Mar 15, 2022 · In this paper we study automatic indoor/outdoor classification based on the radio frequency (RF) environment experienced by a device.

Mar 15, 2022 · We find that tree-based ensemble ML models can achieve greater than 99% test accuracy and F1-Score, thus allowing devices to self-identify their ...

Thus, our classification methodology is based around extracting the signal strength measurements of all Wi-Fi APs and cellular base-stations, combining them ...

Ghosh, ” ML-based classification of device environment using Wi-Fi and cellular signal measurements,” IEEE Access, March 2022, https://ieeexplore.ieee.org ...

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Posted: Dec 2, 2022

We computed the confusion matrix for each model and used the five standard evaluation metrics for classification tasks in ML: accuracy, sensitivity, specificity ...

In this paper, we provide internal localization based on blue-tooth beacons using several different machine learning techniques.

Oct 17, 2024 · We propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different ...

Sep 18, 2023 · This paper presents an orchestration of baseline experiments, analyzing a variety of machine learning algorithms to identify the most suitable one for Wi-Fi- ...

Thus, to tackle this problem, we propose using the existing deployment of the device type policy [23] with an ML-based device type classification to limit ...