Influence of Compressive Sensing on Performance Metrics of Wireless Sensor Networks – A Survey

N. Subhashini*, M. Murugan**
* Assistant Professor, Department of Electronics and Communication Engineering, Valliammai Engineering College, Tamil Nadu, India.
** Professor and Vice Principal, Department of Electronics and Communication Engineering, Valliammai Engineering College, Tamil Nadu, India.
Periodicity:April - June'2016
DOI : https://doi.org/10.26634/jwcn.5.1.6023

Abstract

Compressive sensing outperforms the traditional limits of the sampling theory. Based on the principle of sparsity and incoherence, the Compressive sensing retrieves the original signal with the least number of samples compared to the conventional method. Wireless sensor network consists of a large number of sensor nodes or motes with varying size depending upon the application. The spatially distributed nodes transmit the data sensed from the field in cooperation with other nodes to the fusion center. If the monitoring field is wide, the data collected from the field is also large consuming more energy, bandwidth and capacity of the network. Increase in the energy consumption of the node results in the decrease in the lifetime of the node. Hence, to increase the lifetime of the node, the data traffic in the network is reduced by associating compressive sensing with the wireless sensor network. This paper deals with the variation of the performance metrics of wireless sensor networks, in the presence of compressive sensing.

Keywords

Wireless Sensor Network, Compressive Sensing, Shannon Theorem, Sparsity, Incoherence, Signal Reconstruction.

How to Cite this Article?

Subhashini, N., and Murugan, M. (2016). Influence of Compressive Sensing on Performance Metrics of Wireless Sensor Networks – A Survey. i-manager's Journal on Wireless Communication Networks, 5(1), 34-42. https://doi.org/10.26634/jwcn.5.1.6023

References

[1]. Bajwa, W., Haupt, J., Sayeed, A., & Nowak, R. (2006). “Compressive Wireless Sensing Categories and Subject Descriptors”. Conf. on Info Processing Sensor Networks (IPSN '06).
[2]. Balouchestani, M. (2011). “Compressed sensing in wireless sensor networks: Survey”. Canadian Journal on Multimedia and Wireless Networks, Vol.2, No.1, pp.1-4. Retrieved from http://ampublisher.com/Feb 2011/MWN- 1102-012.pdf
[3]. Bonavolontà, F., D'Apuzzo, M., Liccardo, A., & Vadursi, M. (2014). “New Approach Based on Compressive Sampling for Sample Rate Enhancement in DASs for Low-Cost Sensing Nodes”. Sensors, Vol.14, pp.18915-18940. Retrieved from http://doi.org/10.3390/ s141018915
[4]. Brunelli, D., & Caione, C. (2015). “Sparse Recovery Optimization in Wireless Sensor Networks with a Sub- Nyquist Sampling Rate”. Sensors, Vol.15, No.7, pp.16654- 16673. Retrieved from http://doi.org/10.3390/ s150716654
[5]. Candès, E. (2006). “Compressive sampling”. Proceedings of the International Congress of Mathematicians, pp.1433-1452.
[6]. Candes E. and Wakin B Michael, (2008). “An Introduction to Compressive Sampling”. IEEE Signal Processing Magazine, Vol.25, No.2, pp.21-30.
[7]. Chen, C. (n.d.). A Survey on Sub-Nyquist Sampling. Retrieved from www.seas.ucla.edu
[8]. Colonnese, S., Cusani, R., Rinauro, S., Ruggiero, G., & Scarano, G. (2013). “Efficient compressive sampling of spatially sparse fields in wireless sensor networks”. EURASIP Journal on Advances in Signal Processing, Vol.1, pp.136. Retrieved from http://doi.org/10.1186/1687-6180-2013- 136
[9]. Davenport, M.A, Duarte, M. F. M., Eldar, Y. C. Y., & Kutyniok, G. (2011). “Introduction to compressed sensing”. Preprint, Vol.93, pp.1-68. Retrieved from http://doi.org/10.1287/mksc. 20.3.244.9764
[10]. Donoho, D.L.L. (2006). “Compressed sensing”. IEEE Transactions on Information Theory, Vol.52, No.4, pp.1289- 1306. Retrieved from http://doi.org/Doi 10.1109/Tit.2006.871582
[11]. Foucart, S., & Rauhut, H. (2013). A Mathematical Introduction to Compressive Sensing. Retrieved from http://doi.org/ 10.1007/978-0-8176-4948-7
[12]. García-Hernández, C. F., Ibarguengoytia- Gonzalez, P. H., García-Hernández, J., & Pérez-Díaz, J. A. (2007). “Wireless Sensor Networks and Applications: A Survey”. Int. Journal of Computer Science and Network Security, Vol.7, No.3, pp.264-273.
[13]. Grant, M., & Boyd, S. (2015). CVX Users ' Guide. CVX Research, Inc. , pp.1-72.
[14]. Hayashi, K., Nagahara M., & Tanaka, T. (2013). “A User's Guide to Compressed Sensing for Communications Systems”. IEICE Transactions on Communications, E96. B(3), pp. 685 - 712. Retrieved from http://doi.org/10.1587/transcom. E96.B.685
[15]. Ji. S., Huang, L. ., Wang, J., Shen, J. , & Kim, J.-U. . (2014). “An improved reconstruction methods of compressive sensing data recovery in wireless sensor networks”. International Journal of Security and Its Applications, Vol.8, No.1, pp.1-8. Retrieved from http://doi.org/10.14257/ ijsia.2014.8.1.01
[16]. Liu, X. L., Luo, C., & Wu, F. (2012). “Compressive cooperation for Gaussian half-duplex relay channel”. Journal on Wireless Communication Networks, pp.1-10.
[17]. Luo, J., Xiang, L., & Rosenberg, C. (2010). “Does Compressed Sensing Improve the Throughput of Wireless Sensor Networks”. IEEE International Conference on Communications, pp. 1-6. Retrieved from http://doi.org/10.1109/ ICC.2010.5502565
[18]. Masiero, R., Quer, G., Pillonetto, G., Rossi, M., & Zorzi, M. (2009). “Sensing, Compression and Recovery for Wireless Sensor Networks: Sparse Signal Modelling”. IEEE Global Communication Conference (GLOBECOM), Honululu.
[19]. Masoum, A., Meratnia, N., & Havinga, P. J. M. (2013). “A distributed compressive sensing technique for data gathering in Wireless Sensor Networks”. Procedia Computer Science, Vol.21, pp.207-216. Retrieved from http://doi.org/10.1016/j.procs.2013.09.028
[20]. Pope, G. (2009). Compressive Sensing: A Summary of Reconstruction Algorithms. Matrix, (August 2008).
[21]. Qaisar, S., Bilal, R. M., Iqbal, W., Naureen, M., & Lee, S. (2013). “Compressive sensing: From theory to applications, a survey”. Journal of Communications and Networks, Vol.15, No.5, pp.443-456. Retrieved from http://doi.org/10.1109/JCN.2013.000083
[22]. Rawat, P., Deep, K., Chaouchi, H., & Marie, J. (2014). “ Wireless sensor networks: A sur vey on recent developments and potential synergies”. The Journal of Supercomputing, Vol.68, No.1, pp.1-48. Retrieved from http://doi.org/10.1007/s11227-013-1021-9
[23]. Strohmer, T. (2012). “Measure what should be measured: Progress and challenges in compressive sensing”. IEEE Signal Processing Letters, Vol.19, No.12, pp.887-893. Retrieved from http://doi.org/10.1109/ LSP.2012.2224518
[24]. Villaverde, J. F., Chung, W.-Y., & Chen, S.-L. (2015). “Compressive Sensing Algorithm for Wireless Sensor Network Power Management”. International Journal of Computer and Electrical Engineering, Vol.7, No.3, pp. 199-205. Retrieved from http://doi.org/10.17706/IJCEE. 2015.7.3.199-205
[25]. Walke, S. R., & Jaini, P. (2014). “Compressed Sensing Using Deterministic Measurement”. Int. Journal of Advanced Comput. Engg., and Networking, Vol.2, No.5, pp.60-63.
[26]. Xiayong Z., Houjun W.,Yang, & Zhijian D. (2010). “Wireless Sensor Networks based on Compressive Sensing”. IEEE Int. Conf. Comput. Sci. Inf. Technol., Vol.9, pp.90-92.
[27]. Xie, R., Jia, X., & Society, C. (2014). “Transmission- Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing”. IEEE Transactions on Parallel & Distributed Systems,Vol.25, No.3, pp.806-815.
[28]. Xiong, J., & Tang, Q. (2014). “1-Bit Compressive Data Gathering for Wireless Sensor Networks”. Journal of Sensors, 2014, pp. 1-6. Retrieved from http://doi.org/10.1155/2014/805423
[29]. Yang, G., Tan, V. Y. F., Ho, C. K., Ting, S. H., & Guan, Y. L. (2013). “Wireless Compressive Sensing for Energy Harvesting Sensor Nodes”. IEEE Transactions on Signal Processing, Vol.61, No.18, pp.4491-4505. Retrieved from http://doi.org/10.1109/TSP.2013.2271480
[30]. Zheng, H., Xiao, S., Wang, X., Tian, X., & Guizani, M. (2013). “Capacity and Delay Analysis for Data Gathering with Compressive Sensing in Wireless Sensor Networks”. IEEE Transactions on Wireless Communications, Vol.12, No.2, pp.917-927. Retrieved from http://doi.org/10.1109/ TWC.2012.122212. 121032
[31]. Milliarde, (2016). Compressed Sensing Introduction and Tutorial with MATLAB. Retrieved from Codeproject.com
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.