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Designing an IoT-cloud solution for precision aquaculture

In recent years, the demand of aquaculture product is increasing due to the growing population of the world and the depletion of wild fish population. To address the depleting fish stocks caused by overfishing and environment pollution, aquaculture will become an alternate source to supply fish s...

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Main Author:	Liew, Wei Zheng
Format:	Final Year Project / Dissertation / Thesis
Published:	2023
Subjects:	S Agriculture (General) T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utar.edu.my/5993/1/fyp_IA_2023_LWZ.pdf http://eprints.utar.edu.my/5993/
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id	my-utar-eprints.5993
record_format	eprints
spelling	my-utar-eprints.59932024-01-02T15:32:24Z Designing an IoT-cloud solution for precision aquaculture Liew, Wei Zheng S Agriculture (General) T Technology (General) TA Engineering (General). Civil engineering (General) In recent years, the demand of aquaculture product is increasing due to the growing population of the world and the depletion of wild fish population. To address the depleting fish stocks caused by overfishing and environment pollution, aquaculture will become an alternate source to supply fish stocks to fulfil the demand. Aquaculture has been evolved throughout times by utilizing different technology to improve efficiency and increase production. But, even with the implementation of technology, aquaculture industry is still vulnerable to factor such as labour shortage and inaccurate data analysis. In this case, water quality needs to be monitor periodically because it plays an important role in determine the health and growth of fish. Measuring water quality involve fish farmer collect sample from ponds and pass them to an examiner to perform water testing. The accuracy of the analyse result are determine by the experience of the examiner and prone to human error. Thus, this project designs an IoT-Cloud Solution for Precision Aquaculture in predicting water quality to improve the efficiency and effectiveness of aquaculture operations to increase the overall production. In this paper, an aquaculture farm will be referred as an edge computing environment consisting of IoT sensors and devices used to obtain water parameters from the farm, processed them and store them locally as well as send to cloud for other uses. Sensors of various purpose are used to collect real time water parameter so that the data can be transmit to cloud for analysis via edge device using MQTT protocol. Edge computing can be used to process data locally, reducing the bandwidth requirements for transmitting large amounts of data to the cloud and also reduces the latency in receiving results from the analysis from cloud. While the cloud can store the data as a backup in unstructured format and later serves as data for visualise graphically on a dashboard. The federated learning framework also involves, employing a client-server architecture. In this setup, the edge environment functions as the client, which trains a local machine learning model using local dataset. Meanwhile, the server aggregates the model weights from multiple clients to create a more powerful, global model without accessing the individual client's data. The trained model from the server is then redistributed back to the clients. This process allows clients to have an improved model while ensuring the privacy of their data remains intact. 2023-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5993/1/fyp_IA_2023_LWZ.pdf Liew, Wei Zheng (2023) Designing an IoT-cloud solution for precision aquaculture. Final Year Project, UTAR. http://eprints.utar.edu.my/5993/
institution	Universiti Tunku Abdul Rahman
building	UTAR Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tunku Abdul Rahman
content_source	UTAR Institutional Repository
url_provider	http://eprints.utar.edu.my
topic	S Agriculture (General) T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	S Agriculture (General) T Technology (General) TA Engineering (General). Civil engineering (General) Liew, Wei Zheng Designing an IoT-cloud solution for precision aquaculture
description	In recent years, the demand of aquaculture product is increasing due to the growing population of the world and the depletion of wild fish population. To address the depleting fish stocks caused by overfishing and environment pollution, aquaculture will become an alternate source to supply fish stocks to fulfil the demand. Aquaculture has been evolved throughout times by utilizing different technology to improve efficiency and increase production. But, even with the implementation of technology, aquaculture industry is still vulnerable to factor such as labour shortage and inaccurate data analysis. In this case, water quality needs to be monitor periodically because it plays an important role in determine the health and growth of fish. Measuring water quality involve fish farmer collect sample from ponds and pass them to an examiner to perform water testing. The accuracy of the analyse result are determine by the experience of the examiner and prone to human error. Thus, this project designs an IoT-Cloud Solution for Precision Aquaculture in predicting water quality to improve the efficiency and effectiveness of aquaculture operations to increase the overall production. In this paper, an aquaculture farm will be referred as an edge computing environment consisting of IoT sensors and devices used to obtain water parameters from the farm, processed them and store them locally as well as send to cloud for other uses. Sensors of various purpose are used to collect real time water parameter so that the data can be transmit to cloud for analysis via edge device using MQTT protocol. Edge computing can be used to process data locally, reducing the bandwidth requirements for transmitting large amounts of data to the cloud and also reduces the latency in receiving results from the analysis from cloud. While the cloud can store the data as a backup in unstructured format and later serves as data for visualise graphically on a dashboard. The federated learning framework also involves, employing a client-server architecture. In this setup, the edge environment functions as the client, which trains a local machine learning model using local dataset. Meanwhile, the server aggregates the model weights from multiple clients to create a more powerful, global model without accessing the individual client's data. The trained model from the server is then redistributed back to the clients. This process allows clients to have an improved model while ensuring the privacy of their data remains intact.
format	Final Year Project / Dissertation / Thesis
author	Liew, Wei Zheng
author_facet	Liew, Wei Zheng
author_sort	Liew, Wei Zheng
title	Designing an IoT-cloud solution for precision aquaculture
title_short	Designing an IoT-cloud solution for precision aquaculture
title_full	Designing an IoT-cloud solution for precision aquaculture
title_fullStr	Designing an IoT-cloud solution for precision aquaculture
title_full_unstemmed	Designing an IoT-cloud solution for precision aquaculture
title_sort	designing an iot-cloud solution for precision aquaculture
publishDate	2023
url	http://eprints.utar.edu.my/5993/1/fyp_IA_2023_LWZ.pdf http://eprints.utar.edu.my/5993/
_version_	1787140947436371968
score	13.211869

Designing an IoT-cloud solution for precision aquaculture

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