The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning present advantages using that network data. Nevertheless, in a routing algorithm based on reinforcement learning (RL) technique, additional control message headers could be required. In this context, this research presents an enhanced routing protocol based on RL, named e-RLRP, in which the overhead is reduced. Specifically, a dynamic adjustment in the Hello message interval is implemented to compensate the overhead generated by the use of RL. Different network scenarios with variable number of nodes, routes, traffic flows and degree of mobility are implemented, in which network parameters, such as packet loss, delay, throughput and overhead are obtained. Additionally, a Voice-over-IP (VoIP) communication scenario is implemented, in which the E-model algorithm is used to predict the communication quality. For performance comparison, the OLSR, BATMAN and RLRP protocols are used. Experimental results show that the e-RLRP reduces network overhead compared to RLRP, and overcomes in most cases all of these protocols, considering both network parameters and VoIP quality.
A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach
