RSSI Fingerprinting

RSSI fingerprinting uses the received signal strength of wireless signals (Wi-Fi, Bluetooth, LoRa) to estimate a device's location within an environment. Current research focuses on improving accuracy and scalability using deep learning models, including convolutional and recurrent neural networks, often enhanced by semi-supervised learning techniques or hierarchical training frameworks to handle large, multi-floor, or multi-building datasets. This approach is significant for enabling cost-effective indoor positioning systems in various applications, from smart homes and robotics to asset tracking and emergency response, particularly in areas lacking dedicated infrastructure.