multi-layer perceptron model in 13,9 %.