Improving the Scalability of LoRa Networks Through Dynamical Parameter Set Selection

  • Qingsong Cai
  • Jia LinEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1101)


LoRa technology has emerged as an interesting solution for Low Power Wide Area applications. To support a massive amount of devices in large-scale networks, it is necessary to design an appropriate parameter allocation scheme for device. LoRa devices provide high flexibility in choosing settings of communication parameters (including spreading factors, bandwidth, coding rate, transmission power, etc), which results in there are over 6000 settings for choosing. However, the existing methods mainly focus on the same parameter setting for network deployment. To this aim, the impact of different parameter selections on communication performance is analyzed first. Then, channel collision and link budget model are established and implemented in the NS3 simulator. A dynamic parameter selection method based on orthogonal genetic algorithm (OGA) is introduced to solve the model, ultimately according to link budget, each device selects its parameter setting, which minimized collision probability. Finally, simulation results show that the OGA algorithm proposed in this paper can improve the packet delivery rate by 30%. Knowing different packet sizes have an impact on network performance, the experiment also evaluated the impact of different packet sizes on network transmission reliability under different parameter setting methods, the introduced OGA has significantly improved adaptability and scalability of the network in the case of high payloads.


Internet of things LoRa Low power wide-area network Orthogonal genetic algorithm Parameter combination 


  1. 1.
    Da Xu, L., He, W., Li, S.: Internet of things in industries: a survey. IEEE Trans. Ind. Inform. 10(4), 2233–2243 (2014)CrossRefGoogle Scholar
  2. 2.
    Gubbi, J., Buyya, R., Marusic, S., et al.: Internet of Things (IoT): a vision, architectural elements and future directions. Future Gen. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  3. 3.
    Augustin, A., Yi, J., Clausen, T., et al.: A study of LoRa: long range and low power networks for the internet of things. Sensors 16(9), 1466–1475 (2016)CrossRefGoogle Scholar
  4. 4.
    Wang, H., Fapojuwo, A.O.: A survey of enabling technologies of low power and long range machine-to-machine communications. IEEE Commun. Surv. Tutorials 19(4), 2621–2639 (2017)CrossRefGoogle Scholar
  5. 5.
    Tome, M., Nardelli, P., Alves, H., et al.: Long range low power wireless networks and sampling strategies in electricity metering. IEEE Trans. Ind. Electron. 66(2), 1629–1637 (2019)CrossRefGoogle Scholar
  6. 6.
    Wu, F., Wu, T., Yuce, M.: An internet-of-things (IoT) network system for connected safety and health monitoring applications. Sensors 19(1), 1–21 (2019)CrossRefGoogle Scholar
  7. 7.
    Kim, S., Yoo, Y.: Contention-aware adaptive data rate for throughput optimization in LoRaWAN. Sensors 18(6), 1716–1732 (2018)CrossRefGoogle Scholar
  8. 8.
    Cuomo, F., Campo, M., Caponi, A., et al.: EXPLoRa: extending the performance of LoRa by suitable spreading factor allocations. In: Wireless and Mobile Computing, Networking and Communications (WiMob), March 2017, pp. 1–8 (2017)Google Scholar
  9. 9.
    Alonso, J.M., Alonso, J.M., Alonso, J.M., et al.: Do LoRa low-power wide-area networks scale? In: ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, March 2016, pp. 59–67 (2016)Google Scholar
  10. 10.
    LoRa Alliance.LoRa. [OL], 25 June 2018.
  11. 11.
    Peng, Y., Shangguan, L., Hu, Y., et al.: PLoRa: a passive long-range data network from ambient LoRa transmissions. In: Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, March 2018, pp. 147–160. ACM (2018)Google Scholar
  12. 12.
    Voigt, T., Bor, M., Roedig, U., et al.: Mitigating inter-network interference in lora networks. arXiv:1611.00688 (2016)
  13. 13.
    Cuomo, F., Gamez, J.C.C., Maurizio, A., et al.: Towards traffic-oriented spreading factor allocations in LoRaWAN systems. In: 2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), March 2018, pp. 1–8 (2018)Google Scholar
  14. 14.
    Bor, M., Roedig, U.: LoRa transmission parameter selection. In: International Conference on Distributed Computing in Sensor Systems (DCOSS), March 2017, pp. 27–34 (2017)Google Scholar
  15. 15.
    Adelantado, F., Vilajosana, X., Tuset-Peiro, P., et al.: Understanding the limits of LoRaWAN. IEEE Commun. Mag. 55(9), 34–40 (2017)CrossRefGoogle Scholar
  16. 16.
    Bouguera, T., Diouris, J.F., Chaillout, J., et al.: Energy consumption model for sensor nodes based on LoRa and LoRaWAN. Sensors 18(7), 2104–2127 (2018)CrossRefGoogle Scholar
  17. 17.
    Mikhaylov, K., Petajajarvi, J., Janhunen, J.: On LoRaWAN scalability: empirical evaluation of susceptibility to inter-network interference. In: 2017 European Conference on Networks and Communications (EuCNC), March 2017, pp. 1–6. IEEE (2017)Google Scholar
  18. 18.
    Petajajarvi, J., Mikhaylov, K., Pettissalo, M., et al.: Performance of a low-power wide-area network based on LoRa technology: doppler robustness, scalability, and coverage. Int. J. Distrib. Sens. Netw. 13(3), 1–16 (2017)CrossRefGoogle Scholar
  19. 19.
    Slabicki, M., Premsankar, G., Di Francesco, M.: Adaptive configuration of LoRa networks for dense IoT deployments. In: 16th IEEE/IFIP Network Operations and Management Symposium (NOMS 2018), March 2018, pp. 1–9 (2018)Google Scholar
  20. 20.
    Van den Abeele, F., Haxhibeqiri, J., Moerman, I., et al.: Scalability analysis of large-scale LoRaWAN networks in ns-3. IEEE Internet Things J. 4(6), 2186–2198 (2017)CrossRefGoogle Scholar
  21. 21.
    Cattani, M., Boano, C., Romer, K.: An experimental evaluation of the reliability of lora long-range low-power wireless communication. J. Sens. Actuator Netw. 6(2), 1–7 (2017)Google Scholar
  22. 22.
    Noreen, U., Bounceur, A., Clavier, L.: A study of LoRa low power and wide area network technology. In: 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), March 2017, pp. 1–6 (2017)Google Scholar
  23. 23.
    Yong, W., Hui, L., Zixing, C.: An orthogonal design based constrained optimization evolutionary algorithm. J. Eng. Optim. 39(6), 715–736 (2007)CrossRefGoogle Scholar
  24. 24.
    Sanodiya, R.K., Saha, S., Mathew, J.: A kernel semi-supervised distance metric learning with relative distance: integration with a MOO approach. Expert Syst. Appl. 125(7), 233–248 (2019) CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer and Information EngineeringBeijing Technology and Business UniversityBeijingChina

Personalised recommendations