Deep Learning-based Data Collection to Reduce Energy Consumption in Internet of Things Devices

The Internet of Things has revolutionized various industries, but the energy consumption of the devices remains a significant challenge. This paper proposes a novel approach to reduce energy consumption using deep learning-based data collection techniques. Our proposed framework leverages deep learning models to forecast sensor data to optimize data trans-mission to adapt to dynamic environmental conditions and reduce unnecessary energy expenditure. We demonstrate the effectiveness of our approach through extensive experiments, showcasing energy savings of up to 93% with respect to the state-of-the-art methods while maintaining desired levels of data accuracy and system performance. This research contributes to the development of more energy-efficient and sustainable Internet of Things systems, paving the way for a greener future.