L3SFA: Load Shifting Strategy for Spreading Factor Allocation in LoRaWAN Systems

LoRaWAN Enabled networks are expected to have a dizzying growth. Thus, an efficient allocation of wireless resources so as to support a large number of nodes is a major concern. In this paper we propose an SF assignment approach paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the nodes on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations taking into account the capture effect and the non-orthogonality of SFs. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of DER (Date Extraction Rate) while guaranteeing good scalability on the network size and density.