Otimização do Consumo de Energia em Redes Ad Hoc Aloha Empregando Deep Learning
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
Referências
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Baligh, H., Hong, M., Liao, W.-C., Luo, Z.-Q., Razaviyayn, M., Sanjabi, M., and Sun, R. (2014). Cross layer provision of future cellular networks. arXiv preprint ar- Xiv:1407.1424.
Da Silva, B. A. and De Moraes, R. M. (Sep. 2018). Um estudo sobre o consumo de energia em redes ad hoc lineares aloha com saltos equidistantes. In Anais do XXXVI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais - Diponı́vel em: https://www.dropbox.com/s/89k2t7swo064afk/Anais SBrT 2018 v2.pdf?dl=0, Campina Grande, PB.
Fadlullah, Z., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., and Mizutani, K. (2017). State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s in- telligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4):2432–2455.
Huan, Y., Qin, Y., You, Y., Zheng, L., and Zou, Z. (Sep. 2016). A multiplication reduc- tion technique with near-zero approximation for embedded learning in iot devices. In System-on-Chip Conference (SOCC), 2016 29th IEEE International, Seattle, USA.
Kapoor, C., Singh, H., and Laxmi, V. (Apr. 2018). A survey on energy efficient routing for delay minimization in iot networks. In International Conference on Intelligent Circuits and Systems (ICICS), Phagwara, India.
Karl, H. and Willig, A. (2007). Protocols and architectures for wireless sensor networks. John Wiley & Sons.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436– 444.
Luxhøj, J. T., Williams, T. P., and Shyur, H.-J. (1997). Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft. IIE transactions, 29(2):91–101.
Mamdouh, M., Elrukhsi, M. A., and Khattab, A. (Aug. 2018). Securing the internet of things and wireless sensor networks via machine learning: A survey. In International Conference on Computer and Applications (ICCA), Beirut, Lebanon.
Mao, Q., Hu, F., and Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys Tutorials, 20(4):2595–2621.
Mohammadi, M., Al-Fuqaha, A., Sorour, S., and Guizani, M. (2018). Deep learning for iot big data and streaming analytics: A survey. IEEE Communications Surveys Tutorials, 20(4):2923–2960.
Nguyen, V.-T., Luong, T.-K., Le Duc, H., and Hoang, V.-P. (Sep. 2018). An efficient hardware implementation of activation functions using stochastic computing for deep neural networks. In 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Hanoi, Vietnam.
Samuel, A. and Sharma, D. K. (Jul. 2017). Summary generation using geo-coordinates and temporal data in microblogging environment. In 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India.
Shi, Q., Razaviyayn, M., Luo, Z.-Q., and He, C. (May. 2011). An iteratively weighted mmse approach to distributed sum-utility maximization for a mimo interfering bro- adcast channel. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., and Sidiropoulos, N. D. (Jul. 2017). Le- arning to optimize: Training deep neural networks for wireless resource management. In 18th International Workshop on Signal Processing Advances in Wireless Communi- cations (SPAWC), Sapporo, Japan.
Tachibana, K. and Otsuka, K. (Sep. 2018). Wind prediction performance of complex neural network with relu activation function. In 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Nara, Japan.
Zhang, R. and Gorce, J.-M. (Mar. 2008). Optimal transmission range for minimum energy consumption in wireless sensor networks. In IEEE Wireless Communications and Networking Conference (WCNC), Las Vegas, USA.
Zheng, J., Ma, Q., and Zhou, W. (Oct. 2016). Performance comparison of full-batch bp and mini-batch bp algorithm on spark framework. In 8th International Conference on Wireless Communications & Signal Processing (WCSP), Yangzhou, China.
Publicado
08/07/2019
Como Citar
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 [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2019.6462.
