Skip to main content
SpringerLink
Log in

Multimedia data compression storage of sensor network based on improved Huffman coding algorithm in cloud

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Based on the application of wireless sensor networks, this thesis studies the sensing data compression algorithm on sensor nodes and the compressed storage processing method of massive sensor data in wireless sensor networks. Considering the spatio-temporal correlation between sensor data of a single node, an improved adaptive Huffman coding algorithm is proposed, which aims to compress the capacity of transmitted data. The algorithm is applicable to wireless sensor network nodes with limited memory and computing resources. The time-space-related sensor data is compressed in the case where the error is adjustable. And carry out the corresponding experiments and analysis. Several lossless compression algorithms for sensing data characteristics were analyzed and related comparison experiments were conducted. The results show that the algorithm can significantly reduce redundant data, have a higher compression ratio and can guarantee data reconstruction accuracy.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Arunkumar N, Ramkumar K, Venkatraman V, Abdulhay E, Fernandes SL, Kadry S, Segal S (2017) Classification of focal and non focal EEG using entropies. Pattern Recogn Lett 94:112–117

    Article  Google Scholar 

  2. Arunkumar N, Ramkumar K, Venkatraman V (2018) Entropy features for focal EEG and non focal EEG. J Comput Sci 27:440–444

    Article  Google Scholar 

  3. Cao X, Madria S, Hara T (2017) A WSN testbed for Z-order encoding based multi-modal sensor data compression[C]//. IEEE International Conference on Sensing, Communication, and NETWORKING IEEE :1–2

  4. Chen SL, Luo GA, Lin TL (2013) Efficient fuzzy-controlled and hybrid entropy coding strategy lossless ECG encoder VLSI design for wireless body sensor networks[J]. Electron Lett 49(17):1058–1059

    Article  Google Scholar 

  5. Emad A (2014) LiftingWiSe: a lifting-based efficient data processing technique in wireless sensor networks[J]. Sensors (Basel, Switzerland) 14(8):14567–14585

    Article  MathSciNet  Google Scholar 

  6. Ganjewar P, Barani S, Wagh SJ (2016) Energy Efficient Deflate (EEDeflate) Compression for Energy Conservation in Wireless Sensor Network[C]//. The International Symposium on Intelligent Systems Technologies and Applications. Springer International Publishing :287–296

  7. Hsu C H, Lin C T, Tserng H P, et al. (2014) An implementation of light-weight compression algorithm for wireless sensor network technology in structure health monitoring[C]//. Internet of Things IEEE :548–552

  8. HYunge D, Park S, Kindt P et al. (2017) Dynamic alternation of Huffman codebooks for sensor data compression[J]. IEEE Embed Syst Lett(99):1

  9. Imran M, Shahzad K, Ahmad N et al (2014) Energy-efficient SRAM FPGA-based wireless vision sensor node: SENTIOF-CAM[J]. Circ Syst Video Technol IEEE Trans 24(12):2132–2143

    Article  Google Scholar 

  10. Kavitha K, Sharma D, Surana R et al (2013) Induced redundancy based Lossy data compression algorithm[J]. Int J Comput Applic 62(16):16–21

    Article  Google Scholar 

  11. Li Y, Wei H, Peng X, et al. (2014) A wireless sensor network for the metallurgical gas monitoring[J]. Proceedings - International Symposium on Computers and Communications:1–6

  12. Li Y, Wei H, Peng X, et al. (2014) A wireless sensor network for the metallurgical gas monitoring[C]//. Comput Commun IEEE :1–6

  13. Liao Y, Mollineaux M, Hsu R et al (2014) SnowFort: an open source wireless sensor network for data analytics in infrastructure and environmental monitoring[J]. Sensors J IEEE 14(12):4253–4263

    Article  Google Scholar 

  14. Luo GA, Chen SL, Lin TL (2013) VLSI implementation of a lossless ECG encoder design with fuzzy decision and two-stage Huffman coding for wireless body sensor network[C]// communications and signal processing. IEEE :1–4

  15. Medeiros HP, Maciel MC, Demo Souza R et al (2015) Lightweight data compression in wireless sensor networks using Huffman coding[J]. Int J Distrib Sensor Netw 2014(1):1–11

    Google Scholar 

  16. Mehfuz S, Tiwari U, Rathore A et al. (2015) A Huffman based lossless compression algorithm for wireless sensor networks[C]// IEEE: 48–53

  17. Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj U, Arunkumar N, Murugappan M, Acharya UR (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Applic: 1–7. https://doi.org/10.1007/s00521-018-3689-5

  18. Rajendra Achary U, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Arunkumar N, Ciaccio EJ, Lim CM (2019) Characterization of focal EEG signals: a review. Fut Gen Comput Syst 91:290–299

    Article  Google Scholar 

  19. Renugadevi S, Darisini PSN (2013) Huffman and Lempel-Ziv based data compression algorithms for wireless sensor networks[C]// international conference on pattern recognition, informatics and Mobile engineering. IEEE: 461–463

  20. Song Y, Shin H, Paek J (2018) Lightweight server-assisted H-K compression for image-based embedded wireless sensor network[J]. IEEE Syst J(99):1–11

  21. Szalapski T, Madria S (2013) On compressing data in wireless sensor networks for energy efficiency and real time delivery[J]. Distrib Parallel Databases 31(2):151–182

    Article  Google Scholar 

  22. Tao Z (2017) Data compression algorithm of sensor networks based on dynamic adjustment of threshold of encoding transmission[J]. J Jilin Univ 55(4):947–951

    Google Scholar 

  23. Yong B, Hong Y, Ren LL, et al. (2013) Assessment of evolving TRMM-based multisatellite real-time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin[J]. J Geophys Res Atmospheres 117(D9)

  24. Zordan D, Martinez B, Vilajosana I, et al. On the performance of Lossy compression schemes for energy constrained sensor Networking[J]. Acm Trans Sensor Netw, 2014, 11(1):1–34.

Download references

Author information

Authors and Affiliations

  1. School of Computing, Tonghua Normal University, Tonghua, Jilin, 134001, China

    Shuxia Wang

Authors
  1. Shuxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuxia Wang.

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

Wang, S. Multimedia data compression storage of sensor network based on improved Huffman coding algorithm in cloud. Multimed Tools Appl 79, 35369–35382 (2020). https://doi.org/10.1007/s11042-019-07765-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-07765-0

Keywords

Search

Navigation