Collaborative Image Coding and Transmission over Wireless Sensor Networks

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.

[1]  Hillol Kargupta,et al.  Energy Consumption in Data Analysis for On-board and Distributed Applications , 2003 .

[2]  Robert D. Nowak,et al.  Distributed image compression for sensor networks using correspondence analysis and super-resolution , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[3]  Bernd Girod,et al.  Distributed Video Coding , 2005, Proceedings of the IEEE.

[4]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[5]  Gregory J. Pottie,et al.  Instrumenting the world with wireless sensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[6]  Zixiang Xiong,et al.  A distributed source coding technique for correlated images using turbo-codes , 2002, IEEE Communications Letters.

[7]  Kannan Ramchandran,et al.  Distributed compression in a dense microsensor network , 2002, IEEE Signal Process. Mag..

[8]  Nikos Paragios,et al.  Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[10]  Daniel P. Huttenlocher,et al.  Scene modeling for wide area surveillance and image synthesis , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[11]  Kannan Ramchandran,et al.  PRISM: A video coding archi-tecture based on distributed compression principles , 2003 .

[12]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[13]  Deborah Estrin,et al.  Residual energy scan for monitoring sensor networks , 2002, 2002 IEEE Wireless Communications and Networking Conference Record. WCNC 2002 (Cat. No.02TH8609).

[14]  Aaron F. Bobick,et al.  Fast Lighting Independent Background Subtraction , 2004, International Journal of Computer Vision.

[15]  Mohamed F. Younis,et al.  An energy-aware QoS routing protocol for wireless sensor networks , 2003, 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings..

[16]  Bernd Girod,et al.  Distributed compression for large camera arrays , 2004, IEEE Workshop on Statistical Signal Processing, 2003.

[17]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[18]  Jan M. Rabaey,et al.  Location in distributed ad-hoc wireless sensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[19]  Chris Savarese.  LOCATIONING IN DISTRIBUTED AD-HOC WIRELESS SENSOR NETWORKS , 2001 .