Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System

Abstract

The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.

Online Spatiotemporal Modeling for Robust and Lightweight Device-Free Localization in Nonstationary Environments

“…In this section, we evaluate the performance of the proposed MC-ELM-based DFL method in typical indoor environments, by comparing with selected state-of-the-art methods, including ELM, kernel ELM (K-ELM), support vector machine (SVM) [24], Kullback-Leibler (KL) divergence [25], 2D-GPR [26], random forest (RF) [27], online sequential ELM (OS-ELM) [23], FOS-ELM [28] and DU-OS-ELM [29].…”