Otimização do Consumo de Energia em Redes Ad Hoc Aloha Empregando Deep Learning

  • Paulo F. C. Barbosa UFPE
  • Bruna A. da Silva UFPE
  • David Macedo UFPE
  • Cleber Zanchettin UFPE
  • Renato M. de Moraes UFPE

Resumo


Os algoritmos normalmente empregados para controle energético em redes IoT envolvem funções de otimização com considerável complexidade e controle rigoroso do ambiente de teste. Isso gera uma lacuna entre o projeto, análise teórica e processamento em tempo real dos dispositivos da rede. O presente artigo propõe uma nova abordagem baseada em aprendizagem de máquina que considera a entrada e a saı́da de um algoritmo de controle de consumo de energia em redes ad hoc slotted Aloha de múltiplas varáveis. Resultados mostram que a rede neural proposta obteve melhor desempenho em relação ao tempo de processamento e custo computacional quando comparado aos algoritmos de controle energético de busca gulosa utilizados atualmente.

Palavras-chave: Consumo de energia, deep learning, protocolo Aloha, redes ad hoc

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Publicado
08/07/2019
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BARBOSA, Paulo F. C.; DA SILVA, Bruna A.; MACEDO, David ; ZANCHETTIN, Cleber ; DE MORAES, Renato M.. Otimização do Consumo de Energia em Redes Ad Hoc Aloha Empregando Deep Learning. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 2019. , 2019, Belém. Anais do XVIII Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Porto Alegre: Sociedade Brasileira de Computação, july 2019 . ISSN 2595-6167. DOI: https://doi.org/10.5753/wperformance.2019.6462.