Improving the Scalability of LoRa Networks Through Dynamical Parameter Set Selection

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.