Energy-efficient wavelet image compression in Wireless Sensor Network

In the recent years, the wireless technology would have known an exponential growth, which has an impact on developing and improving the field of telecommunications beyond the means of transmission wire to the radio frequency communication. The Wireless Sensor Network (WSN) is enrolled in this context. It's a collection of component (nodes) organized into a cooperative network. The main components of this network are tiny battery powered cameras with wireless communication capability. Therefore, image transfer in WSNs presents major challenge which raises issues related to its representation, its storage and its transmission. However, communication of image content has several bottlenecks, including limited bandwidth of cellular networks, restricted computational power, limited storage capability, and battery constraints of the appliances. In this paper, we address the energy, system lifetime and bandwidth bottlenecks of image communication. We present an efficient adaptive compression scheme that can significantly minimize the energy required for wireless image communication while meeting bandwidth constraints of wireless network and image quality. Based on Discrete Wavelet Transform, we propose an efficient image compression scheme, enabling significant reduction in computation energy needed with minimal degradation of image quality. Simulation results are done with C++ and show that the proposed scheme optimizes network lifetime, reduces significantly the amount of required memory and minimizes both (i) computation energy, by reducing the computation needed to compress an image and (ii) communication energy, consumed by the RF component which is proportional to the number of transmitted bits.

[1]  Edward J. Coyle,et al.  An energy efficient hierarchical clustering algorithm for wireless sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[2]  Anantha Chandrakasan,et al.  Energy efficient system partitioning for distributed wireless sensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[3]  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).

[4]  Michael G. Strintzis,et al.  A family of wavelet-based stereo image coders , 2002, IEEE Trans. Circuits Syst. Video Technol..

[5]  Deborah Estrin,et al.  An evaluation of multi-resolution storage for sensor networks , 2003, SenSys '03.

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

[7]  Chang Wen Chen,et al.  Multiple bitstream image transmission over wireless sensor networks , 2003 .

[8]  Luigi Ferrigno,et al.  Balancing computational and transmission power consumption in wireless image sensor networks , 2005, IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2005..

[9]  Nicolas Krommenacker,et al.  Energy-Efficient Transmission of Wavelet-Based Images in Wireless Sensor Networks , 2007, EURASIP J. Image Video Process..

[10]  Sergio D. Servetto Sensing lena-massively distributed compression of sensor images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[11]  Huaming Wu,et al.  Energy efficient distributed JPEG2000 image compression in multihop wireless networks , 2004, 2004 4th Workshop on Applications and Services in Wireless Networks, 2004. ASWN 2004..

[12]  Bhaskar Krishnamachari,et al.  Applications of localized image processing techniques in wireless sensor networks , 2003, SPIE Defense + Commercial Sensing.

[13]  Alhussein A. Abouzeid,et al.  Energy efficient distributed image compression in resource-constrained multihop wireless networks , 2005, Comput. Commun..