Improving WiFi Indoor Localization Through a Peer-to-Peer Architecture

In this paper, we present an implementation of indoor localization, where the location of an object is determined without using the outdoor infrastructure of GPS. Various work involving different technologies exist, but the field remains open to a solution which can attain widespread use, in consideration of cost and infrastructure needed for deployment. For our implementation, we localized a common smartphone in an area with many WiFi signals. The main method was through RSS fingerprinting which was able to achieve a mean error of 2.5 m, aside from outliers that drove the error to 16 m. To improve this, Pedestrian Dead Reckoning (PDR) techniques were leveraged in addition to the WiFi system. Realtime testing showed that the WiFi+PDR system was able to achieve a 2.28 m mean localization error, while it reached 2.62 m for the WiFi-only approach. Finally, a peer-to-peer approach was explored, in which neighboring smartphone peers were treated as pseudo access points to help eliminate the outlier errors that resulted from very similar RSS fingerprints. To measure distance between peers, an acoustic method was employed using 1 KHz sine waves. The Time Difference of Arrival (TDoA) was computed between peers, and the readings allowed for coarse estimates in 5-m bins. However, for very noisy environments, this frequency range may cause the acoustic system to be erroneous, possibly driving the error to as high as 18.01 m.