A Dynamic Spreading Factor Adjustment Algorithm for LoRa Sensors Networks

This paper presents a novel dynamic algorithm for a wireless sensor network (WSN) that utilizes Long Range (LoRa) modulation at a frequency of 2.4 GHz. The algorithm aims to optimize the throughput of the WSN by dynamically adjusting the spreading factor (SF) of each sensor node in response to real-time channel conditions and traffic demands. In high-traffic situations, the algorithm employs channel activity detection (CAD) during the preamble period to select the appropriate SF by adjusting the transmitter's SF. This selection process is based on the knowledge of its own SF for each LoRa sensor node, which is determined by its coverage radius from the LoRa master node. The proposed algorithm, termed Dynamic SF Adjustment using Channel Activity Detection (DySFCAD), operates without the need for packet exchanges. It functions in both ascending and descending modes. We evaluate the throughput performance of the LoRa-WSN under various scenarios, taking into account different configurations of the LoRa parameters.