2023
Other
Restricted
ChAALenge D6.3 - Integrazione in laboratorio e analisi delle prestazioni
Bacco F. M., Baronti P., Barsocchi P., Belli D., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Miori V., Palumbo F., Potortì F., Russo D.
Report di laboratorio di analisi dei risultati dell’integrazione e indagine prestazionale sul framework integrato contenente: (i) Risultati attinenti alla validità dei dati acquisiti dal framework, al fine del loro efficiente utilizzo da parte degli algoritmi sviluppati; (ii) Risultati riguardanti la correttezza, completezza e affidabilità dell’esito della sperimentazione sia in laboratorio sia sul campo e relativi alla valutazione prestazionale del software di sistema.Project(s): ChAALenge
Bacco F. M., Baronti P., Barsocchi P., Belli D., Crivello A., Furfari F., Girolami M., La Rosa D., Mavilia F., Miori V., Palumbo F., Potortì F., Russo D.
Report di laboratorio di analisi dei risultati dell’integrazione e indagine prestazionale sul framework integrato contenente: (i) Risultati attinenti alla validità dei dati acquisiti dal framework, al fine del loro efficiente utilizzo da parte degli algoritmi sviluppati; (ii) Risultati riguardanti la correttezza, completezza e affidabilità dell’esito della sperimentazione sia in laboratorio sia sul campo e relativi alla valutazione prestazionale del software di sistema.Project(s): ChAALenge
2023
Conference article
Open Access
Let's talk about k-NN for indoor positioning: myths and facts in RF-based fingerprinting
Torressospedra J, Pendão C, Silva I, Meneses F, Quezadagaibor D, Montoliu R, Crivello A, Barsocchi P, Péreznavarro A, Moreira A
Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.DOI: 10.1109/ipin57070.2023.10332535
Metrics:
Torressospedra J, Pendão C, Silva I, Meneses F, Quezadagaibor D, Montoliu R, Crivello A, Barsocchi P, Péreznavarro A, Moreira A
Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.DOI: 10.1109/ipin57070.2023.10332535
Metrics:
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