Large-scale WiFi indoor localization via extreme learning machine

Due to the widespread deployment and low cost, WiFi has become one of the most attractive research fields in indoor environments, for example, almost every smart mobile device can be a WiFi signal receiver and transmitter. Various WiFi based indoor localization approaches have been proposed under different circumstances. However, most of them are only suitable for relatively small indoor environments, and when we implement these approaches to large-scale environments, they may encounter the scalability problem due to the huge received signal strength (RSS) database, and also suffer from time-consuming, poor accuracy, etc. In order to tackle these issues, we propose a modified indoor localization scheme for large-scale area by utilizing extreme learning machine (ELM) and K-means clustering. The whole localization process involves two phases, i.e., the offline phase and the online phase. During the offline phase, we firstly divide the monitoring area into some smaller parts by K-means clustering, and then an ELM classifier will be trained for identifying the divided parts, after that ELMs for every divided part will be trained using the collected received signal strength (RSS) values. In the online phase, the attribution of the localization point is firstly judged by the ELM classifier, and then the localization of the target will be estimated through the corresponding ELM. The performance of the proposed approach is verified in both small indoor area, and larger-scale more complicated indoor area. Experimental results show that the proposed scheme can obtain better localization accuracy than existing schemes.