Abstract
Incorporating the Internet of Things (IoT) systems into Smart environments can reinforce stability and sustainability. IoT-deployed smart environment monitoring devices supports energy monitoring, water consumption, and other resource utilization details. Several earlier research works have focused on energy, water consumption, or other sustainable parameter monitoring that cannot retain and manage sustainability. This paper aims to maintain and manage the sustainability of smart environments by creating a Deep Learning Framework (DLF) to analyze the multimodality data generated by IoT devices. The DLF integrates and incorporates an IoT dashboard, IoT network, Deep and Reinforcement Learning algorithms, Optimization algorithm, and Smart Regulator. The proposed DLF monitors multiple smart environments using IoT devices that generate multi-modality data. The IoT devices are clustered as networks that monitor different smart environments, generate data, and send it to the cloud data centers. Before that, the Particle Swarm Optimization algorithm is used to preprocess and optimize the data. Based on the data modality, deep learning algorithms are activated automatically to process and predict the conditions of the smart environment concerning abnormalities and utility consumptions. Smart metering, building, fleet, and air quality monitoring and predictions are some of the additional solutions given by the DLF. It incorporates cloud and IoT dashboards to improve the overall monitoring performance and reduce cost efficiency. The performance of the DLF is verified by simulating and comparing its output with the other state-of-the-art methods, and it is found that the proposed DLF outperforms the others.
Keywords: Deep Learning Algorithms, IoT Data Processing, IoT-Monitoring, Reinforcement Learning, Smart Environment, Sustainable Development.