Abstract
Wireless multimedia sensor network (WMSN) comprising of miniature sensor nodes is capable of processing multimedia data traffic such as still images and video from the environment. There is a wide range of applications which get benefited from such network. Unprocessed multimedia transmission is always expensive in terms of processing power, storage, and bandwidth. So, data processing is a challenge in WMSN. Exploring low-overhead data compression technique is a solution towards this problem. In this work we propose an energy saving image compression technique for WMSN using curve fitting technique considering the application of post-disaster situation analysis through image capturing of the affected area. Upon employing the method on the macroblocks of sensory image, curve fitting coefficients are generated and transmitted towards the sink thereby saves energy by transmitting reduced volume of data. Finally the design feasibility along with simulation results including statistical analysis is presented to evaluate efficacy of the scheme in terms of two conflicting parameters viz. energy consumption and peak signal to noise ratio. The comparative results confirm our scheme’s supremacy in WMSN application domain over existing methods.
Similar content being viewed by others
References
Seema, A., & Reisslein, M. (2011). Towards efficient wireless video sensor networks: A survey of existing node architectures and proposal for a Flexi-WVSNP design. IEEE Communications Surveys & Tutorials, 13(3), 462–483.
Akyildiz, I., Melodia, T., & Chowdhury, K. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51, 921–960.
Akyildiz, I., Melodia, T., & Chowdhury, K. (2008). Wireless multimedia sensor networks: Applications and test beds. Proceedings of the IEEE, 96(10), 1588–1605.
Banerjee, R., Chatterjee, S., & Bit, D. S. (2015). An energy saving audio compression scheme for wireless multimedia sensor networks using spatio-temporal partial discrete wavelet transform. Computers & Electrical Engineering, 48, 389–404.
Boluk, S. P. (2013). Performance comparisons of the image quality evaluation techniques in wireless multimedia sensor networks. Wireless Networks, 19, 443–460.
Sen, A., Chatterjee, T., & Bit, D. S. (2016). LoWaNA: Low-overhead watermark based node authentication in wsn. Wireless Networks, 22(7), 2453–2467.
Pal, T., & Bit, D. S. (2015). A new CFA based image compression technique for energy-starved wireless multimedia sensor network. In Proceedings on 12th international conference INDICON, 2015 (pp. 1–6). IEEE.
Zain, E. H., Elhosseini, A. M., & Ali, H. A. (2015). Image compression algorithms in wireless multimedia sensor networks: A survey. Ain Shams Engineering Journal, 6, 481–490.
Srisooksai, T., Keamarungsi, K., Lamsrichan, P., & Araki, K. (2012). Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35, 37–59.
Wu, M., Tan, L., & Xiong, N. (2016). Dataprediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Information Sciences, 329, 800–818.
Tavli, B., Bicakci, K., Zilan, R., Jose, M., & Ordinas, B. (2011). A survey of visual sensor network platforms. Multimedia Tools and Applications, 60(3), 689–726.
Jessintha, D., Reghu, C., & Raj, V. (2010). A energy efficient, architectural reconfiguring DCT implementation of JPEG images using vector scaling. In Proceedings on international conference ICSIP, 2010 (pp. 59–62). IEEE.
Qin, L., Wusheng, L., & Xiangbin, Y. (2009). Collaborative in-network processing of LT based image compression algorithm in WMSNs. In Proceedings on international conference ETCS, 2009 (pp. 839–843). IEEE.
Banerjee, R., & Bit, D. S. (2015). Low-overhead image compression in WMSN for post disaster situation analysis. In Proceedings on 9th international conference ANTS, 2015 (pp. 1–6). IEEE.
Hassan, K. K., Ngah, U. K., & Salleh, M. F. M. (2012). The most proper wavelet filters in low-complexity and an embedded hierarchical image compression structures for wireless sensor network implementation requirements. In Proceedings on international conference on control system, computing and engineering, 2012 (pp. 137–142). IEEE.
Eldin, Z. H., Elhosseini, A. M., & Ali, A. H. (2015). A modified listless strip based SPIHT for wireless multimedia sensor networks. Computers and Electrical Engineering, 56, 519–532.
Nasri, M., Helali, A., Sghaier, H., & Maaref, H. (2010). Adaptive image transfer for wireless sensor network (WSNs). In Proceedings on 5th international conference DTIS, 2010 (pp 1–7). IEEE.
Kidwai, R. M., Khan, E., & Reisslein, M. (2016). ZM-SPECK: A fast and memoryless image coder for multimedia sensor networks. IEEE Sensors Journal, 16(8), 2575–2587.
Wang, P., Dai, R., & Akyildiz, I. (2011). A spatial correlation-based image compression framework for wireless multimedia sensor networks. IEEE Transaction on Multimedia, 13(2), 388–401.
Irgan, K., Ünsalan, C., & Baydere, S. (2013). Low-cost prioritization of image blocks in wireless sensor networks for border surveillance. Journal of Network and Computer Applications, 38, 54–64.
Faundez, D. C., Lecuire, V., & Lepage, F. (2011). Tiny block-size coding for energy-efficient image compression and communication in wireless camera sensor networks. Signal Processing: Image Communication, 26, 466–481.
Pal, T., Bandhopadhyay, S., & Bit, D. S. (2015). Energy-saving image transmission over WMSN using block size reduction technique. In Proceedings international symposium on nanoelectronic and information systems, 2015 (pp. 41–45). IEEE.
Luo, K., Li, J., & Wu, J. (2014). A dynamic compression scheme for energy-efficient real-time wireless electrocardiogram biosensors. IEEE Transactions on Instrumentation and Measurement, 63(9), 2160–2169.
Han, Y. (2008). Computer animation in mobile phones using a motion capture database compressed by polynomial curve-fitting techniques. IEEE Transactions on Consumer Electronics, 54(3), 1008–1016.
Gupta, K., Bansal, M., & Chaudhury, S. (2011). A compression scheme for handwritten patterns based on curve fitting. In Proceedings 11th international conference ICDAR, 2011 (pp. 1116–1119). IEEE.
Sung, W. T., & Yang, S. C. (2014). Voronoi-based coverage improvement approach for wireless directional sensor networks. Journal of Network and Computer Applications, 39, 202–213.
Wang, W., Xiao, H., Xia, Q., Li, W., & Zhang, M. (2015). Enhancement of fish-eye imaging quality based on compressive sensing. Optik, 126, 2050–2054.
Song, Y., Hu, J., Yang, X., Fu, J., & Xie, X. (2010). A method for data stream processing based on curve fitting. In Proceedings on 2nd international conference ICSPS, 2010 (pp. 542–546). IEEE.
Altman, G. D., & Bland, M. J. (2005). Standard deviations and standard errors. The British Medical Journal (BMJ), 331(7521), 903.
Arif, S., Mansor, S., & Karim, A. H. (2012). Lossless compression of fluoroscopy medical images using correlation and the combination of run-length and Huffman coding. In Proceedings on international conference of biomedical engineering and sciences (pp. 759–762). IEEE.
MicaZ wireless measurement system. www.openautomation.net/uploadsproductos/micaz_datasheet.pdf. Accessed June 15, 2016.
MSP430x2xx Family user’s guide. www.ti.com/lit/ug/slau144j/slau144j.pdf. Accessed January 21, 2017.
ARM architecture reference model. www.scss.tcd.ie/~waldroj/3d1/arm_arm.pdf. Accessed January 18, 2017.
Misra, S., Reisslein, M., & Xue, G. (2008). A survey of multimedia streaming in wireless sensor networks. IEEE Communications Surveys & Tutorials, 10(4), 18–39.
Labrodor, A. M., & Wightman, M. P. (2009). Topology control in wireless sensor networks. Berlin: Springer.
Sheng, B., Tan, C. C., Li, Q., & Mao, W. (2007). An approximation algorithm for data storage placement in sensor networks. In Proceedings on international conference on wireless algorithms, systems and applications (pp. 71–78). IEEE.
Thomos, N., Boulgouris, V. N., & Strintzis, G. M. (2006). Optimized transmission of JPEG2000 streams over wireless channels. IEEE Transactions on Image Processing, 15(1), 54–67.
Xiang, L., Luo, J., & Rosenberg, C. (2013). Compressed data aggregation: Energy-efficient and high-fidelity data collection. IEEE/ACM Transactions on Networking, 21(6), 1722–1735.
Zhao, Z., & Feng, J. (2016). A sparse signal reconstruction algorithm in wireless sensor networks. Mathemetical Problems in Engineering, 2016, 1–10.
Hemalatha, R., Radha, S., & Sudharsan, S. (2015). Energy-efficient image transmission in wireless multimedia sensor networks using block-based compressive sensing. Computers & Electrical Engineering, 44, 67–79.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Banerjee, R., Das Bit, S. An energy efficient image compression scheme for wireless multimedia sensor network using curve fitting technique. Wireless Netw 25, 167–183 (2019). https://doi.org/10.1007/s11276-017-1543-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-017-1543-9