|Table of Contents|

Node Localization Algorithm of WSN Based on Fruit Flies Optimizationand Generalized Regression Neural Network(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年02期
Page:
31-
Research Field:
·数学·
Publishing date:

Info

Title:
Node Localization Algorithm of WSN Based on Fruit Flies Optimizationand Generalized Regression Neural Network
Author(s):
Chen Jing1; Yu Jimin2
(1.College of Mathematics and Computer Science,Guangxi Science & Technology Normal University,Laibin 546199,China)(2.Key Laboratory of Industrial Internet of Things & Network Control,MOE,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
Keywords:
wireless sensor networks; node localization; generalized regression neural networks; fruit fly optimization algorithm
PACS:
TP391.9
DOI:
10.3969/j.issn.1001-4616.2017.02.006
Abstract:
In order to improve the accuracy of WSN node location and avoid the deficiency of the distance measurement based localization algorithm using node coordinates to calculate the location of unknown nodes,a new WSN node localization algorithm of Fruit Flies Optimization-Generalized Regression Neural Network(FOA-GRNN)was presented. The proposed FOA-GRNN algorithm builds WSN positioning model by the fast learning speed and strong approximation ability of Generalized Regression Neural Network(GRNN)and adjusts GRNN’s smoothing parameter by using Fruit Flies Optimization Algorithm(FOA)to reduce the impact of human factors on selecting GRNN smoothing parameter to minimum. Finally,the coordinates of unknown nodes in WSN can be directly obtained from the output of FOA-GRNN model. Simulating results show that the localization accuracy of FOA-GRNN optimized by FOA is better than that of GRNN model. In addition,the FOA-GRNN algorithm was compared with BP algorithm and VNBP algorithm in WSN nodes localization. Simulating results further show the FOA-GRNN algorithm has obvious advantages in the accuracy of WSN node localization.

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Last Update: 2017-06-30