Survey on IoT Based Microplastic Detection
DOI:
https://doi.org/10.32628/CSEIT26121319Keywords:
Introduction, Sources, IoT Technologies, Literature review, Conclusion, ReferencesAbstract
Microplastic pollution in water bodies has become a serious environmental concern due to its harmful effects on aquatic life and human health. These tiny plastic particles are difficult to detect using traditional methods, as they usually require manual water sampling and laboratory analysis, which are time-consuming and costly. Continuous and real-time monitoring of microplastics is therefore necessary for effective pollution control. This study provides a structured overview of current technological advancements and research gaps, offering valuable insights for the development of efficient, scalable, and intelligent underwater IoT systems for microplastic monitoring.
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C. F. Araujo, J. Sobral, I. C. Costa, and A. M. R. Prata, “Identification of microplastics using Raman spectroscopy: A review,” Science of the Total Environment, 2018 — review of Raman strengths/limits (gold standard for polymer ID).J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
Kataria, B., Jethva, H. B., Shinde, P. V., Banait, S. S., Shaikh, F., & Ajani, S. (2023). SLDEB: Design of a secure and lightweight dynamic encryption bio-inspired model for IoT networks. International Journal of Safety and Security Engineering, 13(2), 325–331. https://doi.org/10.18280/ijsse.130214 DOI: https://doi.org/10.18280/ijsse.130214
B. Shi, et al., “Automatic quantification and classification of microplastics using deep learning,” Science of the Total Environment, 2022 — deep-learning segmentation & quantification work. DOI: https://doi.org/10.1016/j.scitotenv.2022.153903
E. S. Jung, et al., “Quantitative Raman analysis of microplastics in water,” npj / Nature family (quantitative Raman methods), 2024 — example of Raman calibration for concentration estimation.
M. A. B. Sarker, et al., “Real-Time Detection of Microplastics Using an AI Camera,” Sensors (MDPI), 2024 — camera + AI (real-time) prototype, useful for YOLO/vision section. DOI: https://doi.org/10.3390/s24134394
J. Lorenzo-Navarro, et al., “Deep learning approach for automatic microplastics counting and classification,” (preprint / STOTEN related work), 2021 — practical architecture for automated counting from images. DOI: https://doi.org/10.1016/j.scitotenv.2020.142728
Kusuma, Kranthi Kiran. (2025). Impact Analysis of VoLTE and NB-IoT Implementation on Carrier Network Performance. 5. 104-110. 10.56472/25832646/JETA-V5I2P112.
Mishra, Chandan. (2025). Utilizing PeopleSoft Test Framework (PTF) for Efficient System Testing. International Journal of Research and Analytical Reviews. 12. 10.56975/ijrar.v12i3.319332. DOI: https://doi.org/10.56975/ijrar.v12i3.319332
Pal, P. K., Kataria, B., & Jangid, J. (2025). AI-Driven Multimodal Ensemble Framework for Accurate Hardware Failure Detection in Optical Embedded Systems: Eliminating Unnecessary RMAs. Preprints. https://doi.org/10.20944/preprints202512.1937.v1 DOI: https://doi.org/10.20944/preprints202512.1937.v1
Pal, P. K., & Jangid, J. (2025). Reinforcement Learning Based Adaptation for Enhanced Point-to-Point Optical Link Performance DOI: https://doi.org/10.36227/techrxiv.176306428.85921550/v1
I. Chakraborty, “Raman spectroscopy for microplastic detection in water sources: A systematic review,” Environmental Science and Pollution Research, 2023 — systematic review on Raman applied to various water matrices.
P. Govindarajan, et al., “Real-time IoT monitoring for water quality and public health” (includes turbidity-based IoT concepts / TEMPT prototype), ScienceDirect/IoT systems, 2025 — example IoT/turbidity screening system for continuous monitoring.
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