Internet das Coisas Médicas: Uma Avaliação de Desempenho Focando em Prioridades de Requisições
Resumo
A Internet das Coisas Médicas (IoHT) é uma subárea da Internet das Coisas voltada para o contexto de saúde. Dispositivos de monitoramento IoT são dotados de sensores e atuadores que podem tomar uma determinada ação e até salvar vidas. No entanto, existem situações médicas que precisam de sistemas computacionais mais sofisticados que geram uma grande carga de dados e requerem resposta em tempo real. Neste caso, muitas vezes se faz necessário avaliar possíveis arquiteturas de sistemas distribuídos para dar suporte à rede de sensores. Este trabalho apresenta um modelo de rede de filas representando uma arquitetura IoHT com prioridade de requisições. O modelo é altamente parametrizável e pode auxiliar profissionais de tecnologia no contexto de IoHT.
Palavras-chave:
IoHT, queuing theory, Health computing
Referências
Adhikari, M., Mukherjee, M., and Srirama, S. N. (2019). Dpto: A deadline and priority-aware task offloading in fog computing framework leveraging multilevel feedbackqueueing.IEEE Internet of Things Journal, 7(7):5773–5782.
Baker, S. B., Xiang, W., and Atkinson, I. (2017). Internet of things for smart healthcare:Technologies, challenges, and opportunities.IEEE Access, 5:26521–26544.
Bertoli, M., Casale, G., and Serazzi, G. (2009). Jmt: performance engineering tools forsystem modeling.SIGMETRICS Perform. Eval. Rev., 36(4):10–15.
de Morais Barroca Filho, I. and de Aquino Junior, G. S. (2017). Iot-based healthcareapplications: a review. InInternational conference on computational science and itsapplications, pages 47–62. Springer.
El Kafhali, S. and Salah, K. (2018). Performance modelling and analysis of internet ofthings enabled healthcare monitoring systems.IET Networks, 8(1):48–58
Haragos, I.-M. and Cernazanu-Glavan, C. (2012). Modelling road traffic using servicecenter.AECE 2012, 2
He, D., Ye, R., Chan, S., Guizani, M., and Xu, Y. (2018). Privacy in the internet of thingsfor smart healthcare.IEEE Communications Magazine, 56(4):38–44.
Iorga, M., Feldman, L., Barton, R., Martin, M. J., Goren, N. S., and Mahmoudi, C. (2018).Fog computing conceptual model.
Islam, M. M., Rahaman, A., and Islam, M. R. (2020). Development of smart healthcaremonitoring system in iot environment.SN computer science, 1:1–11.
Khoa, T. V., Saputra, Y. M., Hoang, D. T., Trung, N. L., Nguyen, D., Ha, N. V., and Dut-kiewicz, E. (2020). Collaborative learning model for cyberattack detection systems iniot industry 4.0. In2020 IEEE Wireless Communications and Networking Conference(WCNC), pages 1–6. IEEE.
Maktoubian, J. and Ansari, K. (2019). An iot architecture for preventive maintenance ofmedical devices in healthcare organizations.Health and Technology, 9(3):233–243.
Mohammadian, H. D., Mohammadian, F. D., and Assante, D. (2020). Iot-education po-licies on national and international level regarding best practices in german smes. In2020 IEEE Global Engineering Education Conf. (EDUCON), pages 1848–1857. IEEE.
Mukherjee, A., Ghosh, S., Behere, A., Ghosh, S. K., and Buyya, R. (2020). Internetof health things (ioht) for personalized health care using integrated edge-fog-cloudnetwork.Journal of Ambient Intelligence and Humanized Computing, pages 1–17.
Rubin, J., Abreu, R., Ganguli, A., Nelaturi, S., Matei, I., and Sricharan, K. (2017). Recog-nizing abnormal heart sounds using deep learning.arXiv preprint arXiv:1707.04642.
Sarmah, S. S. (2020). An efficient iot-based patient monitoring and heart disease predic-tion system using deep learning modified neural network.IEEE Access, 8:135784–135797.
Shafiq, M., Tian, Z., Sun, Y., Du, X., and Guizani, M. (2020). Selection of effective ma-chine learning algorithm and bot-iot attacks traffic identification for internet of thingsin smart city.Future Generation Computer Systems, 107:433–442.
Sztrik, J. et al. (2012). Basic queueing theory.University of Debrecen, Faculty of Infor-matics, 193:60–67.
Uslu, B. ç ., Okay, E., and Dursun, E. (2020). Analysis of factors affecting iot-based smarthospital design.Journal of Cloud Computing, 9(1):1–23.
Yassein, M. B., Hmeidi, I., Al-Harbi, M., Mrayan, L., Mardini, W., and Khamayseh,Y. (2019). Iot-based healthcare systems: a survey. InProceedings of the SecondInternational Conf. on Data Science, E-Learning and Information Systems, pages 1–9
Baker, S. B., Xiang, W., and Atkinson, I. (2017). Internet of things for smart healthcare:Technologies, challenges, and opportunities.IEEE Access, 5:26521–26544.
Bertoli, M., Casale, G., and Serazzi, G. (2009). Jmt: performance engineering tools forsystem modeling.SIGMETRICS Perform. Eval. Rev., 36(4):10–15.
de Morais Barroca Filho, I. and de Aquino Junior, G. S. (2017). Iot-based healthcareapplications: a review. InInternational conference on computational science and itsapplications, pages 47–62. Springer.
El Kafhali, S. and Salah, K. (2018). Performance modelling and analysis of internet ofthings enabled healthcare monitoring systems.IET Networks, 8(1):48–58
Haragos, I.-M. and Cernazanu-Glavan, C. (2012). Modelling road traffic using servicecenter.AECE 2012, 2
He, D., Ye, R., Chan, S., Guizani, M., and Xu, Y. (2018). Privacy in the internet of thingsfor smart healthcare.IEEE Communications Magazine, 56(4):38–44.
Iorga, M., Feldman, L., Barton, R., Martin, M. J., Goren, N. S., and Mahmoudi, C. (2018).Fog computing conceptual model.
Islam, M. M., Rahaman, A., and Islam, M. R. (2020). Development of smart healthcaremonitoring system in iot environment.SN computer science, 1:1–11.
Khoa, T. V., Saputra, Y. M., Hoang, D. T., Trung, N. L., Nguyen, D., Ha, N. V., and Dut-kiewicz, E. (2020). Collaborative learning model for cyberattack detection systems iniot industry 4.0. In2020 IEEE Wireless Communications and Networking Conference(WCNC), pages 1–6. IEEE.
Maktoubian, J. and Ansari, K. (2019). An iot architecture for preventive maintenance ofmedical devices in healthcare organizations.Health and Technology, 9(3):233–243.
Mohammadian, H. D., Mohammadian, F. D., and Assante, D. (2020). Iot-education po-licies on national and international level regarding best practices in german smes. In2020 IEEE Global Engineering Education Conf. (EDUCON), pages 1848–1857. IEEE.
Mukherjee, A., Ghosh, S., Behere, A., Ghosh, S. K., and Buyya, R. (2020). Internetof health things (ioht) for personalized health care using integrated edge-fog-cloudnetwork.Journal of Ambient Intelligence and Humanized Computing, pages 1–17.
Rubin, J., Abreu, R., Ganguli, A., Nelaturi, S., Matei, I., and Sricharan, K. (2017). Recog-nizing abnormal heart sounds using deep learning.arXiv preprint arXiv:1707.04642.
Sarmah, S. S. (2020). An efficient iot-based patient monitoring and heart disease predic-tion system using deep learning modified neural network.IEEE Access, 8:135784–135797.
Shafiq, M., Tian, Z., Sun, Y., Du, X., and Guizani, M. (2020). Selection of effective ma-chine learning algorithm and bot-iot attacks traffic identification for internet of thingsin smart city.Future Generation Computer Systems, 107:433–442.
Sztrik, J. et al. (2012). Basic queueing theory.University of Debrecen, Faculty of Infor-matics, 193:60–67.
Uslu, B. ç ., Okay, E., and Dursun, E. (2020). Analysis of factors affecting iot-based smarthospital design.Journal of Cloud Computing, 9(1):1–23.
Yassein, M. B., Hmeidi, I., Al-Harbi, M., Mrayan, L., Mardini, W., and Khamayseh,Y. (2019). Iot-based healthcare systems: a survey. InProceedings of the SecondInternational Conf. on Data Science, E-Learning and Information Systems, pages 1–9
Publicado
18/07/2021
Como Citar
SANTOS, Lucas; SANTOS, Brena; SILVA, Francisco Airton.
Internet das Coisas Médicas: Uma Avaliação de Desempenho Focando em Prioridades de Requisições. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 21-30.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2021.16000.
