Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Low-Complexity One-Dimensional Edge Detection in Wireless Sensor Networks | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010 Article ID 751520 13 pages doi 2010 751520 Research Article Low-Complexity One-Dimensional Edge Detection in Wireless Sensor Networks Marco Martalo and Gianluigi Ferrari WASN Laboratory Department of Information Engineering University of Parma I-43124 Parma Italy Correspondence should be addressed to Marco Martalo Received 16 February 2010 Accepted 26 May 2010 Academic Editor Osvaldo Simeone Copyright 2010 M. Martalo and G. Ferrari. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. In various wireless sensor network applications it is of interest to monitor the perimeter of an area of interest. For example one may need to check if there is a leakage of a dangerous substance. In this paper we model this as a problem of one-dimensional edge detection that is detection of a spatially nonconstant one-dimensional phenomenon observed by sensors which communicate to an access point AP through possibly noisy communication links. Two possible quantization strategies are considered at the sensors i binary quantization and ii absence of quantization. We first derive the minimum mean square error MMSE detection algorithm at the AP. Then we propose a simplified suboptimum detection algorithm with reduced computational complexity. Noisy communication links are modeled either as i binary symmetric channels BSCs or ii channels with additive white Gaussian noise AWGN . 1. Introduction and Related Work Sensor networks have been an active research field in the last years 1 . In particular many civilian applications have been developed on the basis of this technology for example for environmental monitoring 2 . Several frameworks have been proposed for the analysis of sensor networks with a common
