Roteamento em Redes de Sensores Sem Fios Com Base Em Aprendizagem Por Reforço
The use of wireless sensor and actuator networks in industry has been increasing past few years, bringing multiple benefits compared to wired systems, like network flexibility and manageability. Such networks consists of a possibly large number of small and autonomous sensor and actuator devices wit...
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| Hovedforfatter: | |
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| Andre forfattere: | |
| Format: | Dissertação |
| Sprog: | por |
| Udgivet: |
Universidade Federal do Rio Grande do Norte
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| Fag: | |
| Online adgang: | https://repositorio.ufrn.br/jspui/handle/123456789/15451 |
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| Summary: | The use of wireless sensor and actuator networks in industry has been increasing past
few years, bringing multiple benefits compared to wired systems, like network flexibility
and manageability. Such networks consists of a possibly large number of small and autonomous
sensor and actuator devices with wireless communication capabilities. The data
collected by sensors are sent directly or through intermediary nodes along the network
to a base station called sink node. The data routing in this environment is an essential
matter since it is strictly bounded to the energy efficiency, thus the network lifetime. This
work investigates the application of a routing technique based on Reinforcement Learning s
Q-Learning algorithm to a wireless sensor network by using an NS-2 simulated
environment. Several metrics like energy consumption, data packet delivery rates and delays
are used to validate de proposal comparing it with another solutions existing in the
literature |
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