RSSI-Based Localization with Signal Propagation Modeling

In the development of contemporary IoT technology, RSSI-based localization is gaining increasing attention. This technique calculates path loss by combining known transmit signal strength from beacon nodes with received signal strength at receiver nodes. The system typically implements distance conversion through signal propagation models like Log-Distance Path Loss or Friis free-space equation, often incorporating polynomial regression or machine learning algorithms for model optimization. To achieve higher localization precision, multiple signal sampling and processing techniques can be implemented - including moving average filters for RSSI stabilization and outlier rejection algorithms for noise reduction. The final RSSI-based localization system effectively computes node positions through coordinate calculation algorithms (e.g., least squares estimation for trilateration), playing crucial roles in practical applications such as indoor navigation, asset tracking, and smart facility management with typical accuracy ranges of 2-5 meters in controlled environments.