Laplacian scores-based feature reduction in iot systems for agricultural monitoring and decision-making support

Giorgos Tsapparellas, Nanlin Jin*, Xuewu Dai, Gerhard Fehringer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number5107
Pages (from-to)1-18
Number of pages18
JournalSensors (Switzerland)
Issue number18
Publication statusPublished - 2 Sept 2020
Externally publishedYes


  • Data reduction
  • Internet of Things (IoT)
  • Laplacian scores
  • LoRaWAN
  • Sensors

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