Evaluation of crowdsourcing Wi-Fi radio map creation in a real scenario for AAL applications

Indoor location at room level plays a key role for providing useful services for Ambient Assisted Living (AAL) applications. Wi-Fi fingerprinting indoor location methods are extensively used due to the widespread availability of WiFi infrastructures. A main drawback of Wi-Fi fingerprinting methods is the temporal cost involved in creating the radio maps. Crowdsourcing strategies have been presented as a way to minimize the cost of radio map creation. In this work, we present an extensive study of the issues involved when using crowdsourcing strategies for that purpose. Results provided by extensive experiments performed in a real scenario by three users during two weeks are presented. The main conclusions are: i) crowdsourcing data improves accuracy location in most studied cases; ii) accuracy of Wi-Fi fingerprinting methods decay along time; iii) device diversity is an important issue even when using the same device model