A Topology-Aware GNN Learning Approach for Energy Optimization in Multi-Hop LoRa Networks

Energy optimization is crucial for extending battery life and reducing maintenance costs in long-range (LoRa) Internet of Things (IoT) networks. Traditional optimization methods usually need excessive computational complexity, limiting the practicability. This drives the recent development of machine learning (ML)-based optimization, such as deep neural networks (DNNs) and reinforcement learning (RL), in wireless local area networks (WLANs). However, compared to WLANs, LoRa owns a more complicated network topology due to the engagement of multi-hop, which is difficult for the existing ML methods to learn. Motivated by this, we propose a topology-aware graph neural network (GNN) learning method, which is specially tailored to tackle the energy optimization problem in multi-hop LoRa networks. By leveraging each node’s topological position to adaptively determine the optimal message-passing depth, the model better integrates the neighborhood information with node feature representations, enhancing the prediction of transmission parameter and overall energy efficiency. Also, a closed-form model of collision probability is derived for the nodes in LoRa, to measure the energy consumption due to retransmissions. Simulation results show that against traditional optimization methods such as game theory, the proposed method can reduce the runtime by five orders of magnitude, with an energy optimization gap below 13%. Compared to existing GNN-based methods, it achieves up to 50% lower energy consumption 20% fewer outage probability, at a similar level of inference time.