Avaliação de Técnicas de Localização Indoor por Fingerprint de RSSI com Simulações no NS-3
Resumo
A localização indoor através de fingerprint de RSSI foi um tema amplamente estudado nos últimos anos. Um ponto comum na maioria dos trabalhos é que a avaliação de desempenho das técnicas propostas é realizada através de experimentos práticos. Apesar de ser uma boa forma de avaliar desempenho e validar as propostas em ambientes reais, os experimentos práticos limitam a avaliação do impacto que as características do ambiente têm sobre o desempenho das técnicas de localização. Além disso, o uso de cenários distintos dificulta a comparabilidade entre propostas. Este trabalho propõe uma nova forma de avaliar desempenho de técnicas de localização por fingerprint de RSSI usando simulações com o NS-3. Com as ferramentas do NS-3 é possível criar cenários que representam ambientes indoor com características distintas e avaliar o impacto dessas características no desempenho da localização.
Palavras-chave:
Simulação, Localização indoor, Redes sem fio
Referências
Bahl, P. and Padmanabhan, V. N. (2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings IEEE International Conference on Computer Communications (INFOCOM), volume 2, pages 775–784.
Basiri, A., Lohan, E. S., Moore, T., Winstanley, A., Peltola, P., Hill, C., Amirian, P., and Figueiredo e Silva, P. (2017). Indoor location based services challenges, requirements and usability of current solutions. Computer Science Review, 24:1–12.
Brunato, M. and Battiti, R. (2005). Statistical learning theory for location fingerprinting in wireless LANs. Computer Networks, 47(6):825–845.
Bugarcic, P. D., Jevtic, N. J., and Malnar, M. Z. (2021). An extension of building model for indoor communication in NS-3 simulator. In 2021 29th Telecommunications Forum (TELFOR), pages 1–4.
Carvalho, A. d. S. (2021). Localização em Ambientes Internos utilizando redes IEEE 802.11 e algoritmo WKNN. Master’s thesis, Universidade Federal do Amazonas, Brasil.
Ferreira, D., Souza, R., and Carvalho, C. (2020). QA-kNN: Indoor localization based on quartile analysis and the knn classifier for wireless networks. Sensors, 20(17):4714.
Haeberlen, A., Flannery, E., Ladd, A. M., Rudys, A., Wallach, D. S., and Kavraki, L. E. (2004). Practical robust localization over large-scale 802.11 wireless networks. In Proceedings of the 10th annual international conference on Mobile computing and networking, pages 70–84.
ITU (2021a). RECOMMENDATION ITU-R P.1238-11 Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 450 GHz. Disponível em https://www.itu.int/rec/R-REC-P.1238/en.
ITU (2021b). Recommendation ITU-R P.1411-11 Propagation data and prediction methods for the planning of short-range outdoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 100 GHz. Disponível em https://www.itu.int/rec/R-REC-P.1411/en.
Jedari, E., Wu, Z., Rashidzadeh, R., and Saif, M. (2015). Wi-Fi based indoor location positioning employing random forest classifier. In 2015 international conference on indoor positioning and indoor navigation (IPIN), pages 1–5. IEEE.
Jiang, Y., Pan, X., Li, K., Lv, Q., Dick, R. P., Hannigan, M., and Shang, L. (2012). ARIEL: Automatic Wi-Fi based room fingerprinting for indoor localization. In Proceedings of the 2012 ACM conference on ubiquitous computing, pages 441–450.
NS-3 (2022a). Buildings Module Model Library. https://www.nsnam.org/docs/models/html/buildings.html.
NS-3 (2022b). ns-3 | a discrete-event network simulator for internet systems. https://www.nsnam.org/.
Rappaport, T. S. et al. (1996). Wireless communications: principles and practice, volume 2. New Jersey: prentice hall PTR.
Sadowski, S. and Spachos, P. (2018). RSSI-Based Indoor Localization With the Internet of Things. IEEE Access, 6:30149–30161.
Singh, N., Choe, S., and Punmiya, R. (2021). Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview. IEEE Access.
Zafari, F., Gkelias, A., and Leung, K. K. (2019). A survey of indoor localization systems and technologies. IEEE Communications Surveys Tutorials, 21(3):2568–2599.
Basiri, A., Lohan, E. S., Moore, T., Winstanley, A., Peltola, P., Hill, C., Amirian, P., and Figueiredo e Silva, P. (2017). Indoor location based services challenges, requirements and usability of current solutions. Computer Science Review, 24:1–12.
Brunato, M. and Battiti, R. (2005). Statistical learning theory for location fingerprinting in wireless LANs. Computer Networks, 47(6):825–845.
Bugarcic, P. D., Jevtic, N. J., and Malnar, M. Z. (2021). An extension of building model for indoor communication in NS-3 simulator. In 2021 29th Telecommunications Forum (TELFOR), pages 1–4.
Carvalho, A. d. S. (2021). Localização em Ambientes Internos utilizando redes IEEE 802.11 e algoritmo WKNN. Master’s thesis, Universidade Federal do Amazonas, Brasil.
Ferreira, D., Souza, R., and Carvalho, C. (2020). QA-kNN: Indoor localization based on quartile analysis and the knn classifier for wireless networks. Sensors, 20(17):4714.
Haeberlen, A., Flannery, E., Ladd, A. M., Rudys, A., Wallach, D. S., and Kavraki, L. E. (2004). Practical robust localization over large-scale 802.11 wireless networks. In Proceedings of the 10th annual international conference on Mobile computing and networking, pages 70–84.
ITU (2021a). RECOMMENDATION ITU-R P.1238-11 Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 450 GHz. Disponível em https://www.itu.int/rec/R-REC-P.1238/en.
ITU (2021b). Recommendation ITU-R P.1411-11 Propagation data and prediction methods for the planning of short-range outdoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 100 GHz. Disponível em https://www.itu.int/rec/R-REC-P.1411/en.
Jedari, E., Wu, Z., Rashidzadeh, R., and Saif, M. (2015). Wi-Fi based indoor location positioning employing random forest classifier. In 2015 international conference on indoor positioning and indoor navigation (IPIN), pages 1–5. IEEE.
Jiang, Y., Pan, X., Li, K., Lv, Q., Dick, R. P., Hannigan, M., and Shang, L. (2012). ARIEL: Automatic Wi-Fi based room fingerprinting for indoor localization. In Proceedings of the 2012 ACM conference on ubiquitous computing, pages 441–450.
NS-3 (2022a). Buildings Module Model Library. https://www.nsnam.org/docs/models/html/buildings.html.
NS-3 (2022b). ns-3 | a discrete-event network simulator for internet systems. https://www.nsnam.org/.
Rappaport, T. S. et al. (1996). Wireless communications: principles and practice, volume 2. New Jersey: prentice hall PTR.
Sadowski, S. and Spachos, P. (2018). RSSI-Based Indoor Localization With the Internet of Things. IEEE Access, 6:30149–30161.
Singh, N., Choe, S., and Punmiya, R. (2021). Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview. IEEE Access.
Zafari, F., Gkelias, A., and Leung, K. K. (2019). A survey of indoor localization systems and technologies. IEEE Communications Surveys Tutorials, 21(3):2568–2599.
Publicado
31/07/2022
Como Citar
ZAMITH, Marcelo; SILVA, Marcel William Rocha da.
Avaliação de Técnicas de Localização Indoor por Fingerprint de RSSI com Simulações no NS-3. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 21. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 37-48.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2022.223198.
