Citation

Kok, Adrian Eng Hock and Chan, Yee Kit and Koo, Voon Chet (2021) Development of real time metrological grade monitoring stations with AI analytics. In: 2nd FET PG Engineering Colloquium Proceedings 2021, 1-15 Dec. 2021, Online Conference. (Unpublished)

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Abstract

A selection of particle measurement sensors studied and correlated with referenced to metrological grade sensors provides a traceable of accuracy of the particle sensors being deployed. Further Kalman filtering techniques was also used to normalise and reduce errors on chosen sensors. Hardware designed based on Internet of Things based (IOT) architecture so that the sensors can be deployed to almost any locations using Wi-Fi, 4G and also Lora communication protocols. Backend server side scripting takes care of the overall dashboard view and analysis of all connected sensor nodes. As more sensors nodes are being deployed, huge amount of data is fed to an Artificial intelligence modelling to forecast pollution indicator to give an early warning system.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	PM2.5,/PM10, Embedded IOT, AI Algorithm, Real Time, Algorithms
Subjects:	Q Science > QA Mathematics > QA1-43 General
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	26 Jan 2022 01:26
Last Modified:	26 Jan 2022 01:26
URII:	http://shdl.mmu.edu.my/id/eprint/9884

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