AI-ERA: Artificial Intelligence-Empowered Resource Allocation for LoRa-Enabled IoT Applications

Adaptive data rate (ADR) is a widely adopted resource assignment approach in long-range wide-area networks (LoRaWANs) for static Internet of Things (IoT) applications such as smart grids and metering. Blind ADR (BADR) has been recommended for mobile IoT applications such as pet and industrial asset tracking. However, ADR and BADR cannot provide appropriate measures to alleviate the massive packet loss problem caused by the unsuitable spreading factors (SFs) assigned to end devices when they are mobile. This article proposes a novel proactive approach—“artificial intelligence-empowered resource allocation” (AI-ERA)—to address the resource assignment issue in static and mobile IoT applications. The AI-ERA approach consists of two modes, namely offline and online modes. First, a deep neural network (DNN) model is trained with a dataset generated at ns-3 in the offline mode. Second, the proposed AI-ERA approach utilizes the pretrained DNN model in the online mode to proactively assign an efficient SF for the end device before each uplink packet transmission. The proactive behavior of the AI-ERA improved the packet success ratio by an average of 32% and 28% in static and mobility scenarios compared with the typical LoRaWAN ADR, respectively.