D-Trace: Diffusion-Based Fingerprinting for Indoor Tracking in Single-Gateway LoRa Networks

Indoor localization and trajectory tracking in multi-story, energy-constrained IoT environments such as smart healthcare and industrial monitoring remain challenging. This paper investigates how to achieve reliable, fine-grained localization under practical cost and power constraints that necessitate single-gateway LoRa deployments. Existing methods such as Wi-Fi, BLE, and UWB require dense infrastructure or incur high power consumption, while conventional RSSI fingerprinting lacks robustness and cross-domain generalization. Generative methods such as GANs and VAEs typically exhibit training instability and produce oversmoothed fingerprints, which limits their applicability to complex indoor environments. To address these gaps, we present D-Trace, a lightweight trajectory tracking system that employs a conditional diffusion model to generate high-fidelity RSSI fingerprints guided by spatial priors. The system introduces an RSSI + feature representation that enhances discriminability and robustness, reduces manual data collection, and enables cross-domain generalization across floors and LoRa configurations. Extensive experiments in a multi-floor building show that D-Trace achieves 95.94% localization precision with a 0.5 m mean error under sparse deployments, and maintains up to 90.3% precision with a 3.26 m average error in cross-domain scenarios. These results validate the system’s practicality, scalability, and robustness for resource-constrained IoT deployments, providing a cost-effective solution for intelligent indoor tracking.