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  • Research Article
  • Open Access

Collaborative Image Coding and Transmission over Wireless Sensor Networks

EURASIP Journal on Advances in Signal Processing20062007:070481

  • Received: 6 February 2006
  • Accepted: 13 August 2006
  • Published:


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.


  • Sensor Network
  • Wireless Sensor Network
  • Visual Information
  • Imaging Sensor
  • Target Image

Authors’ Affiliations

MAKO Surgical Corporation, Fort Lauderdale, FL 33317, USA
Department of Electrical and Computer Engineering, Florida Institute of Technology (FIT), Melbourne, FL 32901, USA


  1. Estrin D, Girod L, Pottie G, Srivastava M: Instrumenting the world with wireless sensor networks. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '01), May 2001, Salt Lake City, Utah, USA 4: 2033–2036.Google Scholar
  2. Savarese C, Rabaey JM, Beutel J: Locationing in distributed ad-hoc wireless sensor networks. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '01), May 2001, Salt Lake City, Utah, USA 4: 2037–2040.Google Scholar
  3. Pradhan SS, Kusuma J, Ramchandran K: Distributed compression in a dense microsensor network. IEEE Signal Processing Magazine 2002,19(2):51–60. 10.1109/79.985684View ArticleGoogle Scholar
  4. Li D, Wong KD, Hu YH, Sayeed AM: Detection, classification, and tracking of targets. IEEE Signal Processing Magazine 2002,19(2):17–29. 10.1109/79.985674View ArticleGoogle Scholar
  5. Zhao YJ, Govindan R, Estrin D: Residual energy scan for monitoring sensor networks. Proceedings of IEEE Wireless Communications and Networking Conference (WCNC '02), March 2002, Orlando, Fla, USA 1: 356–362.Google Scholar
  6. Wagner R, Nowak R, Baraniuk R: Distributed image compression for sensor networks using correspondence analysis and super-resolution. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 1: 597–600.Google Scholar
  7. Liveris AD, Xiong Z, Georghiades CN: A distributed source coding technique for correlated images using turbo-codes. IEEE Communications Letters 2002,6(9):379–381. 10.1109/LCOMM.2002.803479View ArticleGoogle Scholar
  8. Girod B, Aaron A, Rane S, Rebollo-Monedero D: Distributed video coding. Proceedings of the IEEE 2005,93(1):71–83. IEEE special issues on advances in video coding and delivery, 2004View ArticleGoogle Scholar
  9. Zhu X, Aaron A, Girod B: Distributed compression for large camera arrays. Proceedings of IEEE Workshop on Statistical Signal Processing (SSP '03), September–October 2003, St. Louis, Mo, USA 30–33.Google Scholar
  10. Puri R, Ramchandran K: PRISM: a video coding architecture based on distributed compression principles. In Tech. Rep. UCB/ERL M03/6. EECS Department, University of California, Berkeley, Calif, USA; 2003. Scholar
  11. Akkaya K, Younis M: A survey on routing protocols for wireless sensor networks. Ad Hoc Networks 2005,3(3):325–349. 10.1016/j.adhoc.2003.09.010View ArticleGoogle Scholar
  12. Belongie S, Malik J, Puzicha J: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(4):509–522. 10.1109/34.993558View ArticleGoogle Scholar
  13. Bhargava R, Kargupta H, Powers M: Energy consumption in data analysis for on-board and distributed applications. Proceedings of the ICML Workshop on Machine Learning Technologies for Autonomous Space Applications, August 2003, Washington, DC, USAGoogle Scholar
  14. Akkaya K, Younis M: An energy-aware QoS routing protocol for wireless sensor networks. Proceedings of the 23rd International Conference on Distributed Computing Systems Workshops (ICDCSW '03), May 2003, Providence, RI, USA 710–715.Google Scholar
  15. Mainwaring A, Polastre J, Szewczyk R, Culler D, Anderson J: Wireless sensor networks for habitat monitoring. Proceedings of the ACM International Workshop on Wireless Sensor Networks and Applications (WSNA '02), September 2002, Atlanta, Ga, USA 88–97.View ArticleGoogle Scholar
  16. Ye W, Heidemann J, Estrin D: An energy-efficient MAC protocol for wireless sensor networks. Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '02), June 2002, New York, NY, USA 3: 1567–1576.Google Scholar
  17. Javed O, Shafique K, Shah M: A hierarchical approach to robust background subtraction using color and gradient information. Proceedings of IEEE Workshop on Motion and Video Computing (MOTION '02), December 2002, Orlando, Fla, USA 22–28.Google Scholar
  18. Mittal A, Huttenlocher D: Scene modeling for wide area surveillance and image synthesis. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), June 2000, Hilton Head Island, SC, USA 2: 160–167.Google Scholar
  19. Monnet A, Mittal A, Paragios N, Ramesh V: Background modeling and subtraction of dynamic scenes. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), June 2000, Hilton Head Island, SC, USAGoogle Scholar
  20. Ivanov Y, Bobick A, Liu J: Fast lighting independent background subtraction. International Journal of Computer Vision 2000,37(2):199–207. 10.1023/A:1008107805263View ArticleGoogle Scholar


© M.Wu and C.W. Chen 2007

This article is published under license to BioMed Central Ltd. 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.