Estimação de descarga de dispositivo IoT usando deep learning com otimização NSGA-II

Abstract

The increasing adoption of IoT (Internet of Things) network applications highlights the need to optimize energy management in these systems, because energy efficiency is crucial for the adaptability of IoT implementations. This study analyzes the discharge curves of a rechargeable battery in an IoT network context utilizing LoRa (Long Range) communication and various sensors, with the objective of generating multiple discharge curves to estimate the battery behavior in this scenario. These curves were used to train a Multilayer Artificial Neural Network (ANN), implementing Deep Learning techniques, where the ANN architecture was outlined using the NSGA-II (Non-dominated Sorting Genetic Algorithm II) Multi-objective Optimization algorithm. This resulted in models capable of estimating the battery discharge time by analyzing a segment of the discharge process observed by the model with a mean squared error of approximately two minutes for the most efficient model found. This result represents a very positive margin, considering that the duration of the discharge tests extends to approximately seventy-one hours and the data collection sampling rate is one minute.