An explainable hybrid deep learning architecture for WiFi-based indoor localization in Internet of Things environment

Abstract

The indoor positioning service is one of the essential services needed in the Internet of Things ecosystem. Recently, many researchers have focused on the fingerprinting method, which is a method based on signal mapping with the Received Signal Strength Indicator (RSSI) values obtained from the WiFi access points. However, the fingerprinting method is particularly challenging due to some difficulties, such as RSSI variance over time, device diversity, and similarities of fingerprints in indoor networks. For this reason, machine learning and deep learning methods are used for many purposes, such as estimating the location of the building, floor, or the rooms. Detecting the location of a room or more than one reference point in a room becomes a more difficult problem because neighboring reference points' fingerprints are very similar to each other. This study proposes a WiFi-based XAI-empowered deep learning architecture to predict the reference points in a room or corridor. We present a hybrid deep learning-based method that uses Long-Short-Term Memory to capture long-term dependencies between the signal features, and Convolutional Neural Network to extract local spatial signal patterns. Our deep learning aims to enrich fingerprinting data of each sample to capture more meaningful feature maps coming from different angles. Moreover, the method applies effective filtering and dimension scaling on the data to regulate the RSS values and capture more discriminative patterns using particle filter and sparse autoencoder. To provide local and global explanations for indoor localization estimations, the proposed architecture comprises two Explainable Artificial Intelligence techniques as Interpretable Model-Agnostic Explanations, and SHapley Additive exPlanations. The experimental results demonstrate that the proposed architecture achieves higher accuracy values for all datasets than the baseline deep learning methods.