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Sensors 2015, 15(11), 28314-28339; doi:10.3390/s151128314

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil

1
National Engineering and Technology Center for Agriculture/Jiangsu Key Laboratory for Information Agriculture/Collaborative Innovation Center for Modern Crop Production, Nanjing Agriculture University, Nanjing 210095, China
2
Jiangsu Collaborative Innovation Center for the Technology and Application of Internet of Things, Nanjing 210095, China
3
Jiangsu Key Laboratory for Eco-Agricultural Biotechnology around Hongze Lake/Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Huaiyin Normal University, Huai’an 223300, China
4
Nanjing Institute of Agricultural Mechanization of National Ministry of Agriculture, Nanjing 210014, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 21 August 2015 / Revised: 20 October 2015 / Accepted: 4 November 2015 / Published: 11 November 2015
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [2768 KB, uploaded 11 November 2015]   |  

Abstract

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters. View Full-Text
Keywords: intelligent sensor network; deployment; farmland soil differences; coverage degree; cost; fuzzy c-means clustering; normalized intra-cluster coefficient of variation intelligent sensor network; deployment; farmland soil differences; coverage degree; cost; fuzzy c-means clustering; normalized intra-cluster coefficient of variation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, N.; Cao, W.; Zhu, Y.; Zhang, J.; Pang, F.; Ni, J. The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil. Sensors 2015, 15, 28314-28339.

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