AdapLoRa: Resource Adaptation for Maximizing Network Lifetime in LoRa networks

Abstract

LoRa has attracted much research attention due to its long communication range and low power consumption on end devices. In LoRa networks, the energy consumption on the end devices can be unfair, because some end devices have to use large spreading factors (leading to long transmission time) or large transmission power to reach a far-away gateway, and their energy consumption can be quite different. As a result, these end devices will run out of their batteries much faster, which may significantly reduce the network lifetime. The existing works have focused on the static resource allocation in LoRa networks to achieve energy fairness. However, due to the dynamic wireless environment, the static allocation can be inefficient in practice. In this paper, we develop AdapLoRa, a lifetime-aware dynamic network resource allocation system, to maximize the network lifetime of LoRa networks. AdapLoRa periodically adapts the resource allocation according to the link quality of end devices. A fine-grained network model is developed to capture the link quality variations and network interference. Finally, by considering the adaptation overhead (e.g., energy consumed by end devices to receive the configuration commands), we propose to gradually improve the network lifetime by periodically estimating network lifetime with different resource allocations. We implement AdapLoRa on a LoRa testbed, and the experimental results reveal that AdapLoRa improves the network lifetime by 23.7% compared with the state-of-the-art works.