# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 6 Conclusions  
In this work, the experimental results demonstrate that a routing protocol based on RL overcomes traditional protocols, such as BATMAN and OLSR, specifically in \(Ppl\) and throughput parameters. These network performance results prove the relevance of the RL-based routing protocols to improve the computer, and ad-hoc networks. However, the RL technique generates an extra overhead. Thus, the proposed and developed adjustment algorithm was able to reduce the network overhead in terms of reducing the number of control messages. The dynamic adjustment in the frequency of sending Hello messages provided a reduction of up to 18% overhead. This gain increases the network's payload providing better network performance. The global performance of the proposed method was optimized using different configurations and parameter values, leading to a final configuration which was defined experimentally. In terms of throughput and \(Ppl\), in most of the test scenarios used in this work the e-RLRP achieved better performance, specially with respect to the \(Ppl\) parameter. Therefore, it is demonstrated that the proposed solution reduces overhead and also improves the network conditions. Reducing network overhead in conventional protocols is an important approach because it provides performance improvements. This approach is even more relevant when it is used by new routing techniques, such RL, that aim to improve network performance but it generates extra overhead. Thus, an important contribution of this work is to demonstrate that extra overhead can be reduced using the proposed dynamic adjustment function. It is worth noting that in our experimental tests different network topologies and configurations were used, including different numbers of nodes and their drops, and also different numbers of traffic flows. Also, experimental results show the impact of network performance parameters on the user's QoE in the VoIP communication services. The e-RLRP obtained better values of \(R_{\text{WB}}\) due to having lower \(Ppl\) values despite to have higher RTT values, which are calculated according to (6) and (7) defined in the \(WB\) E-model algorithm. In this case, it is observed that \(Ppl\) has a greater negative impact on speech quality than RTT, for the values obtained in the simulation scenarios considered in this research. Results indicate a quality improvement of more than 90% if compared to OLSR, and up to 8% if compared to RLRP. Therefore, it can be concluded that the RL-based routing protocols has a significant positive impact on user's QoE in real-time communication services. Figure 15: \(R_{\text{WB}}\) Score in Scenario P.  
As a general conclusion, this research highlights the usefulness of incorporating machine-learning algorithms in routing protocols, specially for ad-hoc networks that recurrently present node drops. RL-based routing protocols can help to improve network conditions, and as a consequence, different communication applications are improved. In this work, only the VoIP service is evaluated, but in future works, video communication service will also be evaluated. Also, the implemented dynamic adjustment mechanism in the sending of Hello messages provided a performance improvement on the network, mainly by reducing overhead, which is an important approach to be applied in RL-based routing protocols. In a future work, the proposed e-RLRP will be implemented in a real network environment to validate the performance results and potential benefits found in our simulation tests. Also, the inclusion of a scheduler or decentralized schedulers will be considered to work in conjunction with the e-RLRP algorithm in a future research, in which more complex and dynamic networks will be also implemented. Methodology and data curation, writing--original draft, D.R.M. and H.P.d.M. ; formal analysis and validation, project conceptualization, R.L.R. ; Supervision, writing--original draft, project administration, L.W.