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Erschienen in: Wireless Networks 8/2020

07.06.2019

Compress sensing algorithm for estimation of signals in sensor networks

verfasst von: Juan Martinez, Jose Mejia, Boris Mederos, Alberto Ochoa, Oliverio Cruz-Mejía, José Antonio Marmolejo-Saucedo

Erschienen in: Wireless Networks | Ausgabe 8/2020

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Abstract

In this research, we present a data recovery scheme for wireless sensor networks. In some sensor networks, each node must be able to recover the complete information of the network, which leads to the problem of the high cost of energy in communication and storage of information. We proposed a modified gossip algorithm for acquire distributed measurements and communicate the information across all nodes of the network using compressive sampling and Gossip algorithms to compact the data to be stored and transmitted through a network. The experimental results on synthetic data show that the proposed method reconstruct better the signal and in less iterations than with a similar method using a thresholding algorithm.

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Literatur
1.
Zurück zum Zitat Aysal, T. C., Yildiz, M. E., Sarwate, A. D., & Scaglione, A. (2009). Broadcast gossip algorithms for consensus. IEEE Transactions on Signal processing, 57(7), 2748–2761.MathSciNetCrossRef Aysal, T. C., Yildiz, M. E., Sarwate, A. D., & Scaglione, A. (2009). Broadcast gossip algorithms for consensus. IEEE Transactions on Signal processing, 57(7), 2748–2761.MathSciNetCrossRef
2.
Zurück zum Zitat Baraniuk, R. G. (2007). Compressive sensing [lecture notes]. IEEE Signal Processing Magazine, 24(4), 118–121.CrossRef Baraniuk, R. G. (2007). Compressive sensing [lecture notes]. IEEE Signal Processing Magazine, 24(4), 118–121.CrossRef
3.
Zurück zum Zitat Boyd, S., Ghosh, A., Prabhakar, B., & Shah, D. (2006). Randomized gossip algorithms. IEEE Transactions on Information Theory, 52(6), 2508–2530.MathSciNetCrossRef Boyd, S., Ghosh, A., Prabhakar, B., & Shah, D. (2006). Randomized gossip algorithms. IEEE Transactions on Information Theory, 52(6), 2508–2530.MathSciNetCrossRef
4.
Zurück zum Zitat Candes, E. J., & Tao, T. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory, 52(12), 5406–5425.MathSciNetCrossRef Candes, E. J., & Tao, T. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory, 52(12), 5406–5425.MathSciNetCrossRef
5.
Zurück zum Zitat Cao, M., Spielman, D. A., & Yeh, E. M. (2006). Accelerated gossip algorithms for distributed computation. In Proceedings of the 44th annual Allerton conference on communication, control, and computation (pp. 952–959). Citeseer. Cao, M., Spielman, D. A., & Yeh, E. M. (2006). Accelerated gossip algorithms for distributed computation. In Proceedings of the 44th annual Allerton conference on communication, control, and computation (pp. 952–959). Citeseer.
6.
Zurück zum Zitat Di Lorenzo, P., & Scutari, G. (2016). Next: In-network nonconvex optimization. IEEE Transactions on Signal and Information Processing over Networks, 2(2), 120–136.MathSciNetCrossRef Di Lorenzo, P., & Scutari, G. (2016). Next: In-network nonconvex optimization. IEEE Transactions on Signal and Information Processing over Networks, 2(2), 120–136.MathSciNetCrossRef
7.
Zurück zum Zitat Dimakis, A. G., Kar, S., Moura, J. M., Rabbat, M. G., & Scaglione, A. (2010). Gossip algorithms for distributed signal processing. Proceedings of the IEEE, 98(11), 1847–1864.CrossRef Dimakis, A. G., Kar, S., Moura, J. M., Rabbat, M. G., & Scaglione, A. (2010). Gossip algorithms for distributed signal processing. Proceedings of the IEEE, 98(11), 1847–1864.CrossRef
8.
Zurück zum Zitat Donoho, D. L., Maleki, A., & Montanari, A. (2009). Message-passing algorithms for compressed sensing. Proceedings of the National Academy of Sciences, 106(45), 18914–18919.CrossRef Donoho, D. L., Maleki, A., & Montanari, A. (2009). Message-passing algorithms for compressed sensing. Proceedings of the National Academy of Sciences, 106(45), 18914–18919.CrossRef
9.
Zurück zum Zitat Donoho, D. L., Maleki, A., & Montanari, A. (2010). Message passing algorithms for compressed sensing: I. Motivation and construction. In 2010 IEEE information theory workshop on information theory (ITW 2010, Cairo) (pp. 1–5). IEEE. Donoho, D. L., Maleki, A., & Montanari, A. (2010). Message passing algorithms for compressed sensing: I. Motivation and construction. In 2010 IEEE information theory workshop on information theory (ITW 2010, Cairo) (pp. 1–5). IEEE.
10.
Zurück zum Zitat Duarte, M. F., Davenport, M. A., Takhar, D., Laska, J. N., Sun, T., Kelly, K. F., et al. (2008). Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25(2), 83–91.CrossRef Duarte, M. F., Davenport, M. A., Takhar, D., Laska, J. N., Sun, T., Kelly, K. F., et al. (2008). Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25(2), 83–91.CrossRef
11.
Zurück zum Zitat Han, P., Niu, R., Ren, M., & Eldar, Y. C. (2014). Distributed approximate message passing for sparse signal recovery. In 2014 IEEE global conference on signal and information processing (GlobalSIP) (pp. 497–501). IEEE. Han, P., Niu, R., Ren, M., & Eldar, Y. C. (2014). Distributed approximate message passing for sparse signal recovery. In 2014 IEEE global conference on signal and information processing (GlobalSIP) (pp. 497–501). IEEE.
12.
Zurück zum Zitat Haupt, J., Bajwa, W. U., Rabbat, M., & Nowak, R. (2008). Compressed sensing for networked data. IEEE Signal Processing Magazine, 25(2), 92–101.CrossRef Haupt, J., Bajwa, W. U., Rabbat, M., & Nowak, R. (2008). Compressed sensing for networked data. IEEE Signal Processing Magazine, 25(2), 92–101.CrossRef
13.
Zurück zum Zitat Li, S., Da Xu, L., & Wang, X. (2013). Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Transactions on Industrial Informatics, 9(4), 2177–2186.CrossRef Li, S., Da Xu, L., & Wang, X. (2013). Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Transactions on Industrial Informatics, 9(4), 2177–2186.CrossRef
14.
Zurück zum Zitat Liu, J., Lian, F., & Mallick, M. (2016). Distributed compressed sensing based joint detection and tracking for multistatic radar system. Information Sciences, 369, 100–118.MathSciNetCrossRef Liu, J., Lian, F., & Mallick, M. (2016). Distributed compressed sensing based joint detection and tracking for multistatic radar system. Information Sciences, 369, 100–118.MathSciNetCrossRef
15.
Zurück zum Zitat Lu, J., Tang, C. Y., Regier, P. R., & Bow, T. D. (2011). Gossip algorithms for convex consensus optimization over networks. IEEE Transactions on Automatic Control, 56(12), 2917–2923.MathSciNetCrossRef Lu, J., Tang, C. Y., Regier, P. R., & Bow, T. D. (2011). Gossip algorithms for convex consensus optimization over networks. IEEE Transactions on Automatic Control, 56(12), 2917–2923.MathSciNetCrossRef
16.
Zurück zum Zitat Lustig, M., Donoho, D. L., Santos, J. M., & Pauly, J. M. (2008). Compressed sensing MRI. IEEE Signal Processing Magazine, 25(2), 72.CrossRef Lustig, M., Donoho, D. L., Santos, J. M., & Pauly, J. M. (2008). Compressed sensing MRI. IEEE Signal Processing Magazine, 25(2), 72.CrossRef
17.
Zurück zum Zitat Mamaghanian, H., Khaled, N., Atienza, D., & Vandergheynst, P. (2011). Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Transactions on Biomedical Engineering, 58(9), 2456–2466.CrossRef Mamaghanian, H., Khaled, N., Atienza, D., & Vandergheynst, P. (2011). Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Transactions on Biomedical Engineering, 58(9), 2456–2466.CrossRef
18.
Zurück zum Zitat Mota, J. F., Xavier, J. M., Aguiar, P. M., & Puschel, M. (2012). Distributed basis pursuit. IEEE Transactions on Signal Processing, 60(4), 1942–1956.MathSciNetCrossRef Mota, J. F., Xavier, J. M., Aguiar, P. M., & Puschel, M. (2012). Distributed basis pursuit. IEEE Transactions on Signal Processing, 60(4), 1942–1956.MathSciNetCrossRef
19.
Zurück zum Zitat Mukhopadhyay, S., & Chakraborty, M. (2018). Deterministic and randomized diffusion based iterative generalized hard thresholding (DiFIGHT) for distributed sparse signal recovery. arXiv preprint, arXiv:1804.08265. Mukhopadhyay, S., & Chakraborty, M. (2018). Deterministic and randomized diffusion based iterative generalized hard thresholding (DiFIGHT) for distributed sparse signal recovery. arXiv preprint, arXiv:​1804.​08265.
20.
Zurück zum Zitat Nedic, A., Olshevsky, A., & Shi, W. (2017). Achieving geometric convergence for distributed optimization over time-varying graphs. SIAM Journal on Optimization, 27(4), 2597–2633.MathSciNetCrossRef Nedic, A., Olshevsky, A., & Shi, W. (2017). Achieving geometric convergence for distributed optimization over time-varying graphs. SIAM Journal on Optimization, 27(4), 2597–2633.MathSciNetCrossRef
21.
Zurück zum Zitat Nedic, A., Ozdaglar, A., & Parrilo, P. A. (2010). Constrained consensus and optimization in multi-agent networks. IEEE Transactions on Automatic Control, 55(4), 922–938.MathSciNetCrossRef Nedic, A., Ozdaglar, A., & Parrilo, P. A. (2010). Constrained consensus and optimization in multi-agent networks. IEEE Transactions on Automatic Control, 55(4), 922–938.MathSciNetCrossRef
22.
Zurück zum Zitat Ravazzi, C., Fosson, S., & Magli, E. (2014). Energy-saving gossip algorithm for compressed sensing in multi-agent systems. In 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5060–5064). IEEE. Ravazzi, C., Fosson, S., & Magli, E. (2014). Energy-saving gossip algorithm for compressed sensing in multi-agent systems. In 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5060–5064). IEEE.
23.
Zurück zum Zitat Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297–307.CrossRef Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297–307.CrossRef
24.
Zurück zum Zitat Tian, Z., & Giannakis, G. B. (2007). Compressed sensing for wideband cognitive radios. In 2007 IEEE international conference on acoustics, speech and signal processing-ICASSP’07 (Vol. 4, pp. IV–1357). IEEE. Tian, Z., & Giannakis, G. B. (2007). Compressed sensing for wideband cognitive radios. In 2007 IEEE international conference on acoustics, speech and signal processing-ICASSP’07 (Vol. 4, pp. IV–1357). IEEE.
25.
Zurück zum Zitat Zaki, A., Venkitaraman, A., Chatterjee, S., & Rasmussen, L. K. (2018). Greedy sparse learning over network. IEEE Transactions on Signal and Information Processing over Networks, 4(3), 424–435.MathSciNetCrossRef Zaki, A., Venkitaraman, A., Chatterjee, S., & Rasmussen, L. K. (2018). Greedy sparse learning over network. IEEE Transactions on Signal and Information Processing over Networks, 4(3), 424–435.MathSciNetCrossRef
Metadaten
Titel
Compress sensing algorithm for estimation of signals in sensor networks
verfasst von
Juan Martinez
Jose Mejia
Boris Mederos
Alberto Ochoa
Oliverio Cruz-Mejía
José Antonio Marmolejo-Saucedo
Publikationsdatum
07.06.2019
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 8/2020
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-019-02031-5

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