Indoor Positioning Systems (IPS) are essential for delivering accurate location-based services in environments where Global Navigation Satellite Systems (GNSS) are ineffective. The Received Signal Strength Indicator (RSSI)-based IPS leverages the existing access point infrastructure to provide a cost-effective solution for indoor location determination. However, device heterogeneity, characterized by variations in hardware, sensors, and software between devices, poses a significant challenge, often degrading positioning accuracy and robustness. This study investigates the impact of device heterogeneity on IPS performance using the TUJI1 dataset, which comprises RSSI measurements collected from five different devices. After comprehensive preprocessing of the RSSI signals, the study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) framework, leveraging advanced feature extraction techniques to improve positioning accuracy and mitigate the effects of device variability. The proposed model achieves a three-dimensional (3D) mean positioning error of 2.20 meters, outperforming traditional k-Nearest Neighbors (k-NN) methods. In cross-device evaluations, the model demonstrates improved robustness, reducing positioning errors by up to 0.17 meters compared to conventional approaches. These results highlight the effectiveness of the CNN-LSTM architecture in addressing device heterogeneity, offering a scalable and efficient solution for diverse IPS environments. This study advances the field of indoor positioning by providing a robust framework capable of maintaining high accuracy in the presence of device variability, thus contributing to the development of more reliable and adaptable IPS technologies.

This research investigates the influence of environmental regulation on subjective evaluations of video quality within the Quality of Experience (QoE) paradigm. This work presents a supplementary experiment conducted in a controlled laboratory setting, building on our previous crowdsourcing studies carried out in uncontrolled, web-based conditions using the Prolific platform. Both tests utilized the identical crowdsourcing platform and complied with the International Telecommunication Union Telecommunication (ITU-T) P.910 Recommendations, ensuring external validity and methodological consistency. Participants assessed a collection of processed video sequences (PVS) comprising 46 distinct video clips utilizing the 5-point Absolute Category Rating (ACR) scale, while their response times were documented in milliseconds as measures of cognitive exertion and decision delay. The comparison analysis employs nonparametric tests (Mann–Whitney U and Kolmogorov–Smirnov) and a hierarchical Linear Mixed-Effects Model (LMM) to examine disparities in reaction time distributions, rating consistency, and the incidence of outliers across both environments. The results indicate that controlled settings produce statistically significantly less response variability and enhanced data reliability, whereas uncontrolled settings encompass greater external diversity and real-world unpredictability. These findings offer significant insights into the balance between experimental control and external validity in crowdsourced video quality assessment, advancing the development of scalable approaches for Quality of Experience research.