Extreme learning machine for indoor location fingerprinting

Abstract

With the rapid growing market of wireless devices, positioning systems that make use of the signal strength of wireless devices are gaining more interest nowadays. Being able to track the location of a Wi-Fi or Radio Frequency Identification device could improve the quality of services in various sectors, including security, warehouse, logistic management, and healthcare. As compared with outdoor environment, positioning systems face a greater challenge in indoor environment because wireless signal is significantly influenced by building layout and surrounding objects, for which a location fingerprinting approach is needed. Moreover, the signal strength of a wireless device may also change over time, which is known as temporal variation, and therefore a reliable location estimation system must have the ability to learn and adapt with temporal changes. However, if the learning process is highly complex and requires long processing time, deploying the system into a larger scale would not be feasible. In recent years, Extreme Learning Machine (ELM) has surfaced as a viable alternative that challenged the norm of iterative and progressive learning. ELM has also been considered as a solution for indoor location fingerprinting. However, there has not been a comprehensive review on how the ELM-based approaches are linked with existing location fingerprinting techniques. Here we discuss some major location fingerprinting techniques, which are nearest-neighbor, LANDMARC, and LEMT, and formulate a new framework for systematically translating the techniques into ELM-based methods.