HealthDash: Monitoramento remoto de pacientes utilizando programação baseada em fluxo de dados
Resumo
Neste artigo, propomos o HealthDash, um arcabouço para o desen- volvimento de soluções IoT para saúde. O HealthDash emprega o paradigma de programação orientado a fluxo de dados, desde camada cloud à borda da rede (camada fog), unificando tecnologias de desenvolvimento em todas as ca- madas, isto é, dos dispositivos de borda à tomada de decisão. Para validar a proposta, foi conduzido um experimento que simulou a transmissão de da- dos de pacientes crônicos monitorados em domicı́lio, considerando dois modos de envio: transmissão contı́nua de dados e transmissão de dados baseada em eventos. O experimento mostrou que a solução proposta fornece infraestrutura flexı́vel, consumindo menos largura de banda e menos tempo de resposta.
Referências
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Giang, N. K., Blackstock, M., Lea, R., and Leung, V. C. (2015). Developing IoT applications in the Fog: A Distributed Dataflow approach. Proceedings - 2015 5th International Conference on the Internet of Things, IoT 2015, (January 2016):155–162. http://dx.doi.org/10.1109/IOT.2015.7356560
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Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., and Koldehofe, B. (2013). Mobile fog: a programming model for large-scale applications on the internet of things. In Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing - MCC ’13, page 15, New York, New York, USA. ACM Press. http://dx.doi.org/10.1145/2491266.2491270
Liu, L., Stroulia, E., Nikolaidis, I., Miguel-Cruz, A., and Rios Rincon, A. (2016). Smart homes and home health monitoring technologies for older adults: A systematic review. International Journal of Medical Informatics, 91:44–59. http://dx.doi.org/10.1016/j.ijmedinf.2016.04.007
Malik, B. H., Cheema, S. N., Iqbal, I., Mahmood, Y., Ali, M., and Mudasser, A. (2018). From Cloud Computing to Fog Computing ( C2F ): The key technology provides services in health care big data. 03010. http://dx.doi.org/10.1051/matecconf/201818903010
Pare, G., Jaana, M., and Sicotte, C. (2007). Systematic Review of Home Telemonitoring for Chronic Diseases: The Evidence Base. Journal of the American Medical Informatics Association, 14(3):269–277. http://dx.doi.org/10.1197/jamia.M2270
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., and Liljeberg, P. (2018). Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems, 78:641–658. http://dx.doi.org/10.1016/j.future.2017.02.014
Sousa, T. B. (2012). Dataflow Programming Concept, Languages and Applications. Doctoral Symposium on Informatics Engineering, 7(November):13.
Ahmad, M., Amin, M. B., Hussain, S., Kang, B. H., Cheong, T., and Lee, S. (2016). Health Fog: a novel framework for health and wellness applications. The Journal of Supercomputing, 72(10):3677–3695. http://dx.doi.org/10.1007/s11227-016-1634-x
Cerina, L., Notargiacomo, S., Paccanit, M. G., and Santambrogio, M. D. (2017). A fog-computing architecture for preventive healthcare and assisted living in smart ambients. Proc. Int. Forum on Research and Technologies for Society and Industry - RTSI 2017. http://dx.doi.org/10.1109/RTSI.2017.8065939
Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., and Mankodiya, K. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, 78:659–676. http://dx.doi.org/10.1016/j.future.2017.04.036
Gia, T. N., Jiang, M., Rahmani, A. M., Westerlund, T., Liljeberg, P., and Tenhunen, H. (2015). Fog computing in healthcare Internet of Things: A case study on ECG feature extraction. Proceedings of the CIT 2015, pages 356–363. http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.51
Giang, N. K., Blackstock, M., Lea, R., and Leung, V. C. (2015). Developing IoT applications in the Fog: A Distributed Dataflow approach. Proceedings - 2015 5th International Conference on the Internet of Things, IoT 2015, (January 2016):155–162. http://dx.doi.org/10.1109/IOT.2015.7356560
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. http://dx.doi.org/10.1002/spe.2509
Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., and Koldehofe, B. (2013). Mobile fog: a programming model for large-scale applications on the internet of things. In Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing - MCC ’13, page 15, New York, New York, USA. ACM Press. http://dx.doi.org/10.1145/2491266.2491270
Liu, L., Stroulia, E., Nikolaidis, I., Miguel-Cruz, A., and Rios Rincon, A. (2016). Smart homes and home health monitoring technologies for older adults: A systematic review. International Journal of Medical Informatics, 91:44–59. http://dx.doi.org/10.1016/j.ijmedinf.2016.04.007
Malik, B. H., Cheema, S. N., Iqbal, I., Mahmood, Y., Ali, M., and Mudasser, A. (2018). From Cloud Computing to Fog Computing ( C2F ): The key technology provides services in health care big data. 03010. http://dx.doi.org/10.1051/matecconf/201818903010
Pare, G., Jaana, M., and Sicotte, C. (2007). Systematic Review of Home Telemonitoring for Chronic Diseases: The Evidence Base. Journal of the American Medical Informatics Association, 14(3):269–277. http://dx.doi.org/10.1197/jamia.M2270
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., and Liljeberg, P. (2018). Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems, 78:641–658. http://dx.doi.org/10.1016/j.future.2017.02.014
Sousa, T. B. (2012). Dataflow Programming Concept, Languages and Applications. Doctoral Symposium on Informatics Engineering, 7(November):13.
Publicado
11/06/2019
Como Citar
CELESTRINI, Jordano R.; ROCHA, Renato N.; SANTOS, Celso A. S.; MOTA, Vinícius F. S. ; FILHO, José G. Pereira; ANDREÃO, Rodrigo V..
HealthDash: Monitoramento remoto de pacientes utilizando programação baseada em fluxo de dados. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 222-233.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6256.
