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Energy Efficiency and Reliability Considerations of a Compressive Sensing Technique in Wireless Visual Sensor Networks

Energy Efficiency and Reliability Considerations of a Compressive Sensing Technique in Wireless Visual Sensor Networks

Yinhao Ding, Cheng-Chew Lim
Copyright: © 2012 |Pages: 19
ISBN13: 9781613501535|ISBN10: 1613501544|EISBN13: 9781613501542
DOI: 10.4018/978-1-61350-153-5.ch002
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MLA

Ding, Yinhao, and Cheng-Chew Lim. "Energy Efficiency and Reliability Considerations of a Compressive Sensing Technique in Wireless Visual Sensor Networks." Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications, edited by Li-Minn Ang and Kah Phooi Seng, IGI Global, 2012, pp. 21-39. https://doi.org/10.4018/978-1-61350-153-5.ch002

APA

Ding, Y. & Lim, C. (2012). Energy Efficiency and Reliability Considerations of a Compressive Sensing Technique in Wireless Visual Sensor Networks. In L. Ang & K. Seng (Eds.), Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications (pp. 21-39). IGI Global. https://doi.org/10.4018/978-1-61350-153-5.ch002

Chicago

Ding, Yinhao, and Cheng-Chew Lim. "Energy Efficiency and Reliability Considerations of a Compressive Sensing Technique in Wireless Visual Sensor Networks." In Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications, edited by Li-Minn Ang and Kah Phooi Seng, 21-39. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-153-5.ch002

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Abstract

This chapter focuses on the energy efficiency and reliability issues when applying the novel compressive sensing technique in wireless visual sensor networks. An explanation is given for why compressive sensing is useful for visual sensor networks. The relationships between sparsity control and compression ratio, the effect of block-based sampling on reconstruction quality, complexity consideration of reconstruction process for real-time applications, and compensation for packets missing in network flows are discussed. We analyse the effectiveness of using the 2-dimensional Haar wavelet transform for sparsity control, the difference between compressive sampling in spatial and frequency domains, and the computation of the prime-dual optimisation method and the log barrier algorithm for reconstruction. The effectiveness of the approach on recovered image quality is evaluated using Euclidean distance and variance analysis.

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