Evaluation of RSSI Fingerprint Indoor Localization Techniques with NS-3 Simulations
Abstract
Indoor localization through RSSI fingerprinting has been a widely studied topic in recent years. A common point in most works is that the performance evaluation of the proposed techniques is carried out through practical experiments. Despite being a good way to evaluate performance and validate proposals in real environments, practical experiments limit the assessment of the impact that environmental characteristics have on the performance of localization techniques. In addition, the use of different scenarios makes it difficult to compare proposals. This work proposes a new way to evaluate the performance of RSSI fingerprint localization techniques using NS-3 simulations. With the NS3 tools it is possible to create scenarios that represent indoor environments with different characteristics and to evaluate the impact of these characteristics on the localization performance.
Keywords:
Simulation, Indoor localization, Wireless networks
References
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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.
Published
2022-07-31
How to Cite
ZAMITH, Marcelo; SILVA, Marcel William Rocha da.
Evaluation of RSSI Fingerprint Indoor Localization Techniques with NS-3 Simulations. In: WORKSHOP ON PERFORMANCE OF COMPUTER AND COMMUNICATION SYSTEMS (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.
