Greenhouse wireless sensor network localization method based on similarity

Abstract: With the development of horticulture facilities technology, a single greenhouse area is constantly expanding, which is advantageous to save material, reduce costs, improve lighting efficiency, and improve cultivation efficiency, but at the same time, it means there is the need to deploy a large number of sensor nodes in order to guarantee the coverage of environmental monitoring. Dynamic monitoring of a greenhouse environment using mobile nodes, cannot only reduce the number of sensor nodes, but also ensures the comprehensiveness of greenhouse environmental information. The mobile node localization is the basis of the application. Node localization information is accurate or not directly related to the validity of the data collected. As a greenhouse mobile node has to compute and complement easily, a kind of greenhouse wireless sensor network localization method based on similarity was presented. The approach mainly included three stages: grid partitioning, measure distances amendment, and node localization. First, according to the distribution information of the beacon node, a greenhouse area was clustered into equal parts as virtual gridding and coordinates of gridding vertices inside the area boundary were returned to sink node. Secondly, by comparing measured distance and actual distance between each beacon node, sink node could get beacon node error coefficients, which was able to modify the measured distance between sensor node and each beacon node, and then form distance vectors in sequence. Finally, the similarity of distance vectors obtained in a second procedure and the distance vectors between gridding vertices and each beacon node was quantified, afterwards choosong the barycenter of gridding vertices with maximum similarity as a sensor node estimated position. The simulation experiment result showed that the greenhouse wireless sensor network localization method was fully considering the effect of distance measurement error, virtual grid partitioning, and the number of beacon nodes on localization errors. The method has a high ability of stability and precision, and meets the practical needs of greenhouse localization. In the same case of beacon node number and arrangement, the greenhouse wireless sensor network localization method and support vector machine (SVM) algorithm was compared. The average localization error was 2.5407 m and 2.9195 m respectively, the average elapsed time of localization algorithm was 0.2326 s and 2.3719 s respectively, and the localization error range was 3.5496 m and 4.0617 m respectively. The comparison results showed the method had a lower localization error and computational complexity.