Preliminary Investigation of Using Crowd-sourced Photos with Wi-Fi Signals for Predicting Indoor Location Class

Abstract

Due to the recent evolution and proliferation of smartphones and the social network service (SNS), there are a huge amount of images taken by smartphones at various places that have been uploaded to SNS. Furthermore, various sensors in smartphones such as camera and Wi-Fi modules enable us to easily generate a camera image associated with the sensory information that represents the context in which the image was taken. Therefore, this work investigates a method for using the benefits of camera images associated with Wi-Fi signal strength information to predict indoor location class for shopping complexes. Our method first estimates the store at which a camera image was taken by analyzing the image and web images of branch stores of store chains. Then, the floor plan is used to determine the 2D coordinates of the images taken at branch stores. A transformation function, that maps Wi-Fi signals onto the 2D coordinates, is then constructed using Wi-Fi signals of the branch store images and their estimated 2D coordinates. The function is adopted to predict the indoor location class of images associated with Wi-Fi signals. Moreover, our transformation function has novel features for addressing the non-linearity of the Wi-Fi space, generating virtual Wi-Fi scans on the floor, and training on unlabeled Wi-Fi signals.