Advertisement

Wireless Personal Communications

, Volume 104, Issue 1, pp 57–71 | Cite as

Performance of a Partial Discrete Wavelet Transform Based Path Merging Compression Technique for Wireless Multimedia Sensor Networks

  • Rajib BanerjeeEmail author
  • Sibashis Chatterjee
  • Sipra Das Bit
Article
  • 37 Downloads

Abstract

Wireless multimedia sensor network is well-known for its constraints in the field of multimedia processing in terms of processing power, bandwidth etc. Data processing is always a challenge in such network. Exploiting low overhead compression accompanied by proper routing and aggregation is a challenge for such energy starved multihop network. In this paper, we propose an efficient path merging protocol for wireless multimedia sensor network with randomly deployed nodes. Any partial discrete wavelet transform based compression technique can be plugged into this path merging protocol to reduce redundant data transmission in a significant manner by appropriate aggregation of data packets from merging paths. The design feasibility and the simulation results prove supremacy of our protocol over state-of-the-art competing schemes in terms of maintaining a trade-off between energy consumption and reconstruction quality.

Keywords

Compression Partial wavelet transform Path merging Tree based routing 

Notes

References

  1. 1.
    Kucukbay, E. S., Sert, M., & Yazici, A. (2017). Use of acoustic sensor data to detect objects in surveillance wireless sensor networks. In Proceedings of IEEE 21st international conference on control systems and computer science (CSCS), (pp. 201–212).Google Scholar
  2. 2.
    Jaigirdar, T. F., & Islam, M. M. (2016). A new cost-effective approach for battlefield surveillance in wireless sensor networks. In Proceedings of IEEE international conference on network systems and security (NSysS), (pp. 1–6).Google Scholar
  3. 3.
    Bal, M. (2014). An industrial wireless sensor networks framework for production monitoring. In Proceedings of IEEE 23rd international symposium on industrial electronics (ISIE), (pp. 1142–1147).Google Scholar
  4. 4.
    Wibisono, G., Saktiaji, P. G., & Ibrahim, I. (2017). Techno economic analysis of smart meter reading implementation in PLN Bali using LoRa technology. In Proceedings of IEEE international conference on broadband communication, wireless sensors and powering (BCWSP), (pp. 1–6).Google Scholar
  5. 5.
    Akyildiz, F. I., Melodia, T., & Chowdhury, R. K. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921–960.CrossRefGoogle Scholar
  6. 6.
    Akyildiz, F. I., Melodia, T., & Chowdhury, R. K. (2008). Wireless multimedia sensor networks: Applications and test beds. IEEE Journals and Magazine, 96(10), 1588–1605.Google Scholar
  7. 7.
    Chowdhury, R. A., Chatterjee, T., & DasBit, S. (2014). LOCHA: A light-weight one-way cryptographic hash algorithm for wireless sensor network. Procedia Computer Science, 32, 497–504.CrossRefGoogle Scholar
  8. 8.
    Banerjee, R., Chatterjee, S., & Das Bit, S. (2015). An energy saving audio compression scheme for wireless multimedia sensor networks using spatio-temporal partial discrete wavelet transform. Computers and Electrical Engineering, 48, 389–404.CrossRefGoogle Scholar
  9. 9.
    Banerjee, R., & Das Bit, S. (2015). Low-overhead image compression in WMSN for post disaster situation analysis. In Proceedings of IEEE international conference on advanced networks and telecommunication systems (ANTS), (pp. 1–6).Google Scholar
  10. 10.
    Randhawa, S., & Jain, S. (2017). Data aggregation in wireless sensor networks: Previous research, current status and future directions. Wireless Personal Communications, 97(3), 3355–3425.CrossRefGoogle Scholar
  11. 11.
    Eswaran, S., Edwards, J., Misra, A., & La Porta, F. T. (2012). Adaptive in-network processing for bandwidth and energy constrained mission-oriented multihop wireless networks. IEEE Transactions on Mobile Computing, 11(9), 1484–1498.CrossRefGoogle Scholar
  12. 12.
    Erratt, N., & Liang, Y. (2011). Compressed data-stream protocol—An energy-efficient compressed data-stream protocol for wireless sensor networks. IET Communications, 15(18), 2673–2683.MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    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.CrossRefGoogle Scholar
  14. 14.
    Narang, K. S., Shen, G., & Ortega, A. (2010). Unidirectional graph-based wavelet transforms for efficient data gathering in sensor networks. In Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), (pp. 2902–2905).Google Scholar
  15. 15.
    Shen, G., & Ortega, A. (2008). Optimized distributed 2D transforms for irregularly sampled sensor network grid using wavelet lifting. In Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), (pp. 2513–2516).Google Scholar
  16. 16.
    Winter, T., Thubert, P., Brandt, A., Hui, J., Kelsey, R., Levis, P., et al. (2012). RFC 6550-RPL: IPv6 routing protocols for low-power lossy networks. https://tools.ietf.org/html/rfc6550. Accessed September 6, 2018.
  17. 17.
    Hui, J., & Vasseur, P. J. (2102). The routing protocol for low-power and lossy networks (RPL) options for carrying RPL information in data-plane datagrams. https://tools.ietf.org/html/rfc6553. Accessed September 6, 2018.
  18. 18.
    Crossbow technologies. (2007). Wireless sensor networks, product reference guide. http://www.investigacion.frc.utn.edu.ar/sensores/Equipamiento/Wireless/Crossbow_Wireless_2007_Catalog.pdf. Accessed 7 Oct 2018.
  19. 19.
    Douglas, S. (2009). Audio engineering explained. Philadelphia: Taylor Francis.Google Scholar
  20. 20.
    Huber, R., Sommer, P., & Wattenhofer, R. (2011). Demo abstract: Debugging wireless sensor network simulations with yeti and cooja. In Proceedings of IEEE 10th international conference on information processing in sensor networks (IPSN), (pp. 141–142).Google Scholar
  21. 21.
    Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., & Voigt, T. (2006). Cross-level sensor network simulation with cooja. In Proceedings of IEEE 31st international conference on local computer networks (LCN), (pp. 641–648).Google Scholar
  22. 22.
    Geier, J. (2013). How to define minimum SNR value for signal coverage. www.wireless.nets.com/resources/tutorials/define_SNR_values.html. Accessed September 5, 2018.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Rajib Banerjee
    • 1
    Email author
  • Sibashis Chatterjee
    • 2
  • Sipra Das Bit
    • 2
  1. 1.Department of Electronics and Communication EngineeringDr. B.C. Roy Engineering CollegeDurgapurIndia
  2. 2.Department of Computer Science and TechnologyIndian Institute of Engineering Science and TechnologyShibpur, HowrahIndia

Personalised recommendations