Viabilização de Alocação de Recursos em LoRaWAN
Palavras-chave:
IoT, LoRaWAN, MILP, Resource AllocationResumo
O LoRaWAN é uma tecnologia sem fio de longo alcance promissora em aplicações de Internet das Coisas (IoT). Esta tecnologia trabalha com alta densidade sendo capaz de conectar dispositivos que requerem comunicação de longo alcance, baixo custo e menor consumo de energia. Contudo, a densificação do uso de LoRaWAN em serviços IoT traz uma série de desafios devido a interferência por transmissão simultânea no mesmo canal. Nesse contexto, este artigo apresenta uma heurística e um modelo ótimo para alocação de recursos adaptativos no LoRaWAN para aplicações IoT. Os resultados obtidos por meio de simulações mostraram que a heurística CORRECT fornece resultados próximos ao ótimo obtido pelo modelo MARCO para uso do canal.
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Referências
(2019). Adaptive Data Rate. https://www.thethingsnetwork.org/docs/lorawan/adaptive-data-rate.html.
Caillouet, C., Heusse, M., and Rousseau, F. (2019). Optimal SF Allocation in LoRaWAN Considering Physical Capture and Imperfect Orthogonality. In Global Communications Conference (GLOBECOM), Waikoloa, United States.
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 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 1–8. IEEE.
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.
El-Aasser, M., Elshabrawy, T., and Ashour, M. (2018). Joint spreading factor and coding rate assignment in lorawan networks. In Global Conference on Internet of Things (GCIoT), pages 1–7. IEEE.
Harinda, E., Hosseinzadeh, S., Larijani, H., and Gibson, R. M. (2019). Comparative performance analysis of empirical propagation models for lorawan 868mhz in an urban scenario. In 5th World Forum on Internet of Things (WF-IoT), pages 154–159. IEEE.
Kufakunesu, R., Hancke, G. P., and Abu-Mahfouz, A. M. (2020). A survey on adaptive data rate optimization in lorawan: Recent solutions and major challenges. Sensors, 20(18):5044.
Matni, N., Moraes, J., Pacheco, L., Rosario, D., MayOliveira, H., Cerqueira, E., and Neto, A. J. V. Experimenting Long Range Wide Area Network in an e-Health Environment: Discussion and Future Directions. In roceedings of the 16th International Wireless Communications Mobile Computing Conference (IWCMC 2020).
Moraes, J., Matni, N., Riker, A., Oliveira, H., Cerqueira, E., Both, C., and Rosario, D. ´(2020). An Efficient Heuristic LoRaWAN Adaptive Resource Allocation for IoT Applications. In 25th IEEE Symposium on Computers and Communications (ISCC), pages 1–6. IEEE.
Sandoval, R. M., Garcia-Sanchez, A.-J., and Garcia-Haro, J. (2019). Optimizing and updating lora communication parameters: A machine learning approach. IEEE Transactions on Network and Service Management, 16(3):884–895.
Yousuf, A. M. et al. (2018). Throughput, coverage and scalability of LoRa LPWAN for internet of things. In IEEE/ACM 26th International Symposium on Quality of Service, pages 1–10.
