Data Rate adaptation mechanism for LoRaWAN networks using machine learning
LoRaWAN, ADR, Machine Learning, IoT.
This work aims to investigate Adaptive Data Rate (ADR) mechanisms in LoRaWAN networks as a solution for dynamic IoT scenarios. The standard ADR technique, defined in the LoRaWAN network protocol, is a simple technique that allows the adjustment of the transmission rate by reading the SNR (Signal-to-Noise Ratio) value. Due to the multiplicity and dynamics of IoT scenarios, it is necessary to investigate ADR techniques that establish the compromise between coverage and capacity, especially in time-varying scenarios (emergence of concentrated traffic demand, network with mobile sensors, for example). Preliminary results sing the ns-3 simulator demonstrate the need to dynamically adapt the ADR parameters, as each scenario requires different ADR strategies (or different parameterization of pre-existing strategies). Finally, the execution schedule for completing the work is presented.