Analysis and Implementation for Traffic-Aware Channel Assignment and Contention Scheme in LoRa-Based IoT Networks

Abstract

Internet of Things (IoT) is a promising technology attracting huge attentions in recent years, allowing an excessive number of connections between sensors and devices. Different from conventional human-oriented applications, long range (LoRa) developed in IoT facilitates massive simultaneous sensor data transmissions for the low-power wide-area network (LPWAN), where massive LoRa devices perform packet contention and backoff mechanisms to access opportunity for uplink data transfer. Therefore, it is compellingly imperative to take into account the fluctuation of different channel qualities and various traffic-buffer types for the optimum contention policy, which are not considered in open literatures. In this article, we propose a traffic-aware channel and backoff window size allocation (TCBA) scheme to improve network capacity and latency. Moreover, a statistical latency-aware network model is designed to derive the closed forms of the optimum packet transmission probability and maximum number of LoRa devices supported. The performance results validate that the theoretical analysis approaches the simulated one. Moreover, in both simulated and experimental results, our proposed TCBA scheme is capable of supporting massive LoRa connections achieving the highest throughput and the lowest end-to-end latency compared to other schemes in existing literatures.