Roteamento em Redes de Sensores Sem Fios Com Base Em Aprendizagem Por Reforço

• Autor(a) principal: Campos, Leonardo Rene dos Santos
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/15451
• topic: Redes de Sensores e Atuadores Sem Fi. Simulação. Aprendizado Por Reforço. Técnicas de Roteamento; Wireless Sensor and Actuator Networks. Simulation. Reinforcement Learning. Routing Techniques; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

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