Title :
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
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;
Conference_Titel :
Computer Communication Control and Automation (3CA), 2010 International Symposium on
Conference_Location :
Tainan
Print_ISBN :
978-1-4244-5565-2
DOI :
10.1109/3CA.2010.5533439
