» Articles » PMID: 34883973

Real-Time Environmental Monitoring for Aquaculture Using a LoRaWAN-Based IoT Sensor Network

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2021 Dec 10
PMID 34883973
Citations 4
Authors
Affiliations
Soon will be listed here.
Abstract

IoT-enabled devices are making it easier and cheaper than ever to capture in situ environmental data and deliver these data-in the form of graphical visualisations-to farmers in a matter of seconds. In this work we describe an aquaculture focused environmental monitoring network consisting of LoRaWAN-enabled atmospheric and marine sensors attached to buoys on Clyde River, located on the South Coast of New South Wales, Australia. This sensor network provides oyster farmers operating on the river with the capacity to make informed, accurate and rapid decisions that enhance their ability to respond to adverse environmental events-typically flooding and heat waves. The system represents an end-to-end approach that involves deploying a sensor network, analysing the data, creating visualisations in collaboration with farmers and delivering them to them in real-time via a website known as FarmDecisionTECH®. We compared this network with previously available infrastructure, the results of which demonstrate that an in situ weather station was ∼5 ∘C hotter than the closest available real-time weather station (∼20 km away from Clyde River) during a summertime heat wave. Heat waves can result in oysters dying due to exposure if temperatures rise above 30 ∘C for extended periods of time (such as heat waves), which will mean a loss in income for the farmers; thus, this work stresses the need for accurate in situ monitoring to prevent the loss of oysters through informed farm management practices. Finally, an approach is proposed to present high-dimensional datasets captured from the sensor network to oyster farmers in a clear and informative manner.

Citing Articles

Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight.

Yu H, Dai K, Li Q, Li H, Zhang H Sensors (Basel). 2023; 23(17).

PMID: 37687852 PMC: 10490538. DOI: 10.3390/s23177397.


Mobility of LoRaWAN Gateways for Efficient Environmental Monitoring in Pristine Sites.

Sobhi S, Elzanaty A, Selim M, Ghuniem A, Abdelkader M Sensors (Basel). 2023; 23(3).

PMID: 36772736 PMC: 9919836. DOI: 10.3390/s23031698.


Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture.

Thilakarathne N, Abu Bakar M, Abas P, Yassin H Front Plant Sci. 2023; 13:1030168.

PMID: 36684733 PMC: 9846789. DOI: 10.3389/fpls.2022.1030168.


CEBA: A Data Lake for Data Sharing and Environmental Monitoring.

Sarramia D, Claude A, Ogereau F, Mezhoud J, Mailhot G Sensors (Basel). 2022; 22(7).

PMID: 35408347 PMC: 9003009. DOI: 10.3390/s22072733.

References
1.
Sorensen J, Lapworth D, Marchant B, Nkhuwa D, Pedley S, Stuart M . In-situ tryptophan-like fluorescence: A real-time indicator of faecal contamination in drinking water supplies. Water Res. 2015; 81:38-46. DOI: 10.1016/j.watres.2015.05.035. View

2.
Codeluppi G, Cilfone A, Davoli L, Ferrari G . LoRaFarM: A LoRaWAN-Based Smart Farming Modular IoT Architecture. Sensors (Basel). 2020; 20(7). PMC: 7180486. DOI: 10.3390/s20072028. View

3.
Basford P, Bulot F, Apetroaie-Cristea M, Cox S, Ossont S . LoRaWAN for Smart City IoT Deployments: A Long Term Evaluation. Sensors (Basel). 2020; 20(3). PMC: 7038353. DOI: 10.3390/s20030648. View

4.
Losasso C, Bille L, Patuzzi I, Lorenzetto M, Binato G, Dalla Pozza M . Possible influence of natural events on heavy metals exposure from shellfish consumption: a case study in the north-East of Italy. Front Public Health. 2015; 3:21. PMC: 4316607. DOI: 10.3389/fpubh.2015.00021. View

5.
Bates H, Zavafer A, Szabo M, Ralph P . A guide to Open-JIP, a low-cost open-source chlorophyll fluorometer. Photosynth Res. 2019; 142(3):361-368. DOI: 10.1007/s11120-019-00673-2. View