SELE: RSS-Based Siamese Embedding Location Estimator for a Dynamic IoT Environment

Abstract

Wireless Fidelity (Wi-Fi)-received signal strength (RSS) fingerprints are extensively used for localization in an indoor Internet of Things (IoT) environment. However, RSS fingerprinting-based localization methods face two major challenges. First, the labor-intensive and time-consuming task of constructing a fingerprinting database with a large number of RSS samples for offline training. Second, the RSS values vary due to device heterogeneity and temporal variance caused by the change in access points (APs), device orientation, and environmental factors. These dynamic RSS variations in the online phase decrease localization accuracy. This article proposes a Siamese embedding-based localization method for a dynamic IoT environment. The proposed method provides high localization accuracy with limited RSS samples by utilizing our proposed distance-based sampling method. Also, the Siamese embeddings capture the spatial relations between locations and remain persistent even with the RSS variations in the online phase. This is possible with the help of a lightweight, offline fine-tuning method that requires minimal RSS samples. The proposed method is validated on three real IoT testbeds with RSS variations. It outperforms the existing state-of-the-art methods on two testbeds along with the comparable results on the third.