Spreading Factor Allocation for LoRa Nodes Progressively Joining a Multi-Gateway Adaptive Network

Abstract

In this paper, we investigate how to provide good transmission quality in massive deployments of LoRa networks by considering all parameters such as device heterogeneity, network topology, and deployment density. We consider the scenario with nodes progressively joining the network, i.e., new nodes joining the network are configured based on measured metrics and without modifying the configuration of nodes that already joined the network. Based on this assumption, we propose an algorithm to improve network performance by effectively allocating a spreading factor (SF) to end-devices in realistic multi-gateway deployments. The algorithm performs better than the Adaptive Data Rate (ADR) of LoRaWAN (e.g., it almost doubles the packet delivery ratio (PDR) in scenarios with 10k nodes) and enhances LoRa deployments by adapting the communication parameters of end-devices according to the network size and estimated metrics. The allocation decision is based on different metrics: link PDR, network PDR, and network distribution of SF per gateway. Nodes can easily derive the estimated metrics from gateway measurements.