Energy Efficient Content Based Image Retrieval in Sensor Networks

Abstract

The presence of increased memory and computational power in imaging sensor networks attracts researchers to exploit image processing algorithms on distributed memory and computational power. In this paper, a typical perimeter is investigated with a number of sensors placed to form an image sensor network for the purpose of content based distributed image search. Image search algorithm is used to enable distributed content based image search within each sensor node. The energy model is presented to calculate energy efficiency for various cases of image search and transmission. The simulations are carried out based on consideration of continuous monitoring or event driven activity on the perimeter. The simulation setups consider distributed image processing on sensor nodes and results show that energy saving is significant if search algorithms are embedded in image sensor nodes and image processing is distributed across sensor nodes. The tradeoff between sensor life time, distributed image search and network deployed cost is also investigated.

Share and Cite:

Q. A. Memon and H. Alqamzi, "Energy Efficient Content Based Image Retrieval in Sensor Networks," International Journal of Communications, Network and System Sciences, Vol. 5 No. 7, 2012, pp. 405-415. doi: 10.4236/ijcns.2012.57050.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] D. M. Flournoy, “The Broadband Millennium: Communication Technologies and Markets,” International Engineering Consortium, Chicago, 2004.
[2] S. Tilak, N. Abu-Ghazaleh and W. Heinzelman, “A Taxonomy of Wireless Micro-Sensor Network Models,” ACM SIGMOBILE Mobile Computing and Communications Review, Vol. 6, No. 2, 2002, pp. 28-36.
[3] Y. H. Liu, C. Li, Y. He, J. Wu and Z. Xiong, “A Perimeter Intrusion Detection System Using Dual-Mode Wireless Sensor Networks,”Second International Conference on Communications and Networking, Shanghai, 22-24 August 2007, pp. 861-865. doi:10.1109/CHINACOM.2007.4469520
[4] A. Mishra, K. Nadkarni and A. Patcha, “Intrusion Detection in Wireless Ad Hoc Networks,” IEEE Wireless Communications Magazine, Vol. 11, No. 1, 2004, pp. 48-60.
[5] S. Olariu and J. V. Nickerson, “Protecting with Sensor Networks: Perimeters and Axes,” IEEE Military Communications Conference, Vol. 3, 2005, pp. 1780-1786.
[6] S. Hennin, G. Germana and L. Garcia, “Integrated Perimeter Security System,” IEEE Conference on Technologies for Homeland Security, Woburn, 16-17 May 2007, pp. 70-75. doi:10.1109/THS.2007.370022
[7] P. Skraba and L. Guibas, “Energy Efficient Intrusion Detection in Camera Sensor Networks,” Proceedings of the IEEE International Conference on Distributed Computing in Sensor Systems, Springer-Verlag, 18-20 June 2007, pp. 309-323.
[8] S. Sharafkandi, D. Du and A. Razavi, “Distributed and Energy Efficient Collection of Raw Data in Sensor Networks,” Technical Report, University of Minnesota, 2010.
[9] V. Lecuire, C. Duran-Faundez and N. Krommenacker, “Energy-Efficient Image Transmission in Sensor Networks,” International Journal of Sensor Networks, Vol. 4, No. 1-2, 2008, pp. 37-47. doi:10.1504/IJSNET.2008.019250
[10] C. Liu, K. Wu and J. Pei, “An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation,” IEEE Transactions on Parallel and Distributed Systems, Vol. 18, No. 7, 2007, pp. 1010-1023. doi:10.1109/TPDS.2007.1046
[11] S. Avancha, et al., “Secure Sensor Networks for Perimeter Protection,” Computer Networks, Vol. 43, No. 4, 2003, pp. 421-435. doi:10.1016/S1389-1286(03)00352-9
[12] M. Karakaya and H. Qi, “Distributed Target Localization using a Progressive Certainty Map in Visual Sensor Networks,” Ad Hoc Networks, Vol. 9, 2011, pp. 576-590. doi:10.1016/j.adhoc.2010.08.006
[13] Y. Wang and G. Cao, “On Full-View Coverage in Camera Sensor Networks,” Proceedings of IEEE on INFOCOM, Shangahi, 10-15 April 2011, pp. 1781-1789.
[14] S. Dewar, “Opportunities for Increased Use of Standards in the Integration of Perimeter Intrusion Detection Systems,”42nd Annual IEEE International Carnahan Conference on Security Technology, Prague, 13-16 October 2008, pp. 305-311. doi:10.1109/CCST.2008.4751319
[15] H. Everett, “Robotic Security Systems,” IEEE Instrumentation and Measurement Magazine, Vol. 6, No. 4, 2003, pp. 30-34. doi:10.1109/MIM.2003.1251480
[16] X. N. Liang and Y. Xiao, “Capturing Intrusions by Mobile Robot in Rectangular Perimeter,” IEEE International Conference on Information and Automation, Harbin, 20-23 June 2010, pp. 1404-1409. doi:10.1109/ICINFA.2010.5512096
[17] The Smart Detect WSN Team, “Smart Detect: An Efficient WSN Implementation for Intrusion Detection,” Second International Conference on Communication Systems and Networks, 18 March 2010, pp. 1-2.
[18] R. Koller, et al., “Anatomy of a Real-Time Intrusion Prevention System,” International Conference on Autonomic Computing, Chicago, 2-6 June 2008, pp. 151-160. doi:10.1109/ICAC.2008.24
[19] F. Uluc, E. Emirzade and Y. Bitirim, “The Impact of Number of Query Words on Image Search Engines,” International Conference on Internet and Web Applications and Services, Morne, 13-19 May 2007, p. 50. doi:10.1109/ICIW.2007.61
[20] R. Datta, et al., “Toward Bridging the Annotation-Retrieval Gap in Image Search,” IEEE Multimedia, Vol. 14, No. 3, 2007, pp. 24-35. doi:10.1109/MMUL.2007.67
[21] T. Evgeniou, et al., “Image Representations and Feature Selection for Multimedia Database Search,” IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 4, 2003, pp. 911-920. doi:10.1109/TKDE.2003.1209008
[22] D. Lowe, “Distinctive Features from Scale-Invariant Key-Points,” International Journal of Computer Vision, Vol. 60, No. 2, 2004, pp. 91-110. doi:10.1023/B:VISI.0000029664.99615.94
[23] D. Nister and H. Stewenius, “Scalable Recognition with a Vocabulary Tree,” IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, 2006, pp. 2161-2168.
[24] J. Sivic and A. Zissermann, “Video Google: A Text Retrieval Approach to Object Matching in Videos,” IEEE Conference on Computer Vision and Pattern Recognition, Nice, 13-16 October 2003, pp. 1470-1477.
[25] R. Baeza-Yates and B. Ribeiro-Neto, “Modern Information Retrieval,” ACM Press, New York, 1999.
[26] J. Philbin, et al., “Object Retrieval with Large Vocabularies and Fast Spatial Matching,” IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 17-22 June 2007, pp. 1-8. doi:10.1109/CVPR.2007.383172
[27] http://www.memsic.com/products/wireless-sensor-networks/sensor-boards.html
[28] S. Mallat, “A Wavelet Tour of Signal Processing,” 2nd edition, Academic Publishers, Leiden, 1999.
[29] J. L. Rubio-Guivernau, et al., “Wavelet-Based Image Fusion in Multi-View Three-Dimensional Microscopy,” Bioinformatics, Vol. 28, No. 2, 2011, pp. 238-245.
[30] V. Castelli and L. Bergman, “Image Databases: Search and Retrieval of Digital Imagery,” John Wiley and Sons, New York, 2002.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.