Energy-Efficient IoT Solutions for Smart Cities

The increasing expansion of IoT devices in smart cities has created many energy-optimization options. Despite this, IoT devices generate a lot of sophisticated data, which makes energy efficiency difficult. This study examines energy-efficient IoT technologies for smart cities. This is done with advanced data preparation, feature selection, and classification methods. The density-based spatial clustering of applications with noise(DBSCAN)technique identifies abnormalities, while the imputes missing data to preprocess data. The above aims are achieved using both techniques. Principal Component Analysis (PCA) reduces data dimensionality and selects features. To detect delicate energy consumption trends, Autoencoder-Based Feature Extraction is used. This optimises energy use. Convolutional neural networks (CNNs) are used during categorisation to increase IoT system performance and predict energy usage. The technique aims to improve smart city energy efficiency. This goal will be achieved by managing enormous amounts of IoT data, choosing key attributes, and using advanced machine learning algorithms to make accurate estimates.