Classifying Smart IoT Devices for Running Machine Learning Algorithms
Resumo
Tiny computers called System-on-a-Chip like Raspberry Pi have revolutionized the development of applications for Smart Home and Smart City. Some Machine Learning algorithms have been used to process a large amount of data produced by these Internet of Things (IoT) devices. An important issue in the context of processing IoT data is the decision on where the machine learning algorithm will run. To support this decision, it is necessary to classify the IoT devices according to their capabilities to run these algorithms, in terms of CPU performance, required memory, and energy demand. The aim of this paper is to classify IoT devices according to their capabilities to run machine learning algorithms, and reporting real experiments that validate the proposed classification.
Referências
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Anandhalli, M. and Baligar, V. P. (2017). A novel approach in real-time vehicle detection and tracking using raspberry pi. Alexandria Engineering Journal.
Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15):2787–2805.
Chen, G., Parada, C., and Heigold, G. (2014). Small-footprint keyword spotting using deep neural networks. In ICASSP, pages 4087–4091. IEEE.
Dastjerdi, A. V. and Buyya, R. (2016). Fog computing: Helping the internet of things realize its potential. IEEE Computer, 49(8):112–116.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. Computer Vision and Pattern Recognition (cs.CV).
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <05mb model size.
Kamath, G., Agnihotri, P., Valero, M., Sarker, K., and Song, W.-Z. (2016). Pushing analytics to the edge. In GLOBECOM, pages 1–6. IEEE.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Bartlett, P. L., Pereira, F. C. N., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, pages 1106–1114.
Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., and Kawsar, F. (2015a). An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices. In Xu, C., Zhang, P., and Sigg, S., editors, Proceedings of the 2015 International Workshop on Internet of Things towards Applications, IoT-App 2015, Seoul, South Korea, November 1, 2015, pages 7–12. ACM.
Lane, N. D., Georgiev, P., and Qendro, L. (2015b). Deepear: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In Mase, K., Langheinrich, M., Gatica-Perez, D., Gellersen, H., Choudhury, T., and Yatani, K., editors, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015, Osaka, Japan, September 7-11, 2015, pages 283–294. ACM.
LeCun, Y., Bengio, Y., and Hinton, G. E. (2015). Deep learning. Nature, 521(7553):436–444.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Scott Reed4, C.-Y. F., and Berg, A. C. (2016). Ssd: Single shot multibox detector. Computer Vision – ECCV 2016, 9905:21–37.
Nakamura, E. F., Loureiro, A. A. F., and Frery, A. C. (2007). Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Comput. Surv., 39(3).
Netzer, Y., Wang, T., Coates, A., Bissacco, R., Wu, B., and Ng, A. Y. (2011). Reading digits in natural images with unsupervised feature learning.
Qiu, J., Wu, Q., Ding, G., Xu, Y., and Feng, S. (2016). A survey of machine learning for big data processing. EURASIP J. Adv. Sig. Proc., 2016:67.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., Sangaiah, A. K., Elhoseny, M., and Baik, S. W. (2017). Raspberry pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Generation Computer Systems, November 2017.
Sebe, N., Cohen, I., Garg, A., and Huang, T. S. (2005). Machine Learning in Computer Vision, volume 29 of Computational Imaging and Vision. Springer.
Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., and Nikolopoulos, D. S. (2016). Challenges and opportunities in edge computing. In 2016 IEEE International Conference on Smart Cloud, SmartCloud 2016, New York, NY, USA, November 18-20, 2016, pages 20–26.
Publicado
26/07/2018
Como Citar
ROCHA NETO, Aluizio; SOARES, Bárbara; BARBALHO, Felipe; SANTOS, Luis; BATISTA, Thais; DELICATO, Flávia C.; PIRES, Paulo F..
Classifying Smart IoT Devices for Running Machine Learning Algorithms. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 45. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 24-35.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2018.3429.
