A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms

TL;DR

This study presents a novel method for detecting poisoned water using smartphone-embedded Wi-Fi signals and machine learning algorithms, achieving high classification accuracy in distinguishing contaminated from clean water.

Contribution

The paper introduces a new approach combining Wi-Fi signal analysis with machine learning to identify poisoned water, demonstrating effective classification accuracy.

Findings

LSTM classifier achieved 89% accuracy.

AdaBoost-Ensemble classifier achieved 92% accuracy.

Wi-Fi-based detection is effective for water quality assessment.

Abstract

Water is a necessary fluid to the human body and automatic checking of its quality and cleanness is an ongoing area of research. One such approach is to present the liquid to various types of signals and make the amount of signal attenuation an indication of the liquid category. In this article, we have utilized the Wi-Fi signal to distinguish clean water from poisoned water via training different machine learning algorithms. The Wi-Fi access points (WAPs) signal is acquired via equivalent smartphone-embedded Wi-Fi chipsets, and then Channel-State-Information CSI measures are extracted and converted into feature vectors to be used as input for machine learning classification algorithms. The measured amplitude and phase of the CSI data are selected as input features into four classifiers k-NN, SVM, LSTM, and Ensemble. The experimental results show that the model is adequate to differentiate poison water from clean water with a classification accuracy of 89% when LSTM is applied, while 92% classification accuracy is achieved when the AdaBoost-Ensemble classifier is applied.