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With the ongoing development of Indoor Location-Based Services, accuratelocation information of users in indoor environments has been a challengingissue in recent years. Due to the widespread use of WiFi networks, WiFifingerprinting has become one of the most practical methods of locating mobileusers. In addition to localization accuracy, some other critical factors suchas cost, latency, and users' privacy should be considered in indoorlocalization systems. In this study, we propose a lightweight ConvolutionalNeural Network (CNN)-based method for edge devices (such as smartphones) toovercome the above issues by eliminating the need for a cloud/server in thelocalization system. To enable the use of the proposed model onresource-constraint edge devices, post-training optimization techniquesincluding quantization, pruning and clustering are used to compress the networkmodel. The proposed method is evaluated for three different open datasets,i.e., UJIIndoorLoc, Tampere and UTSIndoorLoc, as well as for our collecteddataset named SBUK-D to verify its scalability. The results demonstrate thesuperiority of the proposed method compared to state-of-the-art studies. Wealso evaluate performance efficiency of our localization method on an androidsmartphone to demonstrate its applicability to edge devices. For UJIIndoorLocdataset, our model with post-training optimizations obtains approximately 99%building accuracy, over 98% floor accuracy, and 4 m positioning mean error withthe model size and inference time of 60 KB and 270 us, respectively, whichdemonstrate high accuracy as well as amenability to the resource-constrainededge devices.

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