Otimização da Taxa de Dados LoRa através de Caracterização Prévia para Transmissão em Cenários de Floresta com Eficiência Energética
Resumo
Com o avanço da Internet das Coisas (IoT) e de tecnologias de transmissão de baixa potência como LoRa (Long Range), emergiu uma gama de soluções para monitoramento ambiental remoto. Nestes cenários, além da seleção adequada dos parâmetros de transmissão LoRa, destaca-se também o desafio de estender a vida útil da bateria dos nós da rede. A estratégia padrão do LoRa consiste no ajuste dinâmico dos parâmetros de transmissão por meio do algoritmo de Taxa de Dados Adaptativa (ADR). Contudo, o ADR, baseado em SNR (Relação sinal-ruído), apresenta limitações em ambientes ruidosos e florestais. Neste trabalho, apresenta-se o LoRaBB, um algoritmo de seleção automática de parâmetros voltado a cenários florestais ruidosos e à redução do consumo energético, que incorpora três métricas do LoRa (RSSI, SNR e PDR) e se fundamenta na caracterização prévia do ambiente de implantação. Experimentos práticos em fragmentos de floresta demonstraram que o LoRaBB convergiu para configurações com redução do Time on Air (ToA) e menor consumo energético. Por fim, simulações conduzidas com 15 e 40 nós, com alcances de 800 a 2200 metros para o gateway, confirmaram melhoria média de 27,6% no PDR do LoRaBB em relação ao ADR e redução de até 54,2% no consumo energético por pacote, além de diminuição de 78,1% nas colisões de pacotes.
Referências
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Cattani, M., Boano, C. A., and Römer, K. (2017). An experimental evaluation of the reliability of lora long-range low-power wireless communication. Journal of Sensor and Actuator Networks, 6(2):7.
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Gao, W., Du, W., Zhao, Z., Min, G., and Singhal, M. (2019). Towards energy-fairness in lora networks. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pages 788–798. IEEE.
Ilahi, I., Usama, M., Farooq, M. O., Umar Janjua, M., and Qadir, J. (2020). Loradrl: Deep reinforcement learning based adaptive phy layer transmission parameters selection for lorawan. In 2020 IEEE 45th Conference on Local Computer Networks (LCN), pages 457–460.
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Khalifeh, A., Shraideh, S., and Darabkh, K. A. (2020). Joint channel and spreading factor selection algorithm for lorawan based networks. In 2020 International Conference on UK-China Emerging Technologies (UCET), pages 1–4.
Li, C. and Cao, Z. (2022). Lora networking techniques for large-scale and long-term iot: A down-to-top survey. ACM Computing Surveys (CSUR), 55(3):1–36.
Marini, R., Mikhaylov, K., Pasolini, G., and Buratti, C. (2022). Low-power wide-area networks: Comparison of lorawan and nb-iot performance. IEEE Internet of Things Journal, 9(21):21051–21063.
Phaiboon, S. and Phokharatkul, P. (2023). An empirical model for 433 mhz lora-wan in ruby mango plantation. In 2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST), pages 25–28.
Polidori, L., Caldeira, C. R. T., Smessaert, M., and Hage, M. E. (2022). Digital elevation modeling through forests: the challenge of the amazon. Acta Amazonica, 52:69–80.
Rašić, T., Simon, J. L. E., Zorić, N., and Simić, M. (2021). The impact of lora transmission parameters on packet delivery and dissipation power. In 2021 International Balkan Conference on Communications and Networking (BalkanCom), pages 11–15. IEEE.
Richardson Ansah, M., Sowah, R. A., Melià-Seguí, J., Katsriku, F. A., Vilajosana, X., and Owusu Banahene, W. (2020). Characterising foliage influence on lorawan pathloss in a tropical vegetative environment. IET Wireless Sensor Systems, 10(5):198–207.
Sabovic, A., Fontaine, J., De Poorter, E., and Famaey, J. (2025). Energy-aware tinyml model selection on zero energy devices. Internet of Things, 30:101488.
Semtech (2015). Lora modulation basics. Acessed december 2023.
Serati, R., Teymuri, B., Anagnostopoulos, N. A., and Rasti, M. (2022). Adr-lite: A low-complexity adaptive data rate scheme for the lora network. In 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (Wi-Mob), pages 296–301.
The Things Network (2023). LoRaWAN Adaptive Data Rate.
Wang, H., Pei, P., Pan, R., Wu, K., Zhang, Y., Xiao, J., and Yang, J. (2022). A collision reduction adaptive data rate algorithm based on the fsvm for a low-cost lora gateway. Mathematics, 10(21):3920.
Yatagan, T. and Oktug, S. (2019). Smart spreading factor assignment for lorawans. In 2019 IEEE Symposium on Computers and Communications (ISCC), pages 1–7.
Angrisani, L., Arpaia, P., Bonavolontà, F., Conti, M., and Liccardo, A. (2017). Lora protocol performance assessment in critical noise conditions. 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), pages 1–5.
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).
Callebaut, G., Ottoy, G., and van der Perre, L. (2019). Cross-layer framework and optimization for efficient use of the energy budget of iot nodes. In 2019 IEEE Wireless Communications and Networking Conference (WCNC), pages 1–6.
Cattani, M., Boano, C. A., and Römer, K. (2017). An experimental evaluation of the reliability of lora long-range low-power wireless communication. Journal of Sensor and Actuator Networks, 6(2):7.
Das, B. and Jain, P. (2017). Real-time water quality monitoring system using internet of things. In 2017 International conference on computer, communications and electronics (Comptelix), pages 78–82. IEEE.
de Jesus, G. G. M., Souza, R. D., Montez, C., and Hoeller, A. (2021). Lorawan adaptive data rate with flexible link margin. IEEE Internet of Things Journal, 8(7):6053–6061.
Filho, M. C. and Campista, M. (2022). Economia de energia através da configuração inteligente de parâmetros de camada física em redes lora. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 559–572, Porto Alegre, RS, Brasil. SBC.
Gao, W., Du, W., Zhao, Z., Min, G., and Singhal, M. (2019). Towards energy-fairness in lora networks. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pages 788–798. IEEE.
Ilahi, I., Usama, M., Farooq, M. O., Umar Janjua, M., and Qadir, J. (2020). Loradrl: Deep reinforcement learning based adaptive phy layer transmission parameters selection for lorawan. In 2020 IEEE 45th Conference on Local Computer Networks (LCN), pages 457–460.
ITU-R (2016). Recommendation p.833-9: Attenuation in vegetation. Recommendation, International Telecommunication Union, Geneva. P Series: Radiowave propagation.
Khalifeh, A., Shraideh, S., and Darabkh, K. A. (2020). Joint channel and spreading factor selection algorithm for lorawan based networks. In 2020 International Conference on UK-China Emerging Technologies (UCET), pages 1–4.
Li, C. and Cao, Z. (2022). Lora networking techniques for large-scale and long-term iot: A down-to-top survey. ACM Computing Surveys (CSUR), 55(3):1–36.
Marini, R., Mikhaylov, K., Pasolini, G., and Buratti, C. (2022). Low-power wide-area networks: Comparison of lorawan and nb-iot performance. IEEE Internet of Things Journal, 9(21):21051–21063.
Phaiboon, S. and Phokharatkul, P. (2023). An empirical model for 433 mhz lora-wan in ruby mango plantation. In 2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST), pages 25–28.
Polidori, L., Caldeira, C. R. T., Smessaert, M., and Hage, M. E. (2022). Digital elevation modeling through forests: the challenge of the amazon. Acta Amazonica, 52:69–80.
Rašić, T., Simon, J. L. E., Zorić, N., and Simić, M. (2021). The impact of lora transmission parameters on packet delivery and dissipation power. In 2021 International Balkan Conference on Communications and Networking (BalkanCom), pages 11–15. IEEE.
Richardson Ansah, M., Sowah, R. A., Melià-Seguí, J., Katsriku, F. A., Vilajosana, X., and Owusu Banahene, W. (2020). Characterising foliage influence on lorawan pathloss in a tropical vegetative environment. IET Wireless Sensor Systems, 10(5):198–207.
Sabovic, A., Fontaine, J., De Poorter, E., and Famaey, J. (2025). Energy-aware tinyml model selection on zero energy devices. Internet of Things, 30:101488.
Semtech (2015). Lora modulation basics. Acessed december 2023.
Serati, R., Teymuri, B., Anagnostopoulos, N. A., and Rasti, M. (2022). Adr-lite: A low-complexity adaptive data rate scheme for the lora network. In 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (Wi-Mob), pages 296–301.
The Things Network (2023). LoRaWAN Adaptive Data Rate.
Wang, H., Pei, P., Pan, R., Wu, K., Zhang, Y., Xiao, J., and Yang, J. (2022). A collision reduction adaptive data rate algorithm based on the fsvm for a low-cost lora gateway. Mathematics, 10(21):3920.
Yatagan, T. and Oktug, S. (2019). Smart spreading factor assignment for lorawans. In 2019 IEEE Symposium on Computers and Communications (ISCC), pages 1–7.
Publicado
19/07/2026
Como Citar
MOREIRA, Diogo Soares; SANTOS, Gilmara; SOUZA, Pedro Barreto de; MELO, Paulo Victor Fernandes de; MOTA, Edjair.
Otimização da Taxa de Dados LoRa através de Caracterização Prévia para Transmissão em Cenários de Floresta com Eficiência Energética. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 18. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 316-327.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2026.23873.
