Deep Learning‐Based Passive Localization System Using Radio Tomography Images and Wi‐Fi RSSI

Passive localization is an important technology within Internet of Things (IoT) applications that allows for tracking and monitoring of people without needing active transmitters. This feature proves useful in privacy‐conscious areas like security and healthcare, where active tracking or wearable devices are not practicable. Traditional radio tomographic imaging (RTI) methods suffer limitations due to vulnerability to environmental noise and rough spatial resolution. To overcome these issues, we suggest a new framework that combines a wireless mesh of 14 ESP32‐transceiver nodes, multipath‐sensitive RTI modeling, and a deep convolutional neural network (CNN) for accurate localization. The system records bidirectional received signal strength indicator (RSSI) measures to produce RTIs, which are then optimized by CNN to provide submeter accuracy. Experimental tests illustrate a precision of 92.81%, outperforming traditional RTI methods and other machine learning baselines. The suggested solution successfully alleviates line‐of‐sight (LOS) and nonline‐of‐sight (NLOS) problems, ensuring scalability and cost‐effectiveness for real‐world IoT applications.