Collaborative Image Coding and Transmission over Wireless Sensor Networks
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Research Article Collaborative Image Coding and Transmission over Wireless Sensor Networks Min Wu1 and Chang Wen Chen2 1 MAKO
Surgical Corporation, Fort Lauderdale, FL 33317, USA of Electrical and Computer Engineering, Florida Institute of Technology (FIT), Melbourne FL32901, USA
2 Department
Received 6 February 2006; Revised 3 August 2006; Accepted 13 August 2006 Recommended by Chun-Shien Lu The imaging sensors are able to provide intuitive visual information for quick recognition and decision. However, imaging sensors usually generate vast amount of data. Therefore, processing and coding of image data collected in a sensor network for the purpose of energy efficient transmission poses a significant technical challenge. In particular, multiple sensors may be collecting similar visual information simultaneously. We propose in this paper a novel collaborative image coding and transmission scheme to minimize the energy for data transmission. First, we apply a shape matching method to coarsely register images to find out maximal overlap to exploit the spatial correlation between images acquired from neighboring sensors. For a given image sequence, we transmit background image only once. A lightweight and efficient background subtraction method is employed to detect targets. Only the regions of target and their spatial locations are transmitted to the monitoring center. The whole image can then be reconstructed by fusing the background and the target images as well as their spatial locations. Experimental results show that the energy for image transmission can indeed be greatly reduced with collaborative image coding and transmission. Copyright © 2007 M. Wu and C. W. Chen. 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.
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INTRODUCTION
Networked microsensor technology is becoming one of the key technologies for the 21st century. Such sensor networks are often designed to perform tasks such as detecting, classifying, localizing, and tracking of one or more targets in the sensor fields [1, 2]. Among all types of sensors, the imaging sensors are able to provide intuitive visual information for quick recognition and decision. However, imaging sensors usually generate vast amount of image data. Therefore, for battery-powered sensors, the transmission of image data collected in a sensor network presents the most challenging problem. A number of research efforts are currently under way to address the issues on collaborative signal and information processing in distributed microsensor networks [3–6]. Pradhan et al. proposed a distributed coding framework to realize the coding gain of correlated data from Slepian-Wolf coding theorem in information theory [3]. Ideally, no information needs to be exchanged among correlated sensors during the encoding process. At the decoder, data can be recovered by reaping the full benefit of the correlation between neighbor-
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