Exploiting the use of machine learning in two different sensor network architectures for indoor localization

Indoor localization has been an active research area for the last two decades. This emerged in the context of providing a mobile robot the capability to conduct navigation tasks in indoor environments. Although the sensing technologies and techniques proposed for indoor robot localization have proven to be reliable solutions, these cannot be adopted as a solution to people or object localization for indoor environments, particularly, due to their high computational cost and power requirements. In order to mitigate these issues, a low-power consumption sensing technology, based on the strength of WiFi signals, is being studied. Nevertheless, a concern when working with these signals is their vulnerability to interference. This paper exploits the use of machine learning is two different architectures for localization and present how a data filtering technique can alleviate interferences. A step into a fault tolerance approach is also given, presenting that the system can maintain certain reliability even losing some of its parts.