Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization

The learning rate is one of the most crucial hyper-parameters to regulate during the training of the Deep Learning (DL) models and optimizers. Adaptive learning rate algorithms try to automate the time-consuming process of manually setting a suitable learning rate, which is still exhausting. This research uses the learn rate schedule mechanism for training DL models. The learn rate schedule mechanism updates the learning rate for each step or iteration in DL models and optimizers for problem-solving. 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. These techniques aim to automate the process of determining suitable learning rates that dynamically update the learning rate for each step or iteration for optimizers and deep learning models. This technique improves the model’s performance by introducing adaptability into the learning process and departing from conventional set learning rates. The mathematical model of the learning rate schedule is derived and formulated with adaptive deep learning rate models to map with the LoRaWAN RSSI-based localization datasets for accessing the performance parameters. The learn rate schedule for different types of localization datasets is also analyzed. The results were compared for all the learning rate schedule mechanisms with the default parameter settings of DL models, and it gives a better accuracy of 98.98%, which is higher than the existing models.