Now, if we want to develop a DL application and deploy low-end devices, such IoT devices won't be able to process them. In particular, handling very high-dimensional data would be a bottleneck. So, an outdoor localization problem can be solved with reasonable accuracy using a machine learning algorithm such as k-nearest neighbors (k-NNs) because the inclusion of GPS sensors in mobile devices means we now have more data at hand.
However, indoor localization is still an open research problem, mainly due to the loss of GPS signals in indoor environments, despite advanced indoor positioning technologies. Fortunately, by using DL techniques, we can solve this problem with reasonable accuracy, especially since using Autoencoders (AEs) and their representation capabilities can be a pretty good workaround and a viable option. In such a setting...