A Percentile Based ADR for Mobile LoRaWAN Applications
Resumo
The article presents a novel solution addressing the limitations of Adaptive Data Rate (ADR) mechanism in LoRaWAN networks, particularly in scenarios characterized by fluctuating channel conditions. By employing percentile-based statistical techniques, the proposed P-ADR optimizes Signal-to-Noise Ratio (SNR) estimation for adjusting transmission parameters, thereby enhancing reliability while preserving energy efficiency. Simulation results revealed superior performance of P-ADR, exhibiting an average Packet Delivery Ratio (PDR) advantage of approximately 5% over ADR+ and around 25% over standard ADR in mobile scenarios. The outcome highlights P-ADR potential as a viable and efficient alternative, improving reliability in LPWAN applications.
Referências
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Finnegan, J., Brown, S., and Farrell, R. (2018). Modeling the energy consumption of LoRaWAN in ns-3 based on real world measurements. In 2018 Global Information Infrastructure and Networking Symposium (GIIS), pages 1–4.
Finnegan, J., Farrell, R., and Brown, S. (2020). Analysis and enhancement of the LoRaWAN Adaptive Data Rate scheme. IEEE Internet of Things Journal, 7(8):7171–7180.
Ivoghlian, A., Wang, K. I.-K., and Salcic, Z. (2022). Application-aware adaptive parameter control for LoRaWAN. Journal of Parallel and Distributed Computing, 166:166–177.
Jiang, Y., Wang, M., and Wang, X. (2023). A efficient Adaptive Data Rate algorithm in LoRaWAN networks: K-ADR. In 2023 24st Asia-Pacific Network Operations and Management Symposium (APNOMS), pages 183–188.
Jouhari, M., Saeed, N., Alouini, M.-S., and Amhoud, E. M. (2023). A survey on scalable LoRaWAN for massive IoT: Recent advances, potentials, and challenges. IEEE Communications Surveys & Tutorials, 25:1841–1876.
Kadusic, E., Ruland, C., Hadzajlic, N., and Zivic, N. (2022). The factors for choosing among NB-IoT, LoRaWAN, and Sigfox radio communication technologies for IoT networking. In 2022 International Conference on Connected Systems & Intelligence (CSI), pages 1–5.
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).
Moraes, J., Oliveira, H., Cerqueira, E., Both, C., Zeadally, S., and Rosário, D. (2022). Evaluation of an adaptive resource allocation for LoRaWAN. Journal of Signal Processing Systems, 94(1):65–79.
Picard, A., Lapayre, J.-C., Hanna, F., and Muthada Pottayya, R. (2021). Comma: A new lorawan communication optimisation mechanism for mobility adaptation of iot. IET Wireless Sensor Systems, 11(3):120–130.
Sadhu, P. K., Yanambaka, V. P., and Abdelgawad, A. (2022). Internet of Things: Security and Solutions Survey. Sensors, 22(19).
Singh, S., Upaddhyay, V. K., and Soni, S. (2023). A literature review: LoRa technology and packet loss analysis in LoRaWAN line-up in our college campus. Intelligent Systems and Smart Infrastructure: Proceedings of ICISSI 2022, page 271.
Slabicki, M., Premsankar, G., and Francesco, M. D. (2018). Adaptive configuration of LoRa networks for dense IoT deployments. pages 1–9. IEEE.
Soy, H. (2023). An adaptive spreading factor allocation scheme for mobile LoRa networks: Blind ADR with distributed TDMA scheduling. Simulation Modelling Practice and Theory, 125:102755.
Alahmadi, H., Bouabdallah, F., Ghaleb, B., and Al-Dubai, A. (2021). Sensitivity-aware configurations for high packet generation rate LoRa networks. In 2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS), pages 240–246.
Amarnath Nandy, A. B. and Ghosh, A. (2022). Robust inference for skewed data in health sciences. Journal of Applied Statistics, 49(8):2093–2123.
Anwar, K., Rahman, T., Zeb, A., Khan, I., Zareei, M., and Vargas-Rosales, C. (2021). Rm-adr: Resource management adaptive data rate for mobile application in lorawan. Sensors, 21(23).
Bonilla, V., Campoverde, B., and Yoo, S. G. (2023). A systematic literature review of LoRaWAN: Sensors and applications. Sensors, 23(20).
Caillouet, C., Heusse, M., and Rousseau, F. (2019). Optimal SF allocation in LoRaWAN considering physical capture and imperfect orthogonality. In 2019 IEEE Global Communications Conference (GLOBECOM), pages 1–6.
Ertürk, M. A., Aydin, M. A., Büyükakkaşlar, M. T., and Evirgen, H. (2019). A Survey on LoRaWAN Architecture, Protocol and Technologies. Future Internet, 11(10).
Farhad, A., Kim, D.-H., Subedi, S., and Pyun, J.-Y. (2020). Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things devices. Sensors, 20(22).
Farhad, A. and Pyun, J.-Y. (2022). HADR: A Hybrid Adaptive Data Rate in LoRaWAN for Internet of Things. ICT Express, 8(2):283–289.
Finnegan, J., Brown, S., and Farrell, R. (2018). Modeling the energy consumption of LoRaWAN in ns-3 based on real world measurements. In 2018 Global Information Infrastructure and Networking Symposium (GIIS), pages 1–4.
Finnegan, J., Farrell, R., and Brown, S. (2020). Analysis and enhancement of the LoRaWAN Adaptive Data Rate scheme. IEEE Internet of Things Journal, 7(8):7171–7180.
Ivoghlian, A., Wang, K. I.-K., and Salcic, Z. (2022). Application-aware adaptive parameter control for LoRaWAN. Journal of Parallel and Distributed Computing, 166:166–177.
Jiang, Y., Wang, M., and Wang, X. (2023). A efficient Adaptive Data Rate algorithm in LoRaWAN networks: K-ADR. In 2023 24st Asia-Pacific Network Operations and Management Symposium (APNOMS), pages 183–188.
Jouhari, M., Saeed, N., Alouini, M.-S., and Amhoud, E. M. (2023). A survey on scalable LoRaWAN for massive IoT: Recent advances, potentials, and challenges. IEEE Communications Surveys & Tutorials, 25:1841–1876.
Kadusic, E., Ruland, C., Hadzajlic, N., and Zivic, N. (2022). The factors for choosing among NB-IoT, LoRaWAN, and Sigfox radio communication technologies for IoT networking. In 2022 International Conference on Connected Systems & Intelligence (CSI), pages 1–5.
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).
Moraes, J., Oliveira, H., Cerqueira, E., Both, C., Zeadally, S., and Rosário, D. (2022). Evaluation of an adaptive resource allocation for LoRaWAN. Journal of Signal Processing Systems, 94(1):65–79.
Picard, A., Lapayre, J.-C., Hanna, F., and Muthada Pottayya, R. (2021). Comma: A new lorawan communication optimisation mechanism for mobility adaptation of iot. IET Wireless Sensor Systems, 11(3):120–130.
Sadhu, P. K., Yanambaka, V. P., and Abdelgawad, A. (2022). Internet of Things: Security and Solutions Survey. Sensors, 22(19).
Singh, S., Upaddhyay, V. K., and Soni, S. (2023). A literature review: LoRa technology and packet loss analysis in LoRaWAN line-up in our college campus. Intelligent Systems and Smart Infrastructure: Proceedings of ICISSI 2022, page 271.
Slabicki, M., Premsankar, G., and Francesco, M. D. (2018). Adaptive configuration of LoRa networks for dense IoT deployments. pages 1–9. IEEE.
Soy, H. (2023). An adaptive spreading factor allocation scheme for mobile LoRa networks: Blind ADR with distributed TDMA scheduling. Simulation Modelling Practice and Theory, 125:102755.
Publicado
20/05/2024
Como Citar
SARMENTO NETO, Geraldo A.; SILVA, Thiago A. R.; ABREU, Pedro F. F.; VELOSO, Arthur F. S.; M., Luis H. O.; R. JUNIOR, José Valdemir.
A Percentile Based ADR for Mobile LoRaWAN Applications. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 43-56.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2024.1248.
