Home / Current Issue / Paper 1707180
Pollution Prediction Using IoT Systems
Subject area: Science,Engineering and Technology · Area of research: Information Technology
Abstract
Aquaculture has emerged as a critical sector in addressing global food security and the increasing demand for seafood. Effective management of aquaculture ponds is essential to ensure optimal growth and health of aquatic organisms. Temperature monitoring plays a vital role in understanding the pond's thermal dynamics, which directly impact the well-being of aquatic species. Wireless sensor networks (WSNs) have attracted a lot of attention recently. as a viable solution for real-time data collection in various domains, including aquaculture. This paper presents a study on data fusion techniques based on temperature monitoring of aquaculture ponds using WSNs. The primary objective is to develop a robust and accurate approach for acquiring and analysing temperature data from multiple sensors deployed in the pond environment. The proposed data fusion methodology combines data from different sensors to obtain a comprehensive and reliable representation of the pond's temperature profile. In this coursework, we will be carrying out three parts of the processing to include: a paper review, summarization and preparation of Data analysis, secondly, we shall carry out a time series analysis and prediction of the dataset, furthermore, we will we'll simulate real-time data from two distinct stations using the MQTT protocol, and we'll utilise Apache Flink to interpret real-time streams and complicated events. (CEP). The research focuses on the challenges associated with data collection, transmission, and fusion in an aquatic environment. The study investigates various WSN architectures, sensor placement strategies, and communication protocols suitable for aquaculture pond monitoring. Furthermore, it explores data fusion algorithms and techniques to integrate temperature readings from multiple sensors, considering factors such as sensor accuracy, spatial distribution, and temporal correlation.
References
[1] Himadri Nath Saha, Supratim Auddy, Avimita Chatterjee, Subrata Pal, Shivesh Pandey, Rocky Singh, Rakhee Singh, Priyanshu Sharan, Swarnadeep Banerjee, Debmalya Ghosh, Ankita Maity. (2017) “Pollution Control Using Internet of Things (IoT)”. 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON)
[2] Akshay Kajale, Prathamesh Inamdar, Vivek Bagad, Ankit Mhaske, Ameya Joshi, Prof. Pooja Dhule. (2018) “Cloud and IoT Enabled Smart Air Pollution Monitoring System”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 7, Issue 4.
[3] Ayaskanta Mishra. (2018) “Air Pollution Monitoring System based on IoT: Forecasting and Predictive Modelling using Machine Learning”. International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC).
[4] Gangwar, A., Singh, S., Mishra, R. et al. (2023). The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IoT, Big Data, and Machine Learning. Wireless Pers Commun 130, 1699–1729 https://doi-org.brad.idm.oclc.org/10.1007/s11277-023-10351-1
[5] Okokpujie, K., Noma-Osaghae, E., Modupe, O., John, S., & Oluwatosin, O. (2018). A smart air pollution monitoring system. Int J Civ Eng Technol, 9(9), 799–809.
[6] Barthwal, A., & Acharya, D. (2018). An internet of things system for sensing, analysis & forecasting urban air quality. In The IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT). India: Bangalore.
[7] Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836. https://doi-org.brad.idm.oclc.org/10.2307/2171846.
[8] Kiruthika, R., & Umamakeswari, A. (2017). Low-cost pollution control and air quality monitoring system using Raspberry Pi for the Internet of Things. In 2017 international conference on Energy, communication, data analytics and soft computing (ICECDS), Chennai (pp. 2319–2326).
[9] Noorian, F., & Leong, P. H. W. (2017). On-time series forecasting error measures for finite horizon control. IEEE Transactions on Control Systems Technology, 25(2), 736–743.
How to cite this paper
@article{1707180,
author = {Egboh Daniel Chukwunonso},
title = {Pollution Prediction Using IoT Systems},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {8},
pages = {413-420},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707180.pdf},
abstract = {Aquaculture has emerged as a critical sector in addressing global food security and the increasing demand for seafood. Effective management of aquaculture ponds is essential to ensure optimal growth and health of aquatic organisms. Temperature monitoring plays a vital role in understanding the pond's thermal dynamics, which directly impact the well-being of aquatic species. Wireless sensor networks (WSNs) have attracted a lot of attention recently. as a viable solution for real-time data collection in various domains, including aquaculture. This paper presents a study on data fusion techniques based on temperature monitoring of aquaculture ponds using WSNs. The primary objective is to develop a robust and accurate approach for acquiring and analysing temperature data from multiple sensors deployed in the pond environment. The proposed data fusion methodology combines data from different sensors to obtain a comprehensive and reliable representation of the pond's temperature profile. In this coursework, we will be carrying out three parts of the processing to include: a paper review, summarization and preparation of Data analysis, secondly, we shall carry out a time series analysis and prediction of the dataset, furthermore, we will we'll simulate real-time data from two distinct stations using the MQTT protocol, and we'll utilise Apache Flink to interpret real-time streams and complicated events. (CEP). The research focuses on the challenges associated with data collection, transmission, and fusion in an aquatic environment. The study investigates various WSN architectures, sensor placement strategies, and communication protocols suitable for aquaculture pond monitoring. Furthermore, it explores data fusion algorithms and techniques to integrate temperature readings from multiple sensors, considering factors such as sensor accuracy, spatial distribution, and temporal correlation.},
month = {February},
}