Cascaded Layered Recurrent Neural Network for Indoor Localization in Wireless Sensor Networks

The growth in using various smart wireless devices in the last few decades has given rise to indoor localization service (ILS). Indoor localization is defined as the process of locating a user location in an indoor environment. Indoor device localization has been widely studied due to its popular applications in public settlement planning, health care zones, disaster management, the implementation of location-based services (LBS) and the Internet of Things (IoT). The ILS problem can be formulated as a learning problem utilizing Wi-Fi technology. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in a various indoor location. Developing a classification model with high accuracy can be achieved using a machine learning approach. Artificial Neural Network is one of the most successful trends in machine learning. In this article, we provide our initial idea of using Cascaded Layered Recurrent Neural Network (L-RNN) for the classification of user localization in an indoor environment. Several neural network models were trained, with the best performance attainment is reported. The experimental results marked that the presented L-RNN model is highly accurate for indoor localization and can be utilized for many applications.