Improving LoRaWAN Scalability for IoT Applications using Context Information

Abstract

In terms of wireless networks and energy consumption, which is the main basis of Internet of Things (IoT) devices, low-power networks (LPWANs) are considered as a suitable solution for IoT applications. The most important LPWAN protocols are SigFox, NB-IoT(NarrowBand-IoT), and LoRa. LoRa is more popular than others because of industry supports such as LoRa Alliance, IBM, Cisco, etc. The large number of devices that communicate with each other in IoT applications has raised concerns about resources availability and the suitable technologies for managing these resources in LoRaWAN networks. There are many applications in the IoT in which the time to receive information from devices (sensors) can be adjusted based on the context information and the internal state of the system in a way that reduces collisions. Therefore, the time of sending information by sensors can be adjusted to some extent, so that sensors have less collisions during transmissions, and thus the problem of scalability of the system is almost solved. In this research, we have presented a framework to improve the performance of LoRaWAN network, in which the devices are scheduled according to their QoS requirements, the density of the network, and the context information. The performance of the proposed model is evaluated by simulations in which the sensors send packets to the server based on the proposed scheduling. Evaluations show that the proposed method has reduced congestion by 51% and energy consumption by 52% on average compared to the baseline solution. © 2021 IEEE.