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2009 WRI World Congress on Computer Science and Information Engineering
Employing CHNN to Develop a Data Refining Algorithm for Wireless Sensor Networks
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
In this report a data refining algorithm (DRA) for obtaining the relationships between wireless sensor measurements and existing tracks is proposed. It is known that a DRA plays an important role in wireless sensors for target tracking over WSN (wireless sensor network) deployments. However, a new approach to data refining is here investigated, wherein the matching between mobile sensor measurements and existing target tracks can achieve global consideration. Embedded within the traditional HNN (Hopfield neural networks) is adopted. In this research, the network is guaranteed to converge into a stable state when performing a data association. The HNN-based DRA is combined with mobile sensors in a WSN system to demonstrate the target tracking capabilities. Finally, computer simulation results indicate that this approach successfully solves the data association problems addressed over WSN environments.
Index Terms:
CHNN (competitive Hopfield neural network), DFA (data fusion algorithm), mobile sensors, WSN (wireless sensor network)
Citation:
Joy Iong-Zong Chen, Chieh Chung Yu, Meng Tsun Hsieh, Yi Nung Chung, "Employing CHNN to Develop a Data Refining Algorithm for Wireless Sensor Networks," csie, vol. 1, pp.24-31, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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