Indoor location detection with a RSS-based short term memory technique (KNN-STM)


The interaction between devices and users has changed dramatically with the advances in mobile technologies. User friendly devices and services are offered by utilizing smart sensing capabilities and using context, location and motion sensor data. However, indoor location sensing is mostly achieved by measuring radio signal (WiFi, Bluetooth, GSM etc.) strength and nearest neighbor identification. The algorithm that is most commonly used for Received Signal Strength (RSS) based location detection is the K Nearest Neighbor (KNN). KNN algorithm identifies an estimate location using the K nearest neighboring points. Accordingly, in this paper, we aim to improve the KNN algorithm by integrating a short term memory (STM) where past signal strength readings are stored. Considering the limited movement capabilities of a mobile user in an indoor environment, user's previous locations can be taken into consideration to derive his/her current position. Hence, in the proposed approach, the signal strength readings are refined with the historical data prior to comparison with the environment's radio map. Our evaluation results indicate that the performance of enhanced KNN outperforms KNN algorithm.

2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)