An Experimental Comparison of RSSI-Based Indoor Localization Techniques Using ZigBee Technology

Abstract

Wireless indoor localization is a significant challenge because of the noise generated by building structures, electromagnetic fields, and distances between connected nodes inside a building. This study compares two main localization methods: fingerprints (real and synthetic) and ranging schemes based on the Received Signal Strength Indicator (RSSI) of the ZigBee network. We followed four steps for the fingerprinting scheme. First, we obtained real data from the transceivers. Second, we computed the path-loss exponent for each device. Third, we produced a synthetic fingerprint dataset. Finally, we used three localization methods: k-nearest neighbor (KNN), multilayer perceptron (MLP), and long short-term memory recurrent neural network (LSTM-RNN). We assessed the performance of the localization methods by measuring their accuracy, precision, error-to-active area ratio, and installation difficulties. The results show that the real fingerprint scheme has the best performance, but it requires more installation time. While the synthetic fingerprint generation is based on the path-loss method that mixes the advantage of both fingerprint and path-loss. Furthermore, we compared the proposed estimated path-loss with that of a state-of-the-art method and demonstrated that the proposed method exhibits superior performance. These results suggest that the proposed method is more precise and suitable for real datasets.