Distance Estimation in Wi-Fi Networks Using Super-Sniffers

• Pedro Videira Rubinstein UFRJ
• Fernando Dias de Mello Silva UFRJ
• Mohammad Imran Syed Sorbonne Université / CNRS / LIP6
• Anne Fladenmuller Sorbonne Université / CNRS / LIP6
• Marcelo Dias de Amorim Sorbonne Université / CNRS / LIP6
• Luís Henrique M. K. Costa UFRJ

DOI: https://doi.org/10.5753/sbrc.2024.1428

Abstract

Wi-Fi sniffers are devices responsible for passive data collection in wireless networks. They find application in, among others, distance estimation and location processes through the use of the RSSI metric (Received Signal Strength Indicator). Unfortunately, RSSI is sensitive to small variations in the environment and, without treatment, fails to provide a reliable distance measure. In this paper, we formulate a new approach that employs redundancy through a super-sniffer, which are multiple co-located sniffers, to enhance the distance classification process through two models: a k-Nearest Neighbors (k-NN) based and a log-distance path loss (LDPL) based model. We apply the formulated strategy to our experimental dataset and demonstrate that the method can generate a model with an average accuracy of 91.73% in addition to determining a saturation point for gains related to increasing the super-sniffer size.

References

Barai, S., Biswas, D., and Sau, B. (2017). Estimate distance measurement using nodemcu esp8266 based on rssi technique. In 2017 IEEE Conference on Antenna Measurements & Applications (CAMA), page 170–173.

Chuku, N. and Nasipuri, A. (2021). RSSI-based localization schemes for wireless sensor networks using outlier detection. Journal of Sensor and Actuator Networks, 10.

Dong, Q. and Dargie, W. (2012). Evaluation of the reliability of RSSI for indoor localization. In 2012 International Conference on Wireless Communications in Underground and Confined Areas.

Freudiger, J. (2015). How talkative is your mobile device?: an experimental study of wi-fi probe requests. In Proceedings of the 8th ACM Conference on Security and Privacy in Wireless and Mobile Networks.

Gupta, V., Beyah, R., and Corbett, C. (2007). A Characterization of Wireless NIC Active Scanning Algorithms. In 2007 IEEE Wireless Communications and Networking Conference, pages 2385–2390.

Heurtefeux, K. and Valois, F. (2012). Is RSSI a good choice for localization in wireless sensor network? In 2012 IEEE 26th International Conference on Advanced Information Networking and Applications.

Jaisinghani, D., Naik, V., Kaul, S. K., and Roy, S. (2017). Sniffer-based inference of the causes of active scanning in WiFi networks. In 2017 Twenty-third National Conference on Communications (NCC), pages 1–6, Chennai, India. IEEE.

Jose, A. A., Rishikesh, P. H., and Shaju, S. (2023). Mitigation of RSSI variations using frequency analysis and kalman filtering. In Proceedings of the International Conference on Cognitive and Intelligent Computing.

Li, G., Geng, E., Ye, Z., Xu, Y., Lin, J., and Pang, Y. (2018). Indoor positioning algorithm based on the improved RSSI distance model. Sensors, vol. 18, no. 9, p. 2820, Aug. 2018.

R, V., Mittal, V., and Tammana, H. (2021). Indoor localization in BLE using mean and median filtered RSSI values. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI).

Rappaport, T. (2002). Wireless Communications Principles and Practice. 2nd Edition. Prentice Hall.

Saha, S., Chaudhuri, K., Sanghi, D., and Bhagwat, P. (2003). Location determination of a mobile device using IEEE 802.11b access point signals. In 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003.

Syed, M. I., Fladenmuller, A., and Amorim, M. D. D. (2022a). RSSI: Lost and alone, a case for redundancy. In 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

Syed, M. I., Fladenmuller, A., and Dias de Amorim, M. (2022b). How much can sniffer redundancy improve Wi-Fi traffic? In 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), pages 1–5.

Syed, M. I., Flandenmuller, A., and Dias de Amorim, M. (2022c). PyPal: Wi-Fi Trace Synchronization and Merging Python Tool. Technical report, LIP6 UMR 7606, UPMC Sorbonne Université, France.

Verma, V. and Singh, A. (2019). Indoor location determination using radio signal strength model for distance estimation. In 2019 International Conference on Computer Communication and Informatics (ICCCI), page 1–4.

Wu, Z., Jedari, E., Muscedere, R., and Rashidzadeh, R. (2016). Improved particle filter based on WLAN RSSI fingerprinting and smart sensors for indoor localization. Computer Communications Volume 83, June 2016, Pages 64-71.

Yiu, S., Dashti, M., Claussen, H., and Perez-Cruz, F. (2017). Wireless RSSI fingerprinting localization. Signal Processing Volume 131, February 2017, Pages 235-244.