An Edge-Based WiFi Fingerprinting Indoor Localization Using Convolutional Neural Network and Convolutional Auto-Encoder

With the ongoing development of Indoor Location-Based Services, the location information of users in indoor environments has been a challenging issue in recent years. Due to the widespread use of WiFi networks, WiFi fingerprinting has become one of the most practical methods of locating mobile users. In addition to localization accuracy, some other critical factors such as latency, and users’ privacy should be considered in indoor localization systems. In this study, we propose a light Convolutional Neural Network-based method for edge devices (e.g. smartphones) to overcome the above issues by eliminating the need for a cloud/server in the localization system. The proposed method is evaluated for three different open datasets, i.e., UJIIndoorLoc, Tampere and UTSIndoorLoc, as well as for our collected dataset named SBUK-D to verify its scalability. We also evaluate performance efficiency of our localization method on an Android smartphone to demonstrate its applicability to edge devices. For UJIIndoorLoc dataset, our model obtains approximately 99% building accuracy, over 90% floor accuracy, and 9.5 m positioning mean error with the model size and inference time of 0.5 MB and
$51~\mu $
s, respectively, which demonstrate high accuracy in range of state of the art works as well as amenability to the resource-constrained edge devices.