Improved Distance Estimation with BLE Beacon Using Kalman Filter and SVM

Lately, Bluetooth Low Energy (BLE) beacon has attracted a lot of interests for its capabilities in enhancing the interaction between smart things in the Internet of Things (IoT) ecosystem via proximity approach. Even though Proximity sensing is capable of delivering a correct interaction, it might have a problem for explicit interaction when exact distance estimation is required. Considering those interactive applications which are distance-dependent, this paper proposed an optimized support vector machine (O-SVM) on the cloud for distance estimation and a Kalman filter (KF) on the edge to obtain a near true RSS value from a list of RSS measurements. Four benchmark functions (i.e., two from Industries and two Machine Learning Techniques) have been used for performance evaluation. Simulation with real signal samples was conducted to verify the performance of our proposed algorithm. Besides examining the performance gain of our proposed solution over the four benchmark functions, we also implemented the proposed solution on a smartphone for practical testing to demonstrate its feasibility. The proposed solution not only outperforms the rest with significant performance gain, i.e., > 50% error reduction compared to the benchmark functions. Furthermore, practical implementation verified that our proposed approach is able to return the estimate distance in less than 1s, such real-time response is desirable for many delay- sensitive applications.