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Báo cáo hóa học: " Research Article Ring-Based Optimal-Level Distributed Wavelet Transform with Arbitrary Filter Length for Wireless Sensor Networks"

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 Ring-Based Optimal-Level Distributed Wavelet Transform with Arbitrary Filter Length for Wireless Sensor Networks | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 396126 11 pages doi 10.1155 2008 396126 Research Article Ring-Based Optimal-Level Distributed Wavelet Transform with Arbitrary Filter Length for Wireless Sensor Networks Siwang Zhou 1 Yaping Lin 1 and Yonghe Liu2 1 School of Software Hunan University Changsha 410082 China 2 Department of Computer Science and Engineering The University of Texas at Arlington Arlington TX 76019 USA Correspondence should be addressed to Yaping Lin yplin@hnu.cn Received 1 May 2007 Revised 31 August 2007 Accepted 8 November 2007 Recommended by Huaiyu Dai We propose an optimal-level distributed transform for wavelet-based spatiotemporal data compression in wireless sensor networks. Although distributed wavelet processing can efficiently decrease the amount of sensory data it introduces additional communication overhead as the sensory data needs to be exchanged in order to calculate the wavelet coefficients. This tradeoff is explored in this paper with the optimal transforming level of wavelet transform. By employing a ring topology our scheme is capable of supporting a broad scope of wavelets rather than specific ones and the border effect generally encountered by wavelet-based schemes is also eliminated naturally. Furthermore the scheme can simultaneously explore the spatial and temporal correlations among the sensory data. For data compression in wireless sensor networks in addition to minimizing energy and consumption it is also important to consider the delay and the quality of reconstructed sensory data which is measured by the ratio of signal to noise PSNR . We capture this with energy xdelay PSNR metric and using it to evaluate the performance of the proposed scheme. Theoretically and experimentally we conclude that the proposed algorithm can effectively explore the spatial and temporal correlation in the sensory data and provide significant reduction in energy and delay cost .