CAE-CNNLoc: An Edge-based WiFi Fingerprinting Indoor Localization Using Convolutional Neural Network and Convolutional Auto-Encoder

With the ongoing development of Indoor Location-Based Services, accurate 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 cost, latency, and users' privacy should be considered in indoor localization systems. In this study, we propose a lightweight Convolutional Neural Network (CNN)-based method for edge devices (such as smartphones) to overcome the above issues by eliminating the need for a cloud/server in the localization system. To enable the use of the proposed model on resource-constraint edge devices, post-training optimization techniques including quantization, pruning and clustering are used to compress the network model. 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. The results demonstrate the superiority of the proposed method compared to state-of-the-art studies. 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 with post-training optimizations obtains approximately 99% building accuracy, over 98% floor accuracy, and 4 m positioning mean error with the model size and inference time of 60 KB and 270 us, respectively, which demonstrate high accuracy as well as amenability to the resource-constrained edge devices.