FADR-MLP Lite: Compact Neural Adaptive Data Rate for Mobile LoRaWAN - Santos, Fernando J. V.; Silva, Thiago A. R. da; S. Neto, Geraldo A.; Ramos, Isaac S. S.; Santos, Alex F. dos; Sanches, Anderson L.; Reis Jr., Jose Valdemir dos (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2026)
The computational complexity of Adaptive Data Rate (ADR) algorithms in mobile LoRaWAN challenges their execution on resource-constrained devices. This work validates FADR-MLP Lite, a surrogate modeling strategy that compresses a fuzzy controller into a compact neural network. Through cosimulation with a network simulator, technical feasibility was evidenced: a 92% reduction in parameters, a 1.45 KB memory footprint, and only 672 floating-point operations per inference. Under mobility, neural generalization acted as a regularizer, outperforming the expert system that trained the network with a 19% energy gain while sustaining a packet delivery ratio above 60% in high-density scenarios, validating its robustness for low-cost Internet of Things (IoT) devices.