Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization
This paper implements a learn rate schedule mechanism and hybrid learn rate schedule mechanism like piecewise, exponential decay, polynomial time, reciprocal time and cosine annealing decay as adaptive learning rate mechanisms for DL models and optimizers like Adadelta, Adam, RMSprop and Stochastic Gradient Descent with Momentum (SGDM) to improve the accuracy of Received Signal Strength Indicator (RSSI)-based localization in LoRaWAN (Long Range Wide Area Networks) based Internet of Things (IoT) networks.
May 1, 2024