Sustainable Environmental Design Using Green IOT with Hybrid Deep Learning and Building Algorithm for Smart City

Smart cities and urbanization use enormous IoT devices to transfer data for analysis and information processing. These IoT can relate to billions of devices and transfer essential data from their surroundings. There is a massive need for energy because of the tremendous data exchange between billions of gadgets. Green IoT aims to make the environment a better place while lowering the power usage of IoT devices. In this work, a hybrid deep learning method called "Green energy-efficient routing (GEER) with long short-term memory deep Q-Network is used to minimize the energy consumption of devices. Initially, a GEER with Ant Colony Optimization (ACO) and AutoEncoder (AE) provides efficient routing between devices in the network. Next, the long short-term memory deep Q-Network based Reinforcement Learning (RL) method reduces the energy consumption of IoT devices. This hybrid approach leverages the strengths of each technique to address different aspects of energy-efficient routing. ACO and AE contribute to efficient routing decisions, while LSTM DQN optimizes energy consumption, resulting in a well-rounded solution. Finally, the proposed GELSDQN-ACO method is compared with previous methods such as RNN-LSTM, DPC-DBN, and LSTM-DQN. Moreover, we critically analyze the green IoT and perform implementation and evaluation.