Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings

TLDR

Location services are central to smart IoT systems, and accurate localization is essential for many location‑based services, prompting growing research into IoT localization for smart buildings. This study proposes a novel localization approach that builds a fingerprint database from neighbor relative RSS and employs a Markov‑chain prediction model to assist positioning. The proposed LNM scheme analyzes pedestrian location history to reduce signal fluctuations, enabling calibration‑free positioning across diverse devices. Evaluation in a realistic environment demonstrates that the method outperforms existing schemes, especially when device heterogeneity and WiFi signal fluctuations are present.

Abstract

Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian's locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.