Year: 2025
Type: article
Abstract: The Internet of Things (IoT) is transforming industrial operations, particularly under Industry 4.0, by enabling real-time connectivity and automation. Accurate indoor localization is critical for warehouse management, where GPS-based solutions are ineffective due to signal obstruction. This paper presents a smart indoor positioning system (IPS) integrating Ultra-Wideband (UWB) sensors with Long Short-Term Memory (LSTM) neural networks and Kalman filtering, employing a tailored data fusion sequence and parameter optimization for real-time object tracking. The system was deployed in a 54 m2 warehouse section on forklifts equipped with UWB modules and QR scanners. Experimental evaluation considered accuracy, reliability, and noise resilience in cluttered industrial conditions. Results, validated with RMSE, MAE, and standard deviation, demonstrate that the hybrid Kalman–LSTM model improves localization accuracy by up to 4% over baseline methods, outperforming conventional sensor fusion approaches. Comparative analysis with standard benchmarks highlights the system’s robustness under interference and its scalability for larger warehouse operations. These findings confirm that combining temporal pattern learning with advanced sensor fusion significantly enhances tracking precision. This research contributes a reproducible and adaptable framework for intelligent warehouse management, offering practical benefits aligned with Industry 4.0 objectives.
Source: Computers
Authors: Bahareh Masoudi, Nazila Razi, Javad Rezazadeh
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4416594730