Enhance the efficient of WSN data fusion by neural networks training process

Author

Sung, Wen-Tsai ; Liu, Yu-Feng ; Chen, Jui-Ho ; Chen, Chia-Hao

Author_Institution

Dept. of Electr. Eng., Nat. Chin-Yi Univ. of Technol., Taiping, Taiwan

Volume

2

fYear

2010

fDate

5-7 May 2010

Firstpage

373

Lastpage

376

Abstract

Issues associated with data transmission in sensing networks via either cabling or single wireless medium are investigated, e.g. installation inconvenience, bad stability, etc. A sophisticated wireless network, wireless sensing network (WSN) possesses a large amount of nodes whereby a sensing base is formed. In this research, neuron concept and its mathematical model are used to depict network nodes of and represent the WSN system, and quasi-neural network idea is applied in WSN data fusion as well, which enables an agile, accurate and low-cost wireless data transmission system and heightens anti-interference capability in acquiring data. Finally, an adaptive self-learning integrated algorithm on data fusion computation for neural network is submitted, and practical simulation examples are further analyzed and discussed.

Keywords

computerised instrumentation; learning (artificial intelligence); neural nets; sensor fusion; wireless sensor networks; WSN data fusion; adaptive self-learning integrated algorithm; anti interference capability; data transmission; low cost wireless data transmission system; mathematical model; neural networks training process; quasineural network; Communication cables; Computational modeling; Computer networks; Data communication; Mathematical model; Neural networks; Neurons; Stability; Wireless networks; Wireless sensor networks; BP Neural Network; Data Fusion; wireless sensing network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communication Control and Automation (3CA), 2010 International Symposium on

Conference_Location

Tainan

Print_ISBN

978-1-4244-5565-2

Type

conf

DOI

10.1109/3CA.2010.5533439

Filename

5533439