Abstract
Analyses of the presence of heavy metals are particularly important in assessing water quality. The monitoring of environmental parameters leads to the collection and analysis of a large set of data that may be used to reduce polluting actions, suggesting that Internet of Things (IoT) technology may provide an efficient contribution to the mitigation of environmental issues. The objective of this paper is to choose the best Machine Learning (ML) model, which can be loaded into a low-power microcontroller, using the sensed data obtained through the Electrochemical Impedance Spectroscopy (EIS) technique as a dataset. The real and imaginary parts of the impedance for each frequency value occurring within the range of 0.18 and 1 Hz are used as features. Five different lead concentrations are used as outputs. The features and the output constitute the dataset. Sixteen distinct scenarios were examined to train the different models in order to investigate ways to reduce the number of features. With the aim of creating a low-energy device that can conduct measurements and predict outcomes locally, the emphasis is on training a variety of models, including qualitative (classification) and quantitative (regression) approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Merenda M, Porcaro C, Iero D (2020) Edge machine learning for ai-enabled IoT devices: a review. Sensors 20:2533
Wang S-L, Hsieh C-Y, Sukesan R, Chen J-C, Wang Y-L (2020) Highly sensitive lead ion detection in one drop of human whole blood using impedance-modulated field-effect transistors and a portable measurement device. ECS J Solid State Sci Technol 9:055020
Malara A, Fotia A, Paone E, Serrano G (2021) Electrospun nanofibers and electrochemical techniques for the detection of heavy metal Ions. Materials 14. https://doi.org/10.3390/ma14113000
Huang Y, Miao YE, Liu T (2014) Electrospun fibrous membranes for efficient heavy metal removal. J Appl Polym Sci 131:1–12. https://doi.org/10.1002/app.40864
Liu Y, Deng Y, Dong H, Liu K, He N (2017) Progress on sensors based on nanomaterials for rapid detection of heavy metal ions. Sci China Chem 60:329–337. https://doi.org/10.1007/s11426-016-0253-2
Fotia A et al (2021) Self standing mats of blended polyaniline produced by electrospinning. Nanomaterials 11:1–15. https://doi.org/10.3390/nano11051269
Lazzaro A, D’Addona DM, Merenda M (2022) Comparison of machine learning models for predictive maintenance applications. In: Advances in system-integrated intelligence. SYSINT 2022. Lecture notes in networks and systems, pp 657–666
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fotia, A., Macheda, A., Sebti, M.R., Nunnari, C., Merenda, M. (2024). Design of a Portable Water Pollutants Detector Exploiting ML Techniques Suitable for IoT Devices Integration. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_51
Download citation
DOI: https://doi.org/10.1007/978-3-031-48121-5_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48120-8
Online ISBN: 978-3-031-48121-5
eBook Packages: EngineeringEngineering (R0)