Tsapparellas, G, Jin, N, Dai, X and Fehringer, G (2020) Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support. Sensors, 20 (18). ISSN 1424-8220

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Abstract

Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based IoT systems, where data volume and frequency are constrained by the protocols. This paper presents an unsupervised learning approach using Laplacian scores to discover which types of sensors can be reduced, without compromising the decision-making. Here, a type of sensor is a feature. An IoT system is designed and implemented for a plant-monitoring scenario. We have collected data and carried out the Laplacian scores. The analytical results help choose the most important feature. A comparative study has shown that using fewer types of sensors, the accuracy of decision-making remains at a satisfactory level.

Item Type:	Article
Uncontrolled Keywords:	0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 0502 Environmental Science and Management, 0602 Ecology
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	Engineering
Publisher:	MDPI
Date Deposited:	10 Sep 2020 08:35
Last Modified:	04 Sep 2021 06:42
DOI or ID number:	10.3390/s20185107
URI:	https://researchonline.ljmu.ac.uk/id/eprint/13614

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