Completely Automated CNN Architecture Design Based on VGG Blocks for Fingerprinting Localisation

WiFi fingerprinting using Convolutional Neural Networks (CNN) is one of the most promising techniques for indoor localisation due to the extraordinary performance of CNN in image classification. However, the performance of CNN is architecture dependant, and thus an architecture that works well in one case may not work in another, especially for the WiFi-based localisation problems. Most of the solutions use an existing hand-crafted architecture or a semi-automated CNN design for fingerprinting, which requires significant CNN expertise and time. Therefore, a satisfactory solution may not be guaranteed as it is challenging to design numerous possible architectures. In this work, we address this challenge by developing a framework that completely automates the CNN architecture design process. Our automated architectures based on VGG blocks have shown superior performance compared to standard architectures such as VGG-16. We further explore three different heuristics for automation: Bayesian optimisation, Hyperband, and Random Search and demonstrate their importance towards the automated CNN architecture development for WiFi fingerprinting. Experiments are conducted on real-world datasets and, a comparative study between our automated architecture and other models is presented. This work would, therefore, facilitate the CNN design for indoor localisation.