Publicação
Towards quality Wi-Fi synthetic data for indoor positioning evaluation
| Resumo: | Synthetic data of high quality can provide research teams with an effective means of conducting large-scale evaluations of their indoor positioning systems under controlled conditions, while avoiding the significant effort and costs associated with real-world experiments and data collection/labelling. Moreover, it facilitates the fair comparison with other solutions, since data can be generated for more diverse conditions and can be shared without concerns. The work described in this paper aims to improve the quality of WiFi synthetic data by integrating new models for channel noise and beacon receive probability into the Dioptra tool. We compare the results of 13 fingerprinting methods used on 15 synthetic databases and 14 real-world databases. The results indicate that synthetic data can be an effective alternative/complement for the evaluation and comparison of WiFi-based positioning methods. |
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| Autores principais: | Pendão, Cristiano Gonçalves |
| Outros Autores: | Silva, Ivo Miguel Menezes; Moreira, Adriano; Aranda, Fernando J.; Torres-Sospedra, Joaquín |
| Assunto: | Benchmarking Channel Model Indoor Positioning Simulation Synthetic Data Wi-Fi Fingerprinting |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Synthetic data of high quality can provide research teams with an effective means of conducting large-scale evaluations of their indoor positioning systems under controlled conditions, while avoiding the significant effort and costs associated with real-world experiments and data collection/labelling. Moreover, it facilitates the fair comparison with other solutions, since data can be generated for more diverse conditions and can be shared without concerns. The work described in this paper aims to improve the quality of WiFi synthetic data by integrating new models for channel noise and beacon receive probability into the Dioptra tool. We compare the results of 13 fingerprinting methods used on 15 synthetic databases and 14 real-world databases. The results indicate that synthetic data can be an effective alternative/complement for the evaluation and comparison of WiFi-based positioning methods. |
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