Efficient IoT Devices Localization Through Wi-Fi CSI Feature Fusion and Anomaly Detection

TL;DR

This paper presents a novel smartphone-based IoT device localization system using Wi-Fi CSI feature fusion and AI-driven anomaly detection, achieving decimeter-level accuracy even in multipath environments.

Contribution

The paper introduces an innovative AI-based fusion of Wi-Fi CSI features and smartphone sensor data for precise IoT localization, along with a new anomaly detection algorithm for improved reliability.

Findings

Achieves nearly 90% decimeter-level localization accuracy.

Effective in multipath and real-world scenarios.

Anomaly detection improves measurement reliability.

Abstract

Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors within a home environment, yet struggles with precision due to inherent inaccuracies in these anchor positions. In response, we introduce a cutting-edge smartphone-based localization system for IoT devices, leveraging the precise positioning capabilities of smartphones equipped with motion sensors. Our system employs artificial intelligence (AI) to merge channel state information from proximal trajectory points of a single smartphone, significantly enhancing line of sight (LoS) angle of arrival (AoA) estimation accuracy, particularly under severe multipath conditions. Additionally, we have developed an AI-based anomaly detection algorithm to further increase the reliability of LoSAoA estimation. This algorithm improves measurement reliability by analyzing the correlation between the accuracy of reversed feature reconstruction and the LoS-AoA estimation. Utilizing a straightforward least squares algorithm in conjunction with accurate LoS-AoA estimation and smartphone positional data, our system efficiently identifies IoT device locations. Validated through extensive simulations and experimental tests with a receiving antenna array comprising just two patch antenna elements in the horizontal direction, our methodology has been shown to attain decimeter-level localization accuracy in nearly 90% of cases, demonstrating robust performance even in challenging real-world scenarios. Additionally, our proposed anomaly detection algorithm trained on Wi-Fi data can be directly applied to ultra-wideband, also outperforming the most advanced techniques.