Convolutional Neural Networks for Efficient Indoor Navigation with Smartphones

Indoor localization is a rapidly evolving application domain for enhanced tracking of people and assets in indoor environments such as airports, hospitals, and underground mines. Most indoor localization solutions proposed in prior work do not deliver good accuracy without expensive infrastructure (and even then, the results may lack consistency). Ambient wireless received signal strength indication (RSSI)-based fingerprinting using smart mobile devices is a low-cost approach to the problem. However, creating an accurate “fingerprinting-only” solution remains an open challenge. This chapter presents a novel approach to transform Wi-Fi signatures into images, to create a scalable fingerprinting framework based on convolutional neural networks (CNNs). Our proposed CNN-based indoor localization framework (CNNLOC) is validated across several indoor locales and shows improvements over the best-known prior works, with an average localization error of <2 m.