Redes de sensores IoT em edifícios inteligentes: uma avaliação de desempenho usando modelos de filas
Resumo
Os edifícios das grandes cidades estão cada vez mais inteligentes com a adoção de dispositivos de IoT. Os sensores e atuadores IoT devem ser eficientes pois o mau funcionamento pode causar danos patrimoniais e talvez perda de vidas. Para avaliar o desempenho de sistemas de monitoramento de edifícios, alguns estudos usaram modelos analíticos. No entanto, alguns pontos ainda não foram explorados na literatura, tais como a análise da capacidade de recursos computacionais adequados à demanda de dados, representação do número de núcleos por máquina e o agrupamento de sensores por localização. Este trabalho propõe um modelo de filas para avaliar o desempenho de uma infraestrutura de edifício inteligente com suporte de múltiplas camadas de processamento.
Palavras-chave:
IoT, modelo de filas, edifício inteligente
Referências
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Nabih, A. K., Gomaa, M. M., Osman, H. S., and Aly, G. M. (2011). Modeling, simulation, and control of smart homes using petri nets. International Journal of Smart Home,5(3):1–14.
Novák, M., Binas, M., and Jakab, F. (2012). Unobtrusive anomaly detection in presence of elderly in a smart-home environment. In 2012 ELEKTRO, pages 341–344.
Oueida, S., Kotb, Y., Aloqaily, M., Jararweh, Y., and Baker, T. (2018). An edge computing based smart healthcare framework for resource management. Sensors, 18(12):4307.
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Said, O. and Masud, M. (2013). Towards internet of things: Survey and future vision. International Journal of Computer Networks, 5(1):1–17.
Sokullu, R., Akkas ̧, M. A., and Demir, E. (2020). Iot supported smart home for the elderly. Internet of Things, 11:100239.
Wang, B. C., Sechilariu, M., and Locment, F. (2013). Power flow petri net modelling for building integrated multi-source power system with smart grid interaction. Mathematics and Computers in Simulation, 91:119–133.
Wu, T., Zhou, P., Liu, K., Yuan, Y., Wang, X., Huang, H., and Wu, D. O. (2020). Multiagent deep reinforcement learning for urban traffic light control in vehicular networks. IEEE Transactions on Vehicular Technology, 69(8):8243–8256.
Xu, H., He, Y., Sun, X., He, J., and Xu, Q. (2020). Prediction of thermal energy insidesmart homes using iot and classifier ensemble techniques. Computer Communications,151:581–589.
Arbib, C., Arcelli, D., Dugdale, J., Moghaddam, M., and Muccini, H. (2019). Real-time emergency response through performant iot architectures. InInternational Conferenceon Information Systems for Crisis Response and Management (ISCRAM).
Attia, M. B., Nguyen, K.-K., and Cheriet, M. (2019). Dynamic qoe/qos-aware queuing for heterogeneous traffic in smart home.IEEE Access, 7:58990–59001.
Fanti, M. P., Mangini, A. M., and Roccotelli, M. (2014a). A petri net model for a building energy management system based on a demand response approach. In 22nd Mediterranean Conference on Control and Automation, pages 816–821.
Fanti, M. P., Mangini, A. M., and Roccotelli, M. (2018). A simulation and control model for building energy management.Control Engineering Practice, 72:192–205.
Fanti, M. P., Mangini, A. M., Ukovic, W., Lesage, J., and Viard, K. (2014b). A petri net model of an integrated system for the health care at home management. In 2014 IEEE International Conference on Automation Science and Engineering (CASE), pages 582–587.
Fishman, G. S. (2013).Discrete-event simulation: modeling, programming, and analysis. Springer Science & Business Media.
Garcia, M., Konios, A., and Nugent, C. (2018). Modelling activities of daily living with petri nets. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 866–871.
Jafarnejad Ghomi, E., Rahmani, A. M., and Qader, N. N. (2019). Applying queue theory for modeling of cloud computing: A systematic review. Concurrency and Computation: Practice and Experience, 31(17):e5186.
Memon, R. A., Li, J. P., and Ahmed, J. (2019). Simulation model for blockchain systems using queuing theory.Electronics, 8(2):234.
Mena, D. M., Papapanagiotou, I., and Yang, B. (2018). Internet of things: Survey on security. Information Security Journal: A Global Perspective, 27(3):162–182.
Nabih, A. K., Gomaa, M. M., Osman, H. S., and Aly, G. M. (2011). Modeling, simulation, and control of smart homes using petri nets. International Journal of Smart Home,5(3):1–14.
Novák, M., Binas, M., and Jakab, F. (2012). Unobtrusive anomaly detection in presence of elderly in a smart-home environment. In 2012 ELEKTRO, pages 341–344.
Oueida, S., Kotb, Y., Aloqaily, M., Jararweh, Y., and Baker, T. (2018). An edge computing based smart healthcare framework for resource management. Sensors, 18(12):4307.
Oueida, S., Kotb, Y., Aloqaily, M., Jararweh, Y., and Baker, T. (2018). An edge computing based smart healthcare framework for resource management. Sensors, 18(12):4307.
Said, O. and Masud, M. (2013). Towards internet of things: Survey and future vision. International Journal of Computer Networks, 5(1):1–17.
Sokullu, R., Akkas ̧, M. A., and Demir, E. (2020). Iot supported smart home for the elderly. Internet of Things, 11:100239.
Wang, B. C., Sechilariu, M., and Locment, F. (2013). Power flow petri net modelling for building integrated multi-source power system with smart grid interaction. Mathematics and Computers in Simulation, 91:119–133.
Wu, T., Zhou, P., Liu, K., Yuan, Y., Wang, X., Huang, H., and Wu, D. O. (2020). Multiagent deep reinforcement learning for urban traffic light control in vehicular networks. IEEE Transactions on Vehicular Technology, 69(8):8243–8256.
Xu, H., He, Y., Sun, X., He, J., and Xu, Q. (2020). Prediction of thermal energy insidesmart homes using iot and classifier ensemble techniques. Computer Communications,151:581–589.
Publicado
18/07/2021
Como Citar
SANTOS, Brena; SILVA, Francisco Airton; SOARES, André.
Redes de sensores IoT em edifícios inteligentes: uma avaliação de desempenho usando modelos de filas. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 20. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 25-36.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2021.15720.
