Otimização do Consumo de Energia em Redes Ad Hoc Aloha Empregando Deep Learning
Abstract
The algorithms commonly used for energy control in IoT networks involve optimization functions with considerable complexity and rigorous control of the test environment. It creates a gap between design, theoretical analysis and real-time processing of the network devices. In this paper, we propose a novel approach based on machine learning which considers the input and output of a power consumption control algorithm in multi-variable slotted Aloha ad hoc networks. Results show that the proposed neural network presented better performance concerning processing time and computational cost when compared to the currently used greedy search energy control algorithms.
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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.
Published
2019-07-08
How to Cite
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 ON PERFORMANCE OF COMPUTER AND COMMUNICATION SYSTEMS (WPERFORMANCE), 2019. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2019.6462.
