Enabling LoRaWAN Resource Allocation

Authors

  • Jean Moraes UFPA
  • Helder Oliveira UFPA
  • Denis Rosário UFPA
  • Eduardo Cerqueira UFPA

Keywords:

IoT, LoRaWAN, MILP, Resource Allocation

Abstract

LoRaWAN is a promising long-range wireless technology in the Internet of Things (IoT) applications. This technology works with high density being able to connect devices that have long-range communication, low cost, and lower energy consumption. However, the densification of the use of LoRaWAN in IoT services poses a series of challenges due to interference by simultaneous transmission on the same channel. In this context, this article presents a heuristic and an optimal model for allocating adaptive resources in LoRaWAN for IoT applications. The results obtained through simulations available that the heuristic CORRECT offers results to the optimum described by the model MARCO for use of the channel.

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References

(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.

Published

2021-06-03

How to Cite

Moraes, J., Oliveira, H., Rosário, D., & Cerqueira, E. (2021). Enabling LoRaWAN Resource Allocation. Electronic Journal of Undergraduate Research on Computing, 19(2). Retrieved from https://journals-sol.sbc.org.br/index.php/reic/article/view/2086

Issue

Section

Special Issue: CTIC/CSBC