Improved Sensor Sampling Method for the Joint Dictionary Learning and Compressive Data Gathering in WSNs with the Aid of Information Theory
Subject Areas : Renewable energy
Gholamreza
Imanian
1
(Department of Mechanical, Electrical and Computer Engineering- Science and Research Branch, Islamic Azad University, Tehran, Iran)
Mohammad Ali
Pourmina
2
(Department of Mechanical, Electrical and Computer Engineering- Science and Research Branch, Islamic Azad University, Tehran, Iran)
Ahmad
Salahi
3
(Iran Telecommunication Research Center, Tehran, Iran)
Keywords: Wireless Sensor Network, dictionary learning, Compressive Sensing, Data aggregation, Environmental Monitoring,
Abstract :
In the last decade, to reduce the costs of environmental monitoring, the data aggregation based on the joint dictionary learning and compressive sensing technique in wireless sensor networks has been considered. In this article, a deterministic and non-random sampling design for use in this data aggregation method is presented. This method is based on estimating the amount of mutual information of sensor data and is obtained by sampling all of them in a short part of the data collection round named the training phase. In the next and main stage of the data collection period, only the nodes that provide the most information about the non-sampled nodes are scheduled to sample. Simulation results for real signals show that when the number of sampling sensors comprises still about 25% of the total network nodes, average energy savings of more than 12% can be achieved over a reference sampling method.
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_||_[1] D. Lin, W. Min, J. Xu, "An energy-saving routing integrated economic theory with compressive sensing to extend the lifespan of WSNs”, IEEE Internet of Things Journal, vol. 7, pp. 7636-7647, Aug. 2020 (doi: 10.1109/JIOT.2020.2987354).
[2] B. Sun, Y. Guo, N. Li, D. Fang, "Multiple target counting and localization using variational bayesian em algorithm in wireless sensor networks”, IEEE Trans. on Communications, vol. 65, no. 7, pp. 2985-2998, July 2017 (doi: 10.1109/TCOMM.2017.2695198).
[3] S. P. Tirani, A. Avokh, S. Azar, "WDAT-OMS: A two-level scheme for efficient data gathering in mobile-sink wireless sensor networks using compressive sensing theory”, IET Communications, vol. 14, pp. 1826-1837, July 2020 (doi: 10.1049/iet-com.2019.0433).
[4] M. Hasanhoseini, F. Mesrinejad, H. Mahdavi-Nasab,"A routing method for tracking a moving target with reduced energy consumption", Journal of Intelligent Procedures in Electrical Technology, vol. 11, no. 43, pp. 29-47, Autumn 2020 (in Persian).
[5] A. Biason, C. Pielli, M. Rossi, A. Zanella, D. Zordan, M. Kelly, M. Zorzi, "EC-CENTRIC: An energy-and context-centric perspective on IoT systems and protocol design”, IEEE Access, vol. 5, pp. 6894-6908, Apr. 2017 (doi: 10.1109/ACCESS.2017.2692522).
[6] S. Pakdaman Tirani, A. Avokh,"Impact of sink node placement onto wireless sensor networks performance regarding clustering routing and compressive sensing theory", Journal of Intelligent Procedures in Electrical Technology, vol. 7, no. 25, pp. 41-54, Spring 2016 (in Persian).
[7] J. Huang and B. Soong, "Cost-aware stochastic compressive data gathering for wireless sensor networks”, IEEE Trans. on Vehicular Technology, vol. 68, no. 2, pp. 1525-1533, Feb. 2019 (doi: 10.1109/TVT.2018.2887091).
[8] Y. Ma, X. Zhang, Y. Gao, "Joint sub-Nyquist spectrum sensing scheme with geo-location database over TV white space”, IEEE Trans. on Vehicular Technology, vol. 67, no. 5, pp. 3998-4007, May 2018 (doi: 10.1109/TVT.2017.2776560).
[9] X. Zhang, Y. Ma, Y. Gao, W. Zhang, "Autonomous compressive-sensing-augmented spectrum sensing”, IEEE Trans. on Vehicular Technology, vol. 67, no. 8, pp. 6970-6980, Aug. 2018 (doi: 10.1109/TVT.2018.2822776).
[10] J. C. Ye, "Compressed sensing MRI: a review from signal processing perspective”, BMC Biomedical Engineering, vol. 1, no. 1, pp. 1-17, Mar. 2019 (doi:10.1186/s42490-019-0006-z).
[11] R. Baraniuk, M. Davenport, R. DeVore, M. Wakin, "A simple proof of the restricted isometry property for random matrices”, Constructive Approximation, vol. 28, no. 3, pp. 253-263, Dec. 2008 (doi:10.1007/s00365-007-9003-x).
[12] W. U. Bajwa, J. D. Haupt, A. M. Sayeed, R. D. Nowak, "Joint source–channel communication for distributed estimation in sensor networks”, IEEE Trans. on Information Theory, vol. 53, no. 10, pp. 3629-3653, Oct. 2007 (doi: 10.1109/TIT.2007.904835).
[13] C. Luo, F. Wu, J. Sun, C. W. Chen, "Compressive data gathering for large-scale wireless sensor networks”, Proceedings of the ACM/MobiCom, pp. 145-156, Beijing, China, Sept. 2009 (doi: 10.1145/1614320.1614337).
[14] L. Xiang, J. Luo, A. Vasilakos, "Compressed data aggregation for energy efficient wireless sensor networks”, Proceedings of the IEEE/SECON, pp. 46-54, Salt Lake City, UT, USA, June 2011 (doi: 10.1109/SAHCN.2011.5984932).
[15] S. Lee, S. Pattem, M. Sathiamoorthy, B. Krishnamachari, A. Ortega, "Compressed sensing and routing in multi-hop networks”, Technical Report, University of Southern California, 2009.
[16] G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer, M. Zorzi, "On the interplay between routing and signal representation for compressive sensing in wireless sensor networks”, Proceeding of the IEEE/ITA Workshop, pp. 206-215, La Jolla, CA, USA, Feb. 2009 (doi: 10.1109/ITA.2009.5044947).
[17] W. Wang, M. Garofalakis, K. Ramchandran, "Distributed sparse random projections for refinable approximation”, Proceedings of the IEEE/IPSN, pp. 331-339, Cambridge, MA, USA, Apr. 2007 (doi: 10.1109/IPSN.2007.4379693).
[18] R. Rana, W. Hu, C.T. Chou, "Energy-Aware Sparse approximation Technique (EAST) for rechargeable wireless sensor networks”, Proceedings of the EWSN, pp. 306-321, University of Coimbra, Coimbra, Portugal, Feb. 2010 (doi:10.1007/978-3-642-11917-0_20).
[19] G. Quer, R. Masiero, G. Pillonetto, M. Rossi, M. Zorzi, "Sensing, compression, and recovery for WSNs: Sparse signal modeling and monitoring framework”, IEEE Trans. on Wireless Communications, vol. 11, no. 10, pp. 3447-3461, Oct. 2012 (doi: 10.1109/TWC.2012.081612.110612).
[20] M. Hooshmand, M. Rossi, D. Zordan, M. Zorzi, "covariogram-based compressive sensing for environmental wireless sensor networks”, IEEE Sensors Journal, vol. 16, no. 6, pp. 1716-1729, Mar. 2016 (doi: 10.1109/JSEN.2015.2503437).
[21] E.J. Candès, J. Romberg, T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information”, IEEE Trans. on Information Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006 (doi: 10.1109/TIT.2005.862083).
[22] H. Mohimani, M. Babaie-Zadeh, C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed L0 norm”, IEEE Trans. on Signal Processing, vol. 57, no. 1, pp. 289-301, Jan. 2009 (doi: 10.1109/TSP.2008.2007606).
[23] I. Jolliffe, Principal component analysis: Springer, 2011.
[24] D. Zordan, G. Quer, M. Zorzi, M. Rossi, "Modeling and generation of space-time correlated signals for sensor network fields”, Proceedings of the IEEE/GLOBECOM, pp. 1-6, Houston, TX, USA, Dec. 2011 (doi: 10.1109/GLOCOM.2011.6133891).
[25] M.C. Vuran, I.F. Akyildiz, "Spatial correlation-based collaborative medium access control in wireless sensor networks”, IEEE/ACM Trans. on Networking, vol. 14, no. 2, pp. 316-329, April 2006 (doi: 10.1109/TNET.2006.872544).
[26] R. Cristescu, M. Vetterli, "On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks”, Proceedings of the IEEE/IPSN, pp. 159-164, Boise, ID, USA, Apr. 2005 (doi: 10.1109/IPSN.2005.1440918).
[27] A. Deshpande, C. Guestrin, S.R. Madden, J.M. Hellerstein, W. Hong, "Model-driven data acquisition in sensor networks”, Proceedings of the VLDB, vol. 30, pp. 588-599, Toronto, Canada, Aug. 2004.
[28] T.M. Cover, J.A. Thomas, Elements of information theory: John Wiley & Sons, 2012.
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