Estimativa de Distância em Redes Wi-Fi usando 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

Resumo

Sniffers Wi-Fi são dispositivos responsáveis por realizar a coleta passiva de pacotes em redes sem-fio. Sniffers possuem aplicações, entre outras, em processos de estimativa de distância e localização através do uso da métrica RSSI (Received Signal Strength Indicator). Porém, o RSSI é sensível a pequenas pertubações no ambiente e, sem tratamento, não fornece uma estimação de distância confiável. Este artigo formula uma nova abordagem que utiliza redundância através de um super-sniffer que consiste de múltiplos sniffers colocalizados para melhorar o processo de classificação de distância através de dois modelos, baseados em k-Nearest Neighbors (k-NN) e em log-distance path loss (LDPL). Aplica-se a estratégia formulada a um conjunto de dados experimental próprio e mostra-se que o método é capaz de gerar um modelo com acurácia média de 91,73%, além de determinar um ponto de saturação para os ganhos relacionados ao aumento do tamanho do super-sniffer.

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