Next Article in Journal
Crustal Electrical Structure of the Ganzi Fault on the Eastern Tibetan Plateau: Implications for the Role of Fluids in Earthquakes
Previous Article in Journal
Investigation of Long-Term Forest Dynamics in Protected Areas of Northeast China Using Landsat Data
 
 
Technical Note

Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study

1
Department of Informatics, Faculty of Science, University of Split, 21000 Split, Croatia
2
Department of Physics, Faculty of Science, University of Split, 21000 Split, Croatia
3
Physical Oceanography Laboratory, Institute of Oceanography and Fisheries, 21000 Split, Croatia
4
University Department of Marine Studies, University of Split, 21000 Split, Croatia
*
Author to whom correspondence should be addressed.
Academic Editors: M. Jamal Deen, Subhas Mukhopadhyay, Yangquan Chen, Simone Morais, Nunzio Cennamo and Junseop Lee
Remote Sens. 2022, 14(13), 2989; https://doi.org/10.3390/rs14132989
Received: 27 April 2022 / Revised: 8 June 2022 / Accepted: 17 June 2022 / Published: 22 June 2022
(This article belongs to the Topic Artificial Intelligence in Sensors)
In this paper, we discuss different approaches to optimal sensor placement and propose that an optimal sensor location can be selected using unsupervised learning methods such as self-organising maps, neural gas or the K-means algorithm. We show how each of the algorithms can be used for this purpose and that additional constraints such as distance from shore, which is presumed to be related to deployment and maintenance costs, can be considered. The study uses wind data over the Mediterranean Sea and uses the reconstruction error to evaluate sensor location selection. The reconstruction error shows that results deteriorate when additional constraints are added to the equation. However, it is also shown that a small fraction of the data is sufficient to reconstruct wind data over a larger geographic area with an error comparable to that of a meteorological model. The results are confirmed by several experiments and are consistent with the results of previous studies. View Full-Text
Keywords: optimal sensor placement; feature selection; unsupervised learning; clustering; self-organizing maps; neural gas; k-means optimal sensor placement; feature selection; unsupervised learning; clustering; self-organizing maps; neural gas; k-means
Show Figures

Figure 1

MDPI and ACS Style

Kalinić, H.; Ćatipović, L.; Matić, F. Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study. Remote Sens. 2022, 14, 2989. https://doi.org/10.3390/rs14132989

AMA Style

Kalinić H, Ćatipović L, Matić F. Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study. Remote Sensing. 2022; 14(13):2989. https://doi.org/10.3390/rs14132989

Chicago/Turabian Style

Kalinić, Hrvoje, Leon Ćatipović, and Frano Matić. 2022. "Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study" Remote Sensing 14, no. 13: 2989. https://doi.org/10.3390/rs14132989

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop