Mecanismo de Alocação de Recursos para LoRaWAN Ciente da Prioridade das Aplicações de IoT
Resumo
O aumento no numero de dispositivos LoRaWAN conectados, tem aumentado as colisões e interferências na rede. Neste sentido, surge a necessidade de um mecanismo para gerenciar o fator de propagação usado pelos dispositivos durante as transmissões. No entanto, os mecanismos existentes não tratam a prioridade das aplicações, ocasionando em dispositivos com funções importantes tenham o mesmo tratamento que dispositivos com funções menos importantes ,trazendo menos segurança do ponto de vista da aplicação. Este artigo propõe o PRA, um mecanismo de alocação de fator de propagação de acordo com o nível de importância da aplicação, priorizando aplicações com alta e media prioridade. Simulações mostram que o PRA reduz em até 85% e 80% o tempo no ar e o consumo de energia para grupos de dispositivos de alta e media prioridade, respectivamente, se comparado a mecanismos do estado-da-arte.
Palavras-chave:
Internet das coisas, Redes de sensores
Referências
Adelantado, F., Vilajosana, X., Tuset-Peiro, P., Martinez, B., Melia-Segui, J., and Watteyne, T. (2017). Understanding the limits of lorawan. IEEE Communications magazine, 55(9):34–40.
Alliance, L. (2017). Lorawantm 1.1 regional parameters. LoRa Alliance.
Bor, M. C., Roedig, U., Voigt, T., and Alonso, J. M. (2016). Do lora low-power wide-area networks scale? In 19th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 59–67. ACM.
Bouguera, T., Diouris, J.-F., Chaillout, J.-J., Jaouadi, R., and Andrieux, G. (2018). Energy consumption model for sensor nodes based on lora and lorawan. Sensors, 18(7):2104.
Caillouet, C., Heusse, M., and Rousseau, F. (2019). Optimal SF Allocation in LoRaWAN Considering Physical Capture and Imperfect Orthogonality. In IEEE Global Communications Conference (GLOBECOM), Waikoloa, USA.
Cuomo, F., Campo, M., Caponi, A., Bianchi, G., Rossini, G., and Pisani, P. (2017). Explora: Extending the performance of lora by suitable spreading factor allocations. In 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 1–8. IEEE.
Dao, N.-N., Vu, D.-N., Na, W., Kim, J., and Cho, S. (2018). Sgco: Stabilized green crosshaul orchestration for dense iot offloading services. IEEE Journal on Selected Areas in Communications, 36(11):2538–2548.
Dawaliby, S., Bradai, A., and Pousset, Y. (2019). Network slicing optimization in large scale lora wide area networks. In Proceedings of the IEEE Conference on Network Softwarization (NetSoft), pages 72–77. IEEE.
Duda, A. and Heusse, M. (2019). Spatial issues in modeling lorawan capacity. In 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 191–198.
Firouzi, F., Farahani, B., Weinberger, M., DePace, G., and Aliee, F. S. (2020). Iot fundamentals: Definitions, architectures, challenges, and promises. In Intelligent Internet of Things, pages 3–50. Springer.
Kavitha, K. and Suseendran, G. (2019). Priority based adaptive scheduling algorithm for iot sensor systems. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pages 361–366. IEEE.
Matni, N., Moraes, J., Rosário, D., Cerqueira, E., and Neto, A. (2019). Optimal Gateway Placement Based on Fuzzy C-Means for Low Power Wide Area Networks. In IEEE Latin-American Conference on Communications (LATINCOM), pages 1–7. IEEE.
Newman, D. (2019). Return on IoT: Dealing with the IoT skills ga.
https://www.forbes.com/sites/danielnewman/2019/07/30/ return-on-iot-dealing-with-the-iot-skills-gap/#73f55d747091. Accessed: 2020-03-20.
Qadir, Q. M., Rashid, T. A., Al-Salihi, N. K., Ismael, B., Kist, A. A., and Zhang, Z. (2018). Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs. IEEE Access, 6:77454–77473.
Sallum, E., Pereira, N., Alves, M., and Santos, M. (2019). Improving qos in lora low-power wide-area networks through optimized radio resource management. Preprints.
Semtech, S. (2015). Datasheet (2015). https://www.semtech.com/products/ wireless-rf/lora-transceivers/sx1272. Accessed: 2020-03-20.
Sinha, R. S., Wei, Y., and Hwang, S.-H. (2017). A survey on lpwa technology: Lora and nb-iot. Ict Express, 3(1):14–21.
Voigt, T. and Bor, M. (última atualização:10-07-2017). LoRaSim a discrete-event simulator based on simpy. https://www.lancaster.ac.uk/scc/sites/lora/lorasim.html, acessado em: 26-11-2019.
Alliance, L. (2017). Lorawantm 1.1 regional parameters. LoRa Alliance.
Bor, M. C., Roedig, U., Voigt, T., and Alonso, J. M. (2016). Do lora low-power wide-area networks scale? In 19th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 59–67. ACM.
Bouguera, T., Diouris, J.-F., Chaillout, J.-J., Jaouadi, R., and Andrieux, G. (2018). Energy consumption model for sensor nodes based on lora and lorawan. Sensors, 18(7):2104.
Caillouet, C., Heusse, M., and Rousseau, F. (2019). Optimal SF Allocation in LoRaWAN Considering Physical Capture and Imperfect Orthogonality. In IEEE Global Communications Conference (GLOBECOM), Waikoloa, USA.
Cuomo, F., Campo, M., Caponi, A., Bianchi, G., Rossini, G., and Pisani, P. (2017). Explora: Extending the performance of lora by suitable spreading factor allocations. In 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 1–8. IEEE.
Dao, N.-N., Vu, D.-N., Na, W., Kim, J., and Cho, S. (2018). Sgco: Stabilized green crosshaul orchestration for dense iot offloading services. IEEE Journal on Selected Areas in Communications, 36(11):2538–2548.
Dawaliby, S., Bradai, A., and Pousset, Y. (2019). Network slicing optimization in large scale lora wide area networks. In Proceedings of the IEEE Conference on Network Softwarization (NetSoft), pages 72–77. IEEE.
Duda, A. and Heusse, M. (2019). Spatial issues in modeling lorawan capacity. In 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 191–198.
Firouzi, F., Farahani, B., Weinberger, M., DePace, G., and Aliee, F. S. (2020). Iot fundamentals: Definitions, architectures, challenges, and promises. In Intelligent Internet of Things, pages 3–50. Springer.
Kavitha, K. and Suseendran, G. (2019). Priority based adaptive scheduling algorithm for iot sensor systems. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM), pages 361–366. IEEE.
Matni, N., Moraes, J., Rosário, D., Cerqueira, E., and Neto, A. (2019). Optimal Gateway Placement Based on Fuzzy C-Means for Low Power Wide Area Networks. In IEEE Latin-American Conference on Communications (LATINCOM), pages 1–7. IEEE.
Newman, D. (2019). Return on IoT: Dealing with the IoT skills ga.
https://www.forbes.com/sites/danielnewman/2019/07/30/ return-on-iot-dealing-with-the-iot-skills-gap/#73f55d747091. Accessed: 2020-03-20.
Qadir, Q. M., Rashid, T. A., Al-Salihi, N. K., Ismael, B., Kist, A. A., and Zhang, Z. (2018). Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs. IEEE Access, 6:77454–77473.
Sallum, E., Pereira, N., Alves, M., and Santos, M. (2019). Improving qos in lora low-power wide-area networks through optimized radio resource management. Preprints.
Semtech, S. (2015). Datasheet (2015). https://www.semtech.com/products/ wireless-rf/lora-transceivers/sx1272. Accessed: 2020-03-20.
Sinha, R. S., Wei, Y., and Hwang, S.-H. (2017). A survey on lpwa technology: Lora and nb-iot. Ict Express, 3(1):14–21.
Voigt, T. and Bor, M. (última atualização:10-07-2017). LoRaSim a discrete-event simulator based on simpy. https://www.lancaster.ac.uk/scc/sites/lora/lorasim.html, acessado em: 26-11-2019.
Publicado
30/06/2020
Como Citar
LIMA, Eduardo; MATNI, Nagib; MORAES, Jean; OLIVEIRA, Helder; ROSÁRIO, Denis; CERQUEIRA, Eduardo.
Mecanismo de Alocação de Recursos para LoRaWAN Ciente da Prioridade das Aplicações de IoT. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1-10.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11206.
