Passive localization based on radio tomography images with CNN model utilizing WIFI RSSI

Passive localization is necessary for Internet of Things (IoT) applications to observe and follow people without requiring them to carry massive equipment. This is crucial in private settings like security and medical monitoring, where individuals are reluctant to wear tracking equipment. Localizing and tracking objects in these spaces are vital since wall loss causes GPS signals to perform poorly in indoor environments. Therefore, passive localization using Radio Tomography Images (RTI) has gained significant importance in present life. Because there are flaws in the RSSI data that models might exploit, previous problems with RTI sparked innovation and resulted in the development of more complex systems, such as a passive localization system that leverages deep learning. This paper employs a set of ESP32 nodes for a mesh network and utilizes a radio frequency sensor network with ESP32 modules to collect RSSI values. We have developed and thoroughly examined the working of radio tomography generation algorithms and present a deep learning approach using a convolutional neural network (CNN) to address the inverse problem. Two CNN models are developed to reconstruct static tomographic images, improve the quality of these images, and localize targeted objects. The targeted object localization accuracy is above 92% by using the proposed system. The results of the proposed system are also compared with previously developed approaches, and it is clearly shown that the proposed system outperforms the previously developed approaches.