Abstract In wireless sensor networks, one of most important issues is data collection from sensors to sink. Many researchers employ a mathematical formula to select the next fo rwarding node in the network%wide manner. We are mo tivated that surrounding environments for nodes are different in time and space. Because different situations of nodes are not considered for selecting the next forwarding node, the perform ance of data collection is degraded. In this paper, we present an intelligent approach for data collection in sensor networks. We model a nonlinear cost function for determining the next forwarder according to the input types whether inputs are cor related or uncorrelated for generating the output of the function. In our method, the correlated inputs are presented in a we ighted sum with the dependent fashion but the uncou pled inputs with an independent fashion in the nonlinear function. The weights in the functions are determined to the direction in which the reliability of data collection maximizes. In the ex perimental section, we show that our method outperf orms other conventional methods with respect to the efficiency in data coll ection from sensors to sink.

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[27] Zhao W., Liu D., and Jiang Y., Distributed Neural Network Routing Algorithm Based on Global Information of Wireless Sensor Network, in Proceedings of IEEE International Conference on Communications and Mobile Computing , China, pp. 5520555, 2009. Yujin Lim received her BSc and a MSc and a PhD degree in computer science from Sookmyung Women s University, Korea in 1995, 1997, and 2000 respectively. From 2000 to 2002 she worked as a research faculty at the Department of Mechanical and Information Engineering in the University of Seoul, Korea. She worked as a researc h staff at the Department of Computer Science in the University of California Los Angeles from 2002 to 2003. She worked for Samsung Advanced Institute of Technology as a senior research engineer from 2003 to 2004. She is currently an assistant professor in th e Department of information media, University of Suwon. Her current research interests include Ad0ho c and sensor networks, mesh networks, vehicular Ad0ho c network, and routing protocols over wireless environments. 42 The International Arab Journal of Information Techn ology, Vol. 10, No. 1, January 2013 Sanggil Kang received his MS and PhD degrees in Electrical Engineering from Columbia University and Syracuse University, USA in 1995 and 2002, respectively. He is currently a faculty in Computer Science at INHA University, Korea. His research interests include semantic web, artifi cial intelligence, multimedia systems, inference systems .