Publications

Papers, datasets, and open-source code

Papers

RSSI-Based Passenger Movement Classification for Non-Intrusive Public Transport Monitoring

EuCNC & 6G Summit 2026

André Ribeiro, Guilherme Matos, Julio Corona, Mário Antunes, Diogo Gomes

Instituto de Telecomunicações, Universidade de Aveiro, Portugal

BibTeX

@INPROCEEDINGS {41897,
  author={A. Ribeiro and G. Matos and J. C. Corona and M. Antunes and D.Gomes Gomes},
  doi={},
  booktitle={EuCNC & 6G Summit},
  title={RSSI-Based Passenger Movement Classification for Non-Intrusive Public Transport Monitoring},
  year={2026},
  month={June},
  volume={},
  pages={-} 
  }

Datasets

RSSI Passenger Movement Dataset

1,356 labelled RSSI samples across four movement classes (AA, BB, BA, AB). Two collection conditions: isolated (160) and noisy (1,196). Four smartphone models, three brands.

IEEE Dataport 10.21227/55nm-0r91

BibTeX

@data{55nm-0r91-26,
  doi = {10.21227/55nm-0r91},
  url = {https://dx.doi.org/10.21227/55nm-0r91},
  author = {André Ribeiro and Guilherme Matos and Júlio Corona and Mário Antunes and Diogo Gomes},
  publisher = {IEEE Dataport},
  title = {WiFi RSSI Passenger Movement Dataset: Boarding, Alighting, and Static Classes in a Simulated Bus Environment},
  year = {2026} }

Zenodo Mirror

The dataset is also archived on Zenodo with a permanent DOI for long-term citation and reproducibility.

Zenodo

Code

inMotion — Full Pipeline

Data export from Wavecom captures, CSV preprocessing, 38-classifier ML pipeline with Optuna hyperparameter optimisation, deep learning experiments, and all analysis scripts. Hyperparameter optimisation supplementary materials with search spaces and optimal configurations.

github.com/ATNoG/inMotion