Chaos-based compression sensing on wireless sensor network: enabling a low-power and high-performance system





Compressed sensing; Pseudo-Random; Chaos; WSN; Landslide.


The energy of sensor nodes in operation mode is primarily consumed by the wireless transceivers. Therefore, reducing the transmitted data can lead to significant energy savings. Compressive sensing is a technique that can reproduce an original signal using a smaller number of samples than required by the Nyquist theorem, by exploiting the sparsity of the signal in the represented domain. In Wireless Sensor Networks, compressed sampling is performed at the sensor node, and decompression is performed at the sink node. However, the limited computing and resource constraints in sensor nodes should be taken into consideration when applying the compressed sensing technique. This paper proposes using a non-linear system to generate chaos-based coefficient sequences applied in the sensor nodes of a landslide warning system. The experimental study demonstrated that the sensor node utilizing pseudo-random sampling is faster and less complex in comparison to the sensor node employing random sampling.


[1]. C. D. Nguyen, T. D. Tran, N. D. Tran, H. T. Huynh, and D. T. Nguyen, "Flexible and Efficient Wireless Sensor Networks for Detecting Rainfall-Induced Landslides," International Journal of Distributed Sensor Networks, vol. 11, no. 11, p. 13, (2015). DOI:

[2]. C. Zhao, B. Tang, Y. Huang, and L. J. I. T. o. I. I. Deng, "Edge Collaborative Compressed Sensing in Wireless Sensor Networks for Mechanical Vibration Monitoring," (2022). DOI:

[3]. E. J. Candès and M. B. Wakin, "An introduction to compressive sampling," IEEE signal processing magazine, vol. 25, no. 2, pp. 21-30, (2008). DOI:

[4]. V. Abolghasemi and M. H. J. I. S. L. Anisi, "Compressive sensing for remote flood monitoring," vol. 5, no. 4, pp. 1-4, (2021). DOI:

[5]. D.-g. Zhang, T. Zhang, J. Zhang, Y. Dong, and X.-d. Zhang, "A kind of effective data aggregating method based on compressive sensing for wireless sensor network," EURASIP Journal on Wireless Communications Networking, vol. 2018, no. 1, pp. 1-15, (2018). DOI:

[6]. Q. Wang, D. Lin, P. Yang, and Z. J. I. S. J. Zhang, "An energy-efficient compressive sensing-based clustering routing protocol for WSNs," vol. 19, no. 10, pp. 3950-3960, (2019). DOI:

[7]. D.-C. Nguyen, D.-T. Tran, and D.-N. Tran, "Application of compressed sensing in effective power consumption of WSN for landslide scenario," in Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), Bali, Indonesia, pp. 111-115, (2015). DOI:

[8]. N. Linh-Trung, D. Van Phong, Z. M. Hussain, H. T. Huynh, V. L. Morgan, and J. C. Gore, "Compressed sensing using chaos filters," in Australasian Telecommunication Networks and Applications Conference, pp. 219-223: IEEE, (2008). DOI:

[9]. J. A. Tropp, M. B. Wakin, M. F. Duarte, D. Baron, and R. G. Baraniuk, "Random filters for compressive sampling and reconstruction," in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 3, pp. III-III: IEEE, (2006).

[10]. E. J. J. C. r. m. Candes, "The restricted isometry property and its implications for compressed sensing," vol. 346, no. 9-10, pp. 589-592, (2008). DOI:

[11]. J. C. Sprott and J. C. Sprott, “Chaos and time-series analysis. Oxford University Press”, (2003). DOI:

[12]. S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, "An interior-point method for large-scale l1-regularized least squares," IEEE journal of selected topics in signal processing, vol. 1, no. 4, pp. 606-617, (2007). DOI:

[13]. S. Matsuoka, S. Ichikawa, and N. Fujieda, "A true random number generator that utilizes thermal noise in a programmable system‐on‐chip (PSoC)," International Journal of Circuit Theory and Applications, vol. 49, no. 10, pp. 3354-3367, (2021). DOI:

[14]. P. L’ecuyer, "Tables of linear congruential generators of different sizes and good lattice structure," Mathematics of Computation, vol. 68, no. 225, pp. 249-260, (1999). DOI:

[15]. D. V. Origines, A. M. Sison, and R. P. Medina, "A Novel Pseudo-random number generator algorithm based on entropy source epoch timestamp," in 2019 International Conference on Information and Communications Technology (ICOIACT), pp. 50-55: IEEE, (2019). DOI:

[16]. Q. A. Gian, D.-T. Tran, D. C. Nguyen, V. H. Nhu, and D. Tien Bui, "Design and implementation of site-specific rainfall-induced landslide early warning and monitoring system: a case study at Nam Dan landslide (Vietnam)," Geomatics, Natural Hazards and Risk, vol. 8, no. 2, pp. 1978-1996, (2017). DOI:

[17]. G. Q. Anh, T. D. Tan, N. D. Chinh, and B. T. Dieu, "Flexible Configuration of Wireless Sensor Network for Monitoring of Rainfall-Induced Landslide," Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 3, pp. 1030 -1036, (2018). DOI:




How to Cite

Quoc, A., V. T. Cao, and D. T. Tran. “Chaos-Based Compression Sensing on Wireless Sensor Network: Enabling a Low-Power and High-Performance System”. Journal of Military Science and Technology, vol. 90, no. 90, Oct. 2023, pp. 3-10, doi:10.54939/1859-1043.j.mst.90.2023.3-10.



Research Articles