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Enhanced Routing Algorithm Based on Reinforcement Machine Learning-A Case of VoIP Service.

Davi Ribeiro Militani1, Hermes Pimenta de Moraes1, Renata Lopes Rosa1

  • 1Department of Computer Science, Federal University of Lavras, Minas Gerais 37200-000, Brazil.

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Summary
This summary is machine-generated.

This study introduces an enhanced routing protocol using reinforcement learning (RL) that reduces network overhead. The new e-RLRP protocol improves network performance and voice communication quality compared to existing methods.

Keywords:
QoEVoIPintelligent routingmachine learningreinforcement learningrouting algorithms

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Area of Science:

  • Computer Science
  • Network Engineering
  • Machine Learning

Background:

  • Conventional routing algorithms neglect historical network data like congestion and faults.
  • Machine learning-based routing algorithms offer potential advantages by utilizing network data.
  • Reinforcement learning (RL) routing may introduce additional control message overhead.

Purpose of the Study:

  • To present an enhanced routing protocol, e-RLRP, based on RL with reduced overhead.
  • To dynamically adjust the Hello message interval to offset RL-induced overhead.
  • To evaluate e-RLRP's performance against established protocols in various network conditions.

Main Methods:

  • Implemented an enhanced routing protocol (e-RLRP) leveraging RL with dynamic Hello message intervals.
  • Simulated diverse network scenarios varying in node count, routes, traffic, and mobility.
  • Assessed network parameters (packet loss, delay, throughput, overhead) and Voice-over-IP (VoIP) quality using the E-model algorithm.

Main Results:

  • e-RLRP demonstrated reduced network overhead compared to the baseline RLRP.
  • The e-RLRP protocol outperformed OLSR, BATMAN, and RLRP in most evaluated network scenarios.
  • Performance improvements were observed in terms of network parameters and predicted VoIP quality.

Conclusions:

  • The e-RLRP protocol effectively mitigates overhead associated with RL in routing.
  • e-RLRP offers superior network performance and communication quality compared to traditional and existing RL-based protocols.
  • The dynamic Hello message adjustment is a viable strategy for overhead compensation in RL routing.