Fusão Hierárquica de Dados para Sistemas de Computação em Névoa
Resumo
O paradigma da computação em névoa possibilita que os recursos computacionais sejam levados para a borda da rede, de forma que a latência e a largura de banda resultantes da computação em nuvem possam ser reduzidas. A combinação desse paradigma com técnicas de fusão de dados e processamento de eventos, a partir da aquisição de dados brutos, tem grande potencial de uso para gerar conhecimento de valor e ajudar a tomada de decisão em sistemas de IoT. Nesse contexto, foi proposta uma arquitetura de fusão de dados em três níveis (nó sensor, névoa e nuvem) que, aliada ao uso de técnicas de processamento de eventos complexos, promove a tomada de decisões com rapidez, enquanto reduz o custo de transmissão de dados na rede. Os experimentos mostram uma redução de 77% no tempo médio de envio de mensagens na rede e 82% de melhoria na vazão média com a utilização da arquitetura proposta.
Palavras-chave:
Computação em Névoa, Fusão de Dados, Processamento de Eventos Complexos
Referências
Akila, V., Govindasamy, V., and Sandosh, S. (2016a). Complex event processing over uncertain events: Techniques, challenges, and future directions. In 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), pages 204–221.
Akila, V., Govindasamy, V., and Sandosh, S. (2016b). Complex event processing over uncertain events: Techniques, challenges, and future directions. In 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), pages 204–221.
Alturki, B., Reiff-Marganiec, S., and Perera, C. (2017). A hybrid approach for data analytics for internet of things. In Proceedings of the Seventh International Conference on the Internet of Things, IoT ’17, pages 7:1–7:8, New York, NY, USA. ACM.
Benarbia, S., Alaoui, N., and Bennani, S. (2018). Complex event processing distributed architecture for massive open online courses. In 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT), pages 273–276.
Bierzynski, K., Escobar, A., and Eberl, M. (2017). Cloud, fog and edge: Cooperation for the future? In 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pages 62–67.
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., and Buyya, R. (2011). Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50.
Caprolu, M., Di Pietro, R., Lombardi, F., and Raponi, S. (2019). Edge computing perspectives: Architectures, technologies, and open security issues. In 2019 IEEE International Conference on Edge Computing (EDGE), pages 116–123.
Cremonezi, B., Nogueira, M., dos Santos, A., Vieira, A. B., and Nacif, J. A. (2019). Um sistema multinı́vel de distribuição de identidades em névoas computacionais. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, pages 543–555, Porto Alegre, RS, Brasil. SBC.
Cugola, G. and Margara, A. (2012). Processing flows of information: From data stream to complex event processing. ACM Computing Surveys, 44.
Dautov, R., Distefano, S., and Buyya, R. (2019). Hierarchical data fusion for smart healthcare. Journal of Big Data, 6(1).
Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., and Buyya, R. (2017). ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience, 47(9):1275–1296.
Lan, L., Shi, R., Wang, B., Zhang, L., and Jiang, N. (2019). A universal complex event processing mechanism based on edge computing for internet of things real-time monitoring. IEEE Access, 7:101865–101878.
Lera, I., Guerrero, C., and Juiz, C. (2019). Yafs: A simulator for iot scenarios in fog computing. IEEE Access, 7:91745–91758.
Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., and Polakos, P. A. (2018). A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys Tutorials, 20(1):416–464.
Nakamura, E., Loureiro, A., and Frery, A. (2007). Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys (CSUR), 39:9.
Oliveira, S. and Kniess, J. (2019). Redução de dados em redes de sensores sem fio baseada em análise de dispersão. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, pages 1–14.
R.Dautov and S.Distefano (2017). Distributed data fusion for the internet of things. In PaCT, pages 427–432.
Vora, J., Tanwar, S., Tyagi, S., Kumar, N., and Rodrigues, J. J. P. C. (2017). Faal: Fog computing-based patient monitoring system for ambient assisted living. In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pages 1–6.
Zhang, L., Xie, Y., Xidao, L., and Zhang, X. (2018). Multi-source heterogeneous data fusion. In 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pages 47–51.
Zhang, Q., Zhang, X., Zhang, Q., Shi, W., and Zhong, H. (2016). Firework: Big data sharing and processing in collaborative edge environment. In 2016 Fourth IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pages 20–25.
Akila, V., Govindasamy, V., and Sandosh, S. (2016b). Complex event processing over uncertain events: Techniques, challenges, and future directions. In 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), pages 204–221.
Alturki, B., Reiff-Marganiec, S., and Perera, C. (2017). A hybrid approach for data analytics for internet of things. In Proceedings of the Seventh International Conference on the Internet of Things, IoT ’17, pages 7:1–7:8, New York, NY, USA. ACM.
Benarbia, S., Alaoui, N., and Bennani, S. (2018). Complex event processing distributed architecture for massive open online courses. In 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT), pages 273–276.
Bierzynski, K., Escobar, A., and Eberl, M. (2017). Cloud, fog and edge: Cooperation for the future? In 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pages 62–67.
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., and Buyya, R. (2011). Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50.
Caprolu, M., Di Pietro, R., Lombardi, F., and Raponi, S. (2019). Edge computing perspectives: Architectures, technologies, and open security issues. In 2019 IEEE International Conference on Edge Computing (EDGE), pages 116–123.
Cremonezi, B., Nogueira, M., dos Santos, A., Vieira, A. B., and Nacif, J. A. (2019). Um sistema multinı́vel de distribuição de identidades em névoas computacionais. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, pages 543–555, Porto Alegre, RS, Brasil. SBC.
Cugola, G. and Margara, A. (2012). Processing flows of information: From data stream to complex event processing. ACM Computing Surveys, 44.
Dautov, R., Distefano, S., and Buyya, R. (2019). Hierarchical data fusion for smart healthcare. Journal of Big Data, 6(1).
Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., and Buyya, R. (2017). ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience, 47(9):1275–1296.
Lan, L., Shi, R., Wang, B., Zhang, L., and Jiang, N. (2019). A universal complex event processing mechanism based on edge computing for internet of things real-time monitoring. IEEE Access, 7:101865–101878.
Lera, I., Guerrero, C., and Juiz, C. (2019). Yafs: A simulator for iot scenarios in fog computing. IEEE Access, 7:91745–91758.
Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., and Polakos, P. A. (2018). A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys Tutorials, 20(1):416–464.
Nakamura, E., Loureiro, A., and Frery, A. (2007). Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys (CSUR), 39:9.
Oliveira, S. and Kniess, J. (2019). Redução de dados em redes de sensores sem fio baseada em análise de dispersão. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, pages 1–14.
R.Dautov and S.Distefano (2017). Distributed data fusion for the internet of things. In PaCT, pages 427–432.
Vora, J., Tanwar, S., Tyagi, S., Kumar, N., and Rodrigues, J. J. P. C. (2017). Faal: Fog computing-based patient monitoring system for ambient assisted living. In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pages 1–6.
Zhang, L., Xie, Y., Xidao, L., and Zhang, X. (2018). Multi-source heterogeneous data fusion. In 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pages 47–51.
Zhang, Q., Zhang, X., Zhang, Q., Shi, W., and Zhong, H. (2016). Firework: Big data sharing and processing in collaborative edge environment. In 2016 Fourth IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pages 20–25.
Publicado
07/12/2020
Como Citar
BEZERRA, Sandy Ferreira; CORDEIRO, Matheus Gomes; ROCHA, Atslands; DELICATO, Flavia.
Fusão Hierárquica de Dados para Sistemas de Computação em Névoa. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 714-727.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12320.
