Volume 18, Issue 3 (12-2021)                   JSDP 2021, 18(3): 65-76 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Azarnia G, Tinati M A, Yousefi Rezaii T. Distributed and Cooperative Compressive Sensing Recovery Algorithm for Wireless Sensor Networks with Bi-directional Incremental Topology. JSDP. 2021; 18 (3) :65-76
URL: http://jsdp.rcisp.ac.ir/article-1-1027-en.html
Urmia University
Abstract:   (377 Views)
Recently, the problem of compressive sensing (CS) has attracted lots of attention in the area of signal processing. So, much of the research in this field is being carried out in this issue. One of the applications where CS could be used is wireless sensor networks (WSNs). The structure of WSNs consists of many low power wireless sensors. This requires that any improved algorithm for this application must be optimized in terms of energy consumption. In other words, the computational complexity of algorithms must be as low as possible and should require minimal interaction between the sensors. For such networks, CS has been used in data gathering and data persistence scenario, in order to minimize the total number of transmissions and consequently minimize the network energy consumption and to save the storage by distributing the traffic load and storage throughout the network. In these applications, the compression stage of CS is performed in sensor nodes, whereas the recovering duty is done in the fusion center (FC) unit in a centralized manner. In some applications, there is no FC unit and the recovering duty must be performed in sensor nodes in a cooperative and distributed manner which we have focused on in this paper. Indeed, the notable algorithm for this purpose is distributed least absolute shrinkage and selection operation (D-LASSO) algorithm which is based on diffusion cooperation structure. This algorithm that compete to the state-of-the-art CS algorithms has a major disadvantage; it involves matrix inversion that may be computationally demanding for sufficiently large matrices. On this basis, in this paper, we have proposed a distributed CS recovery algorithm for the WSNs with a bi-directional incremental mode of cooperation. Actually, we have proposed a comprehensive distributed framework for the recovery of sparse signals in WSNs.  Here, we applied this comprehensive structure to three problems with different constraints which results in three completely distributed solutions named as distributed bi-directional incremental basis pursuit (DBIBP), distributed bi-directional incremental noise-aware basis pursuit (DBINBP) and distributed bi-directional incremental regularized least squares (DBIRLS). The proposed algorithms solely involve linear combinations of vectors and soft thresholding operations. Hence, the computational load is significantly reduced in each sensor. In the proposed method each iteration consists of two phases; clockwise and anti-clockwise phases. At each iteration, in anti-clockwise phase, each node receives the local estimate from its previous neighbor and updates an auxiliary variable. Then in the clockwise phase, each node receives the updated auxiliary variable from its next neighbors to update the local estimate. On the other hand, information exchange in two directions in an incremental manner which we called it bi-directional incremental structure. In an incremental strategy, information flows in a sequential manner from one node to the adjacent node. Unlike the diffusion structure (like as D-LASSO) where each node communicates with all of their neighbors, the incremental mode of cooperation requires the least amount of communication and power. The low computational complexity and better steady state performance are the important features of the proposed methods.
Full-Text [PDF 803 kb]   (145 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2019/06/1 | Accepted: 2020/08/18 | Published: 2022/01/20 | ePublished: 2022/01/20

1. [1] E.J. Candès, and M.B. Wakin, "An introduction to compressive sampling", IEEE signal processing magazine, vol. 25(2), pp.21-30, 2008. [DOI:10.1109/MSP.2007.914731]
2. [2] H. Shiri, M. A. Tinati, M. Codreanu, and G. Azarnia, "Distributed sparse diffusion estimation with reduced communication cost", IET Signal Processing, vol. 12(8), pp. 1043-1052, 2018. [DOI:10.1049/iet-spr.2017.0377]
3. [3] G. Azarnia, M.A. Tinati, and T.Y. Rezaii, "Cooperative and distributed algorithm for compressed sensing recovery in WSNs", IET Signal Processing, vol. 12(3), pp.346-357, 2017. [DOI:10.1049/iet-spr.2017.0093]
4. [4] B.K. Natarajan, "Sparse approximate solutions to linear systems", SIAM Journal on Computing, vol. 24(2), pp. 227-234, 1995. [DOI:10.1137/S0097539792240406]
5. [5] S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIREV, vol. 43(1), pp.129-159, 2001. [DOI:10.1137/S003614450037906X]
6. [6] E. J. Candès and T. Tao, "The Dantzig selector: Statistical estimation when p is much larger than n," The annals of Statistics, vol. 35(6), pp. 2313-2351, 2007. [DOI:10.1214/009053607000000532]
7. [7] R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Roy. Statist. Soc. Ser. B, vol. 58(1), pp. 267-288, 1996. [DOI:10.1111/j.2517-6161.1996.tb02080.x]
8. [8] D. Estrin, L. Girod, G. Pottie, and M. Srivastava, "Instrumenting the world with wireless sensor networks', In 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221), vol. 4, pp. 2033-2036, 2001.
9. [9] G. Azarnia, M. A. Tinati, and T. Y. Rezaii, "Generic cooperative and distributed algorithm for recovery of signals with the same sparsity profile in wireless sensor networks: a non-convex approach," The Journal of Supercomputing, vol. 75(5), pp. 2315-2340, 2019. [DOI:10.1007/s11227-018-2632-y]
10. [10] I. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "A survey on sensor networks", IEEE Communications Magazine, Vol. 40(8), pp.102-114, 2002. [DOI:10.1109/MCOM.2002.1024422]
11. [11] C. Luo, F. Wu, J. Sun, and C. W. Chen, "Compressive data gathering for large-scale wireless sensor networks", In Proceedings of the 15th annual international conference on Mobile computing and networking, 2009, 145-156, [DOI:10.1145/1614320.1614337]
12. [12] C. Luo, F. Wu, J. Sun, and C. Chen, "Efficient measurement generation and pervasive sparsity for compressive data gathering", IEEE Trans Wireless Commun., vol. 9(12), pp. 3728-38, 2010. [DOI:10.1109/TWC.2010.092810.100063]
13. [13] Y. Zhu and X. Wang, "Multi-session data gathering with compressive sensing for large-scale wireless sensor networks," in Proc. Global Telecommunications conf, 2010, pp. 1-5. [DOI:10.1109/GLOCOM.2010.5683396]
14. [14] A. Abrardo, C. M. Carretti, and A. Mecocci, "A compressive sampling data gathering approach for wireless sensor networks using a sparse acquisition matrix with abnormal values", Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on, 2012, pp. 1-4. [DOI:10.1109/ISCCSP.2012.6217784]
15. [15] J. Wang, S. Tang, B. Yin, X. and Li, "Data gathering in wireless sensor networks through intelligent compressive sensing", In 2012 Proceedings IEEE INFOCOM, pp. 603-611, 2012. [DOI:10.1109/INFCOM.2012.6195803]
16. [16] R. Xie and X. Jia, "Minimum transmission data gathering trees for compressive sensing in wireless sensor networks," in Proc. IEEE GlobeCom, pp. 1-5, 2011.
17. [17] X. Wu, Y. Xiong, W. Huang, H. Shen, and M. Li, "An efficient compressive data gathering routing scheme for large-scale wireless sensor networks," Comput. Electr, Eng, vol. 39(6), pp. 1935-1946, 2013. [DOI:10.1016/j.compeleceng.2013.04.009]
18. [18] H. Zheng, F. Yang, X. Tian, X. Gan, X. Wang, and S. Xiao, "Data gathering with compressive sensing in wireless sensor networks: A random walk based approach," IEEE Trans. Parallel Distrib. Syst., vol. 26(1), pp. 35-44, 2015. [DOI:10.1109/TPDS.2014.2308212]
19. [19] D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham, and R. G. Baraniuk, "Distributed compressed sensing", Dept. Elect. Eng., Rice University, Houston, TX, Tech. Rep. TREE-0612, 2006.
20. [20] J. Park, S. Hwang, J. Yang, and D. Kim, "Generalized distributed compressive sensing" appeared in Rice University Compressive Sensing Resources, http://www.dsp.rice.ed-u/cs.
21. [21] H. Xu, N. Fu, L. Qiao, and X. Peng, "Fast pursuit method for greedy algorithms in distributed compressive sensing," in Conf. Rec., IEEE Instrumentation and Measurement Technology Conf., pp. 1118-1122, 2015. [DOI:10.1109/I2MTC.2015.7151428] [PMCID]
22. [22] W. Chen, I. Wassell, and M. Rodrigues, "Dictionary design for distributed compressive sensing," IEEE Signal Processing Letters, vol. 22(1), pp. 95-99, 2015. [DOI:10.1109/LSP.2014.2350024]
23. [23] M. Rabbat, J. Haupt, A. Singh, and R. Nowak, "Decentralized compression and predistribution via random gossiping", in Proc. of IPSN, pp. 51-59, 2006. [DOI:10.1145/1127777.1127789]
24. [24] W. Wang, M. Garofalakis, and K. Ramchandran, "Distributed sparse random projections for refinable approximation," in Proc. of IPSN, pp. 331-339, 2007. [DOI:10.1145/1236360.1236403]
25. [25] A. Talari and N. Rahnavard, "Cstorage: Distributed data storage in wireless sensor networks employing compressive sensing", in Proc. IEEE Global Telecommunications Conf., 2012, pp. 1-5. [DOI:10.1109/GLOCOM.2011.6134318]
26. [26] M. Lin, C. Luo, F. Liu, and F. Wu. "Compressive data persistence in large-scale wireless sensor networks", In Global Telecommunications Conference, 2010, pp. 1-5. [DOI:10.1109/GLOCOM.2010.5684035]
27. [27] F. Liu, M. Lin, Y. Hu, C. Luo, and F. Wu, "Design and analysis of compressive data persistence in large-scale wireless sensor networks," IEEE Trans. Parallel Distrib. Syst., vol. 26(10), pp. 2685-2698, 2015. [DOI:10.1109/TPDS.2014.2360855]
28. [28] G. Azarnia and M. A. Tinati, "Steady-state analysis of the deficient length incremental LMS adaptive networks," Circuits, Syst. Signal Process., vol. 34(9), pp. 2893-2910, 2015. [DOI:10.1007/s00034-015-9978-7]
29. [29] G. Azarnia and M. A. Tinati, "Steady-state analysis of the deficient length incremental LMS adaptive networks with noisy links," AEU Int. J. Electron. Commun., vol. 69(1), pp. 153-162, 2015. [DOI:10.1016/j.aeue.2014.08.007]
30. [30] Z. Zhao, J. Feng and B. Peng "A green distributed signal reconstruction algorithm in wireless sensor networks", IEEE Access, pp. 5908-5917, 2016. [DOI:10.1109/ACCESS.2016.2572303]
31. [31] D. Sundman , S. Chatterjee , and M. Skoglund, "Design and analysis of a greedy pursuit for distributed compressed sensing", IEEE Trans. Sig. Process. Vol. 64 (11), pp. 2803-2818, 2016. [DOI:10.1109/TSP.2016.2523462]
32. [32] W. Chen , and I.J. Wassell , "A decentralized bayesian algorithm for distributed compressive sensing in networked sensing systems", IEEE Trans. Wireless Commu, Vol. 15 (2), pp. 1282-1292, 2016. [DOI:10.1109/TWC.2015.2487989]
33. [33] G. Mateos, J. A. Bazerque, and G. B. Giannakis, "Distributed sparse linear regression," IEEE Transactions on Signal Processing, vol. 58(10), pp. 5262-5276, 2010. [DOI:10.1109/TSP.2010.2055862]
34. [34] S. Foucart, H. Rauhut, "A Mathematical Introduction to Compressive Sensing", Springer, New York, August 2013. [DOI:10.1007/978-0-8176-4948-7]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing