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

Aug 4, 2026

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Water quality monitoring and contamination detection remain active research areas due to the health risks posed by toxic substances in drinking water. A proposed approach differentiates clean water from poisoned water by exploiting Wi-Fi signal behavior: smartphone-embedded Wi-Fi access-point signals are captured, channel-state information (CSI) is extracted, and CSI measurements are converted into feature vectors for machine-learning classification. Amplitude and phase features support k-NN, SVM, LSTM, and ensemble models, achieving 89% accuracy with LSTM and up to 92% with AdaBoost-Ensemble.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Motivation and healthcare relevance

Existing detection approaches and limitations

Proposed Wi-Fi CSI + machine learning method

Study contributions

A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms Halgurd S. Maghdid1,*, Sheerko R. Hma Salah1, Akar T. Hawre1, Hassan M. Bayram1, Azhin T. Sabir1, Kosrat N. Kaka3, Salam Ghafour Taher3, Ladeh S. Abdulrahman1, Abdulbasit K. Al-Talabani1, Safar M. Asaad1, Aras Asaad1,2 1 Department of Software Engineering, Faculty of Engineering, Koya University, Kurdistan Region of Iraq 1,2School of Computing, The University of Buckingham, UK 3 Department of Chemistry, Faculty of Science and health, Koya University, Kurdistan Region of Iraq 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. Keywords: Poisoned Water Detection, CSI, Wi-Fi Signal, Deep Learning, Smartphone. * Corresponding author: [email: ](email: halgurd.maghdid@koyauniversity.org)[halgurd.maghdid@koyauniversity.org](email: halgurd.maghdid@koyauniversity.org) [1- Introduction:](1- Introduction:) Healthcare is one of the most important sectors in our daily life that requires certainty and regular checking [1] . Current healthcare systems help to conduct diagnosis and reduce the medical treatment. Despite the need for a good financial support, sometimes diagnosis and cure may take a period of time due to having various diseases for different reasons such as food, drink, environment, etc. Water drinking flows through the organs of human body, as it is known that it makes up a significant fraction of the body [2] . For example, healthy humans with regular activities need around 2.7 litter to 3.7 litter daily water intake [3] . Further, water is one of the essential fluids for humane body, it’s not just for quenching the thirst, it is also important to hydrate the body and keeping the body organ functions work properly and healthy. However, for whatever reason, sometimes people deliberately put poison in clean water, making it poisonous, or sometimes the water is spontaneously poisoned by chemicals elements (such as toxic element [4]) in the water sources. Furthermore, the cumulated poisoning mainly comes from water, as human drink water frequently, and even trace amounts of toxins in water cumulates with in short period of time and cause serious health problems and in serious cases cause death [5] . There are several hidden toxic chemicals soluble in water with no colour, flavour and some are reported to cause acute poisoning [6] . Among toxic compounds in the list are metals (Pb, Cd, Cr, Ni, and Zn), particularly heavy metals. Lead (Pb) harmfulness is significant consequences for the human body, and has a lethal dose around (LD50 93 mg/kg Chronic Toxicity) to guarantee death by ingestion. Lead poisoning happens when its concentration builds up in the body by entering repeated doses from water or any sources, and often over months or years [7] . This seriously affect mental and physical development with enormous numbers of disease (anaemia, high blood pressure, and chro

FAQs

How does the method detect poisoned water?

It captures Wi-Fi signals using smartphone-embedded Wi-Fi chips, extracts CSI, and converts CSI amplitude and phase into feature vectors for machine-learning classifiers.

Which classifiers are used in the study?

The approach evaluates k-NN, SVM, LSTM, and ensemble learning models, including an AdaBoost-Ensemble classifier.

What classification accuracies are reported?

LSTM reaches 89% accuracy for separating poisoned from clean water, while AdaBoost-Ensemble achieves 92% accuracy.

Aug 4, 2026

Upload by Jiven