Skip to main content

Advertisement

Log in

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

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  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).

  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).

  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).

  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).

  5. Akyildiz, F. I., Melodia, T., & Chowdhury, R. K. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921–960.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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).

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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).

  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).

  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. 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. 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. Douglas, S. (2009). Audio engineering explained. Philadelphia: Taylor Francis.

    Google Scholar 

  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).

  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).

  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.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rajib Banerjee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banerjee, R., Chatterjee, S. & Das Bit, S. Performance of a Partial Discrete Wavelet Transform Based Path Merging Compression Technique for Wireless Multimedia Sensor Networks. Wireless Pers Commun 104, 57–71 (2019). https://doi.org/10.1007/s11277-018-6008-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-6008-7

Keywords

Navigation