Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

A Low-Resources Hardware-based Audio Data Compression Scheme for Wireless Sensors Networks

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Hugues M. Kamdjou, Elie T. Fute, Adnen El Amraoui, Armand Nzeukou

Hugues M Kamdjou, Elie T Fute, Adnen El Amraoui and Armand Nzeukou. A Low-Resources Hardware-based Audio Data Compression Scheme for Wireless Sensors Networks. International Journal of Computer Applications 174(32):13-18, April 2021. BibTeX

	author = {Hugues M. Kamdjou and Elie T. Fute and Adnen El Amraoui and Armand Nzeukou},
	title = {A Low-Resources Hardware-based Audio Data Compression Scheme for Wireless Sensors Networks},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2021},
	volume = {174},
	number = {32},
	month = {Apr},
	year = {2021},
	issn = {0975-8887},
	pages = {13-18},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2021921258},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Over the last two decades, the Wireless Multimedia Sensors Networks (WMSN) technology have become increasingly popular by both actual industrial users and research community, they are used for recording speech and then sending it to a base station. However, their limited amount of resources (power, low capacity of radio waves, bandwidth, memory, processing, storage, etc.) makes it important to save resources in order to extend the life of the sensor as long as possible. This paper aims to propose and evaluate an adaptive lifting wavelet encoding hardware solution for audio data compression in WMSN, with require low memory, low computation and low energy consumption. The simulation results show that the proposed approach is efficient and satisfactory compared to the Discrete Cosine Transform (DCT) approach, since it allows 32.6% storage savings and 47.84% energy savings were achieved.


  1. Jennifer Yick, Biswanath Mukherjee, and Dipak Ghosal. 2008. Wireless sensor network survey. Computer Networks, vol. 52, no. 2008, pp. 2292-2330.
  2. S. Misra, M. Reisslein, and G. Xue. 2008. A survey of multimedia streaming in wireless sensor networks. IEEE Communications Surveys & Tutorials, vol. 10, no. 4.
  3. P. Gope, and T. Hwang. 2016. Bsn-care: A secure Iot-based modern healthcare system using body sensor network. IEEE Sensors Journal, pp. 1368-1376.
  4. Wenhui Liu, K. R. Vijayanagar, and J. Kim. 2013. Low-complexity distributed multiple description coding for wireless video networks. Wireless Sensor Systems IET, vol.3, no. 3, pp. 205-215.
  5. Elie T. Fute, Alain B. Bomgni, and Hugues M. Kamdjou. 2016. An approach to data compression and aggregation in wireless sensor networks. International Journal of Computer Science and Telecommunications (IJCST), vol. 7, no. 4, pp. 13-19.
  6. C. Pham, P. Cousin, and A. Carer. 2014. Real-time On-Demand Multi-Hop Audio Streaming with Low-Resource Sensor Motes. Proceedings of IEEE Sense App, in conjunction with LCN 2014, Edmonton, Canada.
  7. C. Pham, and P. Cousin. 2013. Streaming the Sound of Smart Cities: Experimentations on the SmartSantander test-bed. Proceeding of the 2013 IEEE International Conference on Internet of Things (iThings2013), Beijing, China.
  8. Ken C. Pohlmann. 2000. Principles of Digital Audio,” McGraw-Hill.
  9. C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen. 2011. An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 19, no. 7, pp. 2125-2136.
  10. D. Brunelli, M. Maggiorotti, and F. L. Bellifemine. 2008. Analysis of audio streaming capability of zigbee networks. In Proceedings of Fifth European Workshop on Wireless Sensor Network (EWSN2008).
  11. Elie Tagne Fute, Hugues Marie Kamdjou, Alain Bertrand Bomgni, and Armand Nzeukou. 2019. An Efficient Data Compression Approach based on Entropic Coding for Network Devices with Limited Resources. EJECE, European Journal of Electrical and Computer Engineering, vol. 3, no. 5.
  12. R. Mangharam, A. Rowe, R. Rajkumar, and R. Suzuki. 2006. Voice over sensor networks. In 27th IEEE International of Real-Time Systems Symposium.
  13. O. Turkes, and S. Baydere. 2011. Voice quality analysis in wireless multimedia sensor networks: An experimental study. In Proceedings of ISSNIP.
  14. Tony Robinson. 1994. SHORTEN: Simple lossless and near-lossless waveform compression. Technical report CUED/F-INFENG/TR.156, Cambridge University Engineering Department, Cambridge, UK, Available at
  15. X. Huang, A. Acero, and H-W. Hon. 2001. Spoken Language Processing: A Guide to Theory, Algorithm and System Development. Pearson Education, 1st edition.
  16. Dong Xiao, Fuyuan Mo, Yan Zhang, Min Zhao, and Li Ma. 2018. An extended Levinson-Durbin algorithm and its application in mixed excitation linear prediction. Heliyon, vol. 4, no.11, e00948.
  17. Ryosuke Sugiura, Yutaka Kamamoto, and Takehiro Moriya. 2019. Shape Control of Discrete Generalized Gaussian Distributions for Frequency-Domain Audio Coding. IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 12, pp. 2234-2248.
  18. Banerjee R, Mobashir M, and DasBit S. 2014. Partial DCT-based energy efficient compression algorithm for wireless multimedia sensor network. IEEE, In Proceeding of the 2nd international conference on electronics, computing and communication technologies, pp. 1-6.
  19. R. Punidha, and M. Sivara. 2017. Integer Wavelet Transform Based Approach for High Robustness of Audio Signal Transmission. International Journal of Pure and Applied Mathematics, vol. 116, no. 23, pp. 295-304.
  20. Ipsita Dutta, Rajib Banerjee, Tamaghna Acharya, and Sipra DasBit. 2012. An energy efficient audio compression scheme using wavelet with dynamic difference detection technique in wireless sensor network. ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp. 360-366.
  21. D. Le Gall, and A. Tabatabai. 1988. Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP’88, vol. 2 pp. 761-764.
  22. E. Touloupis, A. Meliones, and S. Apostolacos. 2011. Implementation and evaluation of a voice codec for zigbee. IEEE Symposium on Computers and Communications.


Audio signal, Compression, Energy-efficiency, Wavelet, Wireless sensors networks