Adaptive Mobility-Based IoT LoRa Clustering Communication Scheme

Abstract

Long Range (LoRa) as a low-power wide-area technology is distinguished by its robust long-distance communications tailored for Internet of Things (IoT) networks. Because LoRa was primarily designed for stationary devices, when applied to mobile devices, they become susceptible to frequent channel attenuation. Such a condition can result in packet loss, higher energy consumption, and extended transmission times. To address these inherent challenges posed by mobility, we propose an adaptive mobility-based IoT LoRa clustering communication (AMILCC) scheme, which employs the 2D random waypoint mobility model, strategically partitions the network into optimal spreading factor (SF) regions, and incorporates an adaptive clustering approach. The AMILCC scheme is bolstered by a hybrid adaptive data rate (HADR) mechanism categorized into two approaches, namely intra-SF and inter-SF region HADRs, derived from the standard network-based ADR mechanism for stationary devices, to ensure efficient resource allocation for mobile IoT LoRa devices. Evaluation results show that, based on simulations at low mobility speeds of up to 5 m/s, AMILCC successfully maximizes the packet success ratio to the gateway (GW) by over 70%, reduces energy consumption by an average of 55.5%, and minimizes the end-to-end delay by 47.62%, outperforming stationary schemes. Consequently, AMILCC stands as a prime solution for mobile IoT LoRa networks by balancing the high packet success ratio (PSR) with reliability with energy efficiency.