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Internet of Things Enabled Smart E-Nose System for Pollutants Hazard Detection and Real-Time Monitoring in Indoor Mosquito Repellents

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Abstract

Mosquito-repellent is one of the most utilized products for dispelling mosquitoes that produce a significant quantity of gaseous, and particulate pollutants and are hazardous to human health. An indoor chamber experiment is conducted to identify the primary pollutants from mosquito repellents. This paper proposes an internet-of-things (IoT) based E-nose for volatile-organic compounds (VOCs)/gases/odors detection and real-time monitoring released from different mosquito repellents used in indoor environments. E-noses generate unique signature patterns for various VOCs/gases/odors. A gas sensor node interface is developed using eight cross-selective tin-oxide (MOX) based gas sensor arrays with a low-powered microcontroller to capture real-time signature patterns of VOCs/gases/odors released by different repellents when they are put in use. This microcontroller sends the gas sensor responses to the Amazon web services (AWS) cloud platform, from where data is ported to a remote data processing station (RDPS) for further analysis for its use in real-time. In this experiment, seven different types of mosquito repellents, viz mosquito coil, fast card, herbal cones, herbal spray, mosquito spray, hit spray and all-out liquid have been used. A dataset consisting of 1200 samples was captured. These sensor array responses are pre-processed using the two-stage analysis space transformation method, i.e., standardised linear discriminant analysis (SLDA) is used in the first stage. Subsequently, the second processing stage used Adaboost, random forest and recursive discriminant analysis (RDA) classifiers. The proposed e-nose was tested using 40 unknown VOCs/gases/odors samples that were not used during the training and validation. Experimentally, the RDA classifier trained in the SLDA transformed dataset could classify all 40 test samples with 100% accuracy. The lowest mean squared error achieved was 2.40 × 10–7.

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The associated data will be made available on reasonable request.

Abbreviations

IAQ:

Indoor air quality

IoT:

Internet-of-things

MAE:

Mean absolute error

MBE:

Mean bias error

MOS:

Metal oxide semiconductor

MSE:

Mean squared error

MQTT:

Message queuing telemetry transport

RDA:

Recursive discriminant analysis

RF:

Random forest

RMSE:

Root mean squared error

RMSLE:

Root mean squared logarithmic error

RRMSE:

Relative root mean squared error

SLDA:

Standardised linear discriminant analysis

VOC:

Volatile organic compounds

WHO:

World Health Organisation

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Acknowledgements

We acknowledge the support and guidance provided by IIT BHU, School of Electronics and Electrical Engineering, Lovely Professional University, India and Department of Computer Science, Nottingham Trent University, UK.

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Funding is not received for this work.

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Authors and Affiliations

  1. Department of Electronics Engineering, Indian Institute of Technology, BHU, Varanasi, India

    Kanak Kumar

  2. Lovely Professional University, Punjab, India

    Suman Lata Tripathi

  3. Department of Computer Science and Engineering, Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

Authors
  1. Kanak Kumar
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  2. Suman Lata Tripathi
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  3. Mufti Mahmud
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Contributions

Major experimental work has been performed and written by Kanak Kumar, material introduction and performance analysis is written by Dr. Suman Lata Tripathi and discussion and concluding remark are written by Dr. Mufti Mahmud.

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Correspondence to Kanak Kumar.

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Kumar, K., Tripathi, S.L. & Mahmud, M. Internet of Things Enabled Smart E-Nose System for Pollutants Hazard Detection and Real-Time Monitoring in Indoor Mosquito Repellents. SN COMPUT. SCI. 5, 438 (2024). https://doi.org/10.1007/s42979-024-02786-5

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