Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support

Tsapparellas, Giorgos, Jin, Nanlin, Dai, Xuewu and Fehringer, Gerhard (2020) Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support. Sensors, 20 (18). p. 5107. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s20185107

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: Laplacian scores, data reduction, sensors, Internet of Things (IoT), LoRaWAN
Subjects: G400 Computer Science
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 13 Nov 2020 10:59
Last Modified: 13 Nov 2020 11:00
URI: http://nrl.northumbria.ac.uk/id/eprint/44751

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