  Indoor localization services are a crucial aspect for the realization of
smart cyber-physical systems within cities of the future. Such services are
poised to reinvent the process of navigation and tracking of people and assets
in a variety of indoor and subterranean environments. The growing ownership of
computationally capable smartphones has laid the foundations of portable
fingerprinting-based indoor localization through deep learning. However, as the
demand for accurate localization increases, the computational complexity of the
associated deep learning models increases as well. We present an approach for
reducing the computational requirements of a deep learning-based indoor
localization framework while maintaining localization accuracy targets. Our
proposed methodology is deployed and validated across multiple smartphones and
is shown to deliver up to 42% reduction in prediction latency and 45% reduction
in prediction energy as compared to the best-known baseline deep learning-based
indoor localization model.
