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.
Citations
IRE Journals:
Egboh Daniel Chukwunonso
"Pollution Prediction Using IoT Systems" Iconic Research And Engineering Journals Volume 8 Issue 8 2025 Page 413-420
IEEE:
Egboh Daniel Chukwunonso
"Pollution Prediction Using IoT Systems" Iconic Research And Engineering Journals, 8(8)
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