Enhancing LoRa Network Efficiency: Using Edge Computing for Congestion Mitigation and Latency Reduction

Abstract

LoRa (Long Range) is a low-power, long-range network protocol designed for IoT applications. As the number of devices increases, congestion, packet collisions and latency become major challenges, affecting global network performance. Edge Computing, by processing data closer to the devices, can help reduce congestion and latency, improving network efficiency. This paper explores the impact of the number of devices and variations in the spreading factor (SF) on packet error rate (PER) and latency in a LoRa network, using the Slotted Aloha protocol. It also examines how edge computing can anticipate and attenuate congestion through adaptive transmission strategies, using linear regression to optimize transmission, decrease collisions and reduce latency, thus improving network performance in dense LoRa environments.