EEEDCS: Enhanced energy efficient distributed compressive sensing based data collection for WSNs

Sustainable Computing: Informatics and Systems - Tập 38 - Trang 100871 - 2023
Sekar K.1, Suganya Devi K.1, Satish Kumar Satti2, Srinivasan P.3
1Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam 788010, India
2Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, 522213, India
3Department of Physics, National Institute of Technology Silchar, Silchar, Assam, 788010, India

Tài liệu tham khảo

Xie, 2019, Data collection for security measurement in wireless sensor networks: a survey, IEEE Internet Things J., 6, 2205, 10.1109/JIOT.2018.2883403 Razzaque, 2013, Compression in wireless sensor networks: a survey and comparative evaluation, ACM Trans. Sen. Netw., 10, 10.1145/2528948 Akyildiz, 2002, Wireless sensor networks: a survey, Comput. Netw., 38, 393, 10.1016/S1389-1286(01)00302-4 Lin, 2020, A survey on energy-efficient strategies in static wireless sensor networks, ACM Trans. Sen. Netw., 17, 10.1145/3414315 Yoon, 2007, The clustered aggregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks, ACM Trans. Sen. Netw., 3, 3, 10.1145/1210669.1210672 Donoho, 2006, Compressed sensing, IEEE Trans. Inform. Theory, 52, 1289, 10.1109/TIT.2006.871582 Wang, 2019, Compressive sensing-based data aggregation approaches for dynamic WSNs, IEEE Commun. Lett., 23, 1073, 10.1109/LCOMM.2019.2909861 Lin, 2021, A social welfare theory-based energy-efficient cluster head election scheme for WSNs, IEEE Syst. J., 15, 4492, 10.1109/JSYST.2020.3010868 Wang, 2019, An energy-efficient compressive sensing-based clustering routing protocol for WSNs, IEEE Sens. J., 19, 3950, 10.1109/JSEN.2019.2893912 Sekar, 2021, Energy efficient data gathering using spatio-temporal compressive sensing for WSNs, Wirel. Pers. Commun., 117, 1279, 10.1007/s11277-020-07922-x Hooshmand, 2016, Covariogram-based compressive sensing for environmental wireless sensor networks, IEEE Sens. J., 16, 1716, 10.1109/JSEN.2015.2503437 Lin, 2020, An energy-saving routing integrated economic theory with compressive sensing to extend the lifespan of WSNs, IEEE Internet Things J., 7, 7636, 10.1109/JIOT.2020.2987354 Song, 2019, Research on data fusion scheme for wireless sensor networks with combined improved LEACH and compressed sensing, Sensors, 19 Lin, 2017, A game theory based energy efficient clustering routing protocol for WSNs, Wirel. Netw., 23, 1101, 10.1007/s11276-016-1206-2 Sekar, 2020, Deep wavelet architecture for compressive sensing recovery, 185 Sekar, 2022, Deep wavelet-based compressive sensing data reconstruction for wireless visual sensor networks, 337 Quer, 2012, Sensing, compression, and recovery for WSNs: sparse signal modeling and monitoring framework, IEEE Trans. Wireless Commun., 11, 3447, 10.1109/TWC.2012.081612.110612 Duarte, 2011, Kronecker compressive sensing, IEEE Trans. Image Process.: A Publ. IEEE Signal Process. Soc., 21, 494, 10.1109/TIP.2011.2165289 M.F. Duarte, S. Sarvotham, D. Baron, M.B. Wakin, R.G. Baraniuk, Distributed Compressed Sensing of Jointly Sparse Signals, in: Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers, 2005, 2005, pp. 1537–1541. Wakin, 2005 Haupt, 2008, Compressed sensing for networked data, IEEE Signal Process. Mag., 25, 92, 10.1109/MSP.2007.914732 L. Xiang, J. Luo, A. Vasilakos, Compressed data aggregation for energy efficient wireless sensor networks, in: 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, 2011, pp. 46–54. Candes, 2005, Decoding by linear programming, IEEE Trans. Inform. Theory, 51, 4203, 10.1109/TIT.2005.858979 Emmanuel, 2005 Peyre, 2010, Best basis compressed sensing, IEEE Trans. Signal Process., 58, 2613, 10.1109/TSP.2010.2042490 Xiao, 2006, Distributed compression-estimation using wireless sensor networks, IEEE Signal Process. Mag., 23, 27, 10.1109/MSP.2006.1657815 Yuen, 2008, A distributed framework for correlated data gathering in sensor networks, IEEE Trans. Veh. Technol., 57, 578, 10.1109/TVT.2007.905243 Hormati, 2008, Distributed compressed sensing: Sparsity models and reconstruction algorithms using annihilating filter, 5141 Baron, 2009 Luo, 2010, Efficient measurement generation and pervasive sparsity for compressive data gathering, IEEE Trans. Wireless Commun., 9, 3728, 10.1109/TWC.2010.092810.100063 Masoum, 2013, A distributed compressive sensing technique for data gathering in wireless sensor networks, Procedia Comput. Sci., 21, 207, 10.1016/j.procs.2013.09.028 M. Leinonen, M. Codreanu, M. Juntti, Distributed correlated data gathering in wireless sensor networks via compressed sensing, in: 2013 Asilomar Conference on Signals, Systems and Computers, 2013, pp. 418–422. Caione, 2014, Compressive sensing optimization for signal ensembles in WSNs, IEEE Trans. Ind. Inform., 10, 382, 10.1109/TII.2013.2266097 Asif, 2014, Sparse recovery of streaming signals using ℓ1-homotopy, IEEE Trans. Signal Process., 62, 4209, 10.1109/TSP.2014.2328981 Leinonen, 2018, Distributed distortion-rate optimized compressed sensing in wireless sensor networks, IEEE Trans. Commun., 66, 1609, 10.1109/TCOMM.2018.2790385 Mehrjoo, 2018, Distributed semi-adaptive compressive sensing data collection in wireless sensor networks, Int. J. Commun. Syst., 31, 10.1002/dac.3546 K. Sekar, K. Suganya Devi, P. Srinivasan, T. Dheepa, B. Arpita, L. Dolendro singh, Joint Correlated Compressive Sensing based on Predictive Data Recovery in WSNs, in: 2020 International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE, 2020, pp. 1–5. Candès, 2008, Enhancing sparsity by reweighted ℓ1 minimization, J. Fourier Anal. Appl., 14, 877, 10.1007/s00041-008-9045-x Lu, 2012 Chen, 2018, Adaptive compressive sensing and data recovery for periodical monitoring wireless sensor networks, Sensors, 18, 10.3390/s18103369 Azarnia, 2018, Cooperative and distributed algorithm for compressed sensing recovery in WSNs, IET Signal Process., 12, 346, 10.1049/iet-spr.2017.0093 Jahanshahi, 2019, A modified compressed sensing-based recovery algorithm for wireless sensor networks, Radioengineering, 10.13164/re.2019.0610 Zhan, 2013, Time invariant error bounds for modified-CS based sparse signal sequence recovery, 286 Gopinath, 1994, Optimal wavelet representation of signals and the wavelet sampling theorem, IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process., 41, 262 Sekar, 2022, Compressed tensor completion: a robust technique for fast and efficient data reconstruction in wireless sensor networks, IEEE Sens. J., 22, 10794, 10.1109/JSEN.2022.3169226 Elzanaty, 2019, Lossy compression of noisy sparse sources based on syndrome encoding, IEEE Trans. Commun., 67, 7073, 10.1109/TCOMM.2019.2926080 Shnayder, 2004, 188 2020 Schmidt, 2005 Cressie, 1985, Fitting variogram models by weighted least squares, Hematical Geol., 17, 563 Berger, 2001, Objective bayesian analysis of spatially correlated data, J. Amer. Statist. Assoc., 96, 1361, 10.1198/016214501753382282 de oliveira, 1997, Bayesian prediction of transformed gaussian random fields, JASA J. Amer. Stat. Assoc., 92 Donoho, 2006 Grant, 2014 S.D. Woodruff, S.J. Lubker, K. Wolter, S.J. Worley, J.D. Elms, Comprehensive Ocean-Atmosphere Data Set (COADS), Tech. Rep.: Release 1a: 1980–1992, 4, 1993, U. S. NOAA Earth System Monitor.