A PSO-BPNN-based model for energy saving in wireless sensor networks

Author

Lin, Jia-wen ; Guo, Wen-Zhong ; Chen, Guo-Long ; Gao, Hong-lei ; Fang, Xiao-tong

Author_Institution

Coll. of Math. & Comput. Sci., Fuzhou Univ., Fuzhou, China

Volume

2

fYear

2009

fDate

12-15 July 2009

Firstpage

948

Lastpage

952

Abstract

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes. In this paper, we propose an energy-efficient model based on improved BP neural network by particle swarm optimization (PSO-BPNN) in WSNs. The global optimized initial weights and threshold of BP network are obtained by PSO. And then PSO-BPNN is deployed at both the base station (BS) and the node in WSNs, helps to find out potential laws according to historical data sets. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on ocean surface temperature 2008 made a satisfied performance. When the error threshold greater than 0.05degC, it can decrease more than 80% data transmissions.

Keywords

backpropagation; neural nets; particle swarm optimisation; wireless sensor networks; BPNN; PSO; backpropagation neural network; energy saving; particle swarm optimization; wireless sensor network; Base stations; Data communication; Energy efficiency; Neural networks; Ocean temperature; Particle swarm optimization; Predictive models; Sampling methods; Sea surface; Wireless sensor networks; BP neural network; Energy saving; Particle swarm optimization; Wireless sensor networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212410

Filename

5212410