| Full text | |
| Author(s): |
Militani, Davi Ribeiro
[1]
;
de Moraes, Hermes Pimenta
[1]
;
Rosa, Renata Lopes
[1]
;
Wuttisittikulkij, Lunchakorn
[2]
;
Ramirez, Miguel Arjona
[3]
;
Rodriguez, Demostenes Zegarra
[1]
Total Authors: 6
|
| Affiliation: | [1] Univ Fed Lavras, Dept Comp Sci, BR-37200000 Lavras, MG - Brazil
[2] Chulalongkorn Univ, Dept Elect Engn, Wireless Commun Ecosyst Res Unit, Bangkok 10330 - Thailand
[3] Univ Sao Paulo, Dept Elect Syst Engn, BR-05508010 Sao Paulo - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | SENSORS; v. 21, n. 2 JAN 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 18/26455-8 - Audio-Visual Speech Processing by Machine Learning |
| Grantee: | Miguel Arjona Ramírez |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 18/12579-7 - ELIOT: enabling technologies for IoT |
| Grantee: | Vitor Heloiz Nascimento |
| Support Opportunities: | Research Projects - Thematic Grants |