Year: 2025
Type: article
Abstract: Indoor wireless networks face increasing challenges in maintaining stable coverage and performance, particularly with the widespread use of high-frequency Wi-Fi and growing demands from smart home devices. Traditional methods to improve signal quality, such as adding access points, often fall short in dynamic environments where user movement and physical obstructions affect signal behavior. In this work, we propose a system that leverages existing Internet of Things (IoT) devices to perform real-time user localization and network adaptation using fine-grained Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) measurements. We deploy multiple ESP-32 microcontroller-based receivers in fixed positions to capture wireless signal characteristics and process them through a pipeline that includes filtering, segmentation, and feature extraction. Using supervised machine learning, we accurately predict the user’s location within a defined indoor grid. Our system achieves over 82% accuracy in a realistic laboratory setting and shows improved performance when excluding redundant sensors. The results demonstrate the potential of communication-based sensing to enhance both user tracking and wireless connectivity without requiring additional infrastructure.
Source: Future Internet
DOI: 10.3390/fi17090395
Authors: Diana Macedo, Miguel Loureiro, Óscar G. Martins, Joana Coutinho Sousa, David Belo, Marco Gomes
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
Awards: G1516346500, G1578237752, G1763632427, G2144570049, G274581676, G2863610110, Instituto de Telecomunicações, G3840324753, G3850789590, G387864031, G3891694003, G4724859719, G491858407, G5224927334, G5229091003, G5677667711, G6329845220, G6629118913, G691567502, G6997658380, G7965899483, Instituto de Telecomunicações, G947368024
OpenAlex ID: W4413907537