RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Widespread use of the Internet of Things (IoT) is being made in a number of application areas to save costs and improve performance. These types of applications also include services that are dependent on localization. When a sensor node is localized, an IoT network may use that information to identify it. A Received Signal Strength Indicator (RSSI), which gauges the intensity of the signal received from the sensor nodes, is typically used in conjunction with supervised Machine Learning (ML) algorithms in localization methods. In this study, a unique indoor and outdoor localization and navigation technique is presented that makes use of LoRA nodes and a highly accurate machine learning model. The suggested method has cutting-edge features and shows considerable gains in accuracy and dependability. These discoveries offer potential answers for practical applications and boost localization technology.