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Dual-Domain Compressed Sensing Method for Oceanic Environmental Elements Collection with Underwater Sensor Networks

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Abstract

Bandwidth and energy constraints of underwater wireless sensors networks necessitate an efficient data transmission between sensor nodes and the fusion center. This paper considers the data gathering underwater networks for monitoring oceanic environmental elements (e.g. temperature, salinity) and only a portion of measurements from sensors allows for oceanic information map reconstruction under compressed sensing (CS) theory. By utilizing the spatial sparsity of active sensors’ data, we introduce an activity and data detection based on CS at the receiver side resulting in an efficient data communication by avoiding the necessity of conveying identity information. For an interleave division multiple access (IDMA) sporadic transmission, CS-CBC detection that combines the benefits from chip-by-chip (CBC) multi-user detection and CS detection is proposed. Further, by successively exploring the sparsity of sensor data in spatial and frequency domain, we propose a novel efficient data gathering scheme named Dual-domain compressed sensing (DCS). Simulation results validate the effectiveness of the proposed scheme compared to IDMA-CS scheme and an optimal sensing probability problem related to minimum reconstruction error is explored.

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References

  1. Domingo MC (2011) Securing underwater wireless communication networks. IEEE Wirel Commun 18(1):22–28

    Article  Google Scholar 

  2. Heidemann J, Zorzi M (2012) Underwater sensor networks: applications, advances and challenges. Philos Trans 370(1958):158–175

    Article  Google Scholar 

  3. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  4. Candès EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  MathSciNet  MATH  Google Scholar 

  5. Han Z, Li H, Yin W (2013) Compressive sensing for wireless networks. Cambridge University Press, Cambridge

    Book  Google Scholar 

  6. Haupt J, Bajwa WU, Rabbat M, Nowak R (2008) Compressed sensing for networked data: a different approach to decentralized compression. IEEE Signal Process Mag 25(2):92–101

    Article  Google Scholar 

  7. Fazel F, Fazel M, Stojanovic M (2011) Random access compressed sensing for energy-efficient underwater sensor networks. IEEE J Sel Areas Commun 29(8):1660–1670

    Article  Google Scholar 

  8. Xue T, Dong X, Shi Y (2013) Multiple access and data reconstruction in wireless sensor networks based on compressed sensing. IEEE Trans Wirel Commun 12(7):3399–3411

    Article  Google Scholar 

  9. Liu GL, Kang WJ (2014) IDMA-based compressed sensing for ocean monitoring information acquisition with sensor networks. Math Probl Eng 1:1–13

    MathSciNet  Google Scholar 

  10. Schepker HF, Dekorsy A (2011) Sparse multi-user detection for CDMA transmission using greedy algorithms. In: Proceedings of the 8th ISWCS, pp 291–295

  11. Schepker HF, Bockelmann C, Dekorsy A (2013) Improving greedy compressive sensing based multi-user detection with iterative feedback. In: Proceedings of IEEE 78th VTC-Fall, pp 1–5

  12. Shim B, Song B (2012) Multiuser detection via compressive sensing. IEEE Commun Lett 16(7):972–974

    Article  Google Scholar 

  13. Monsees F, Woltering M, Bockelmann C, Dekorsy A (2015) Compressive sensing multi-user detection for multicarrier systems in sporadic machine type communication. In: Proceedings of IEEE 81st vehicular technology conference, VTC Spring, pp 1–5

  14. Wang B (2012) Dynamic compressive sensing-based multi-user detection for uplink grant-free NOMA. IEEE Commun Lett 16(7):972–974

    Article  Google Scholar 

  15. Chen X, Yu Z, Hoyos S, Sadler BM, Silva-Martinez J (2011) A sub-Nyquist rate sampling receiver exploiting compressive sensing. IEEE Trans Circuits Syst I 58(3):507–520

    Article  MathSciNet  Google Scholar 

  16. Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30

    Article  Google Scholar 

  17. Baraniuk R, Davenport M, DeVore R, Wakin M (2008) A simple proof of the restricted isometry property for random matrices. Constr Approx 28(3):253–263

  18. Fornasier M, Rauhut H (2015) Compressive sensing. Springer, Berlin

    Book  MATH  Google Scholar 

  19. Zelnik-Manor L, Rosenblum K, Eldar YC (2011) Sensing matrix optimization for block-sparse decoding. IEEE Trans Signal Process 59(9):4300–4312

    Article  MathSciNet  Google Scholar 

  20. Bockelmann C, Schepker H, Dekorsy A (2013) Compressive sensing based multi-user detection for machine-to-machine communication. Trans Emerg Telecommun Technol Spec Issue Mach Mach Emerg Commun Paradigm 24(4):389–400

    Google Scholar 

  21. Schepker HF, Bockelmann C, Dekorsy A (2013) Coping with CDMA asynchronicity in compressive sensing multi-user detection. In: Proceedings of IEEE 77th VTC-Spring, pp 1–5

  22. Schepker HF, Bockelmann C, Dekorsy A (2013) Exploiting sparsity in channel and data estimation for sporadic multi-user communication. In: Proceedings of 10th ISWCS, pp 1–5

  23. Mao R, Li H (2010) A novel multiple access scheme via compressed sensing with random data traffic. J Commun Netw 12(4):308–316

    Article  Google Scholar 

  24. Ping L, Liu L, Wu K, Leung WK (2006) Interleave division multiple access. IEEE Trans Wirel Commun 5(4):938–947

    Article  Google Scholar 

  25. Kusume K, Bauch G, Utschick W (2012) IDMA vs. CDMA: analysis and comparison of two multiple access schemes. IEEE Trans Wirel Commun 11(1):78–87

    Article  Google Scholar 

  26. Eldar YC, Kuppinger P, Bölcskei H (2010) Block-sparse signals: uncertainty relations and efficient recovery. IEEE Trans Signal Process 58(6):3042–3054

    Article  MathSciNet  Google Scholar 

  27. Candes E, Romberg J (2007) Sparsity and incoherence in compressive sampling. Inverse Problems 23(3):969–985

    Article  MathSciNet  MATH  Google Scholar 

  28. Pati Y, Rezaiifar R, Krishnaprasad P (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th Asilomar conference on signals, systems & computers, pp 40–44, Pacific Grove

  29. Majumdar A, Ward RK (2009) Fast group sparse classification. Can J Electr Comput Eng 34(4):136–144

    Article  Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No.61371100, No.61501139, No. 61401118).

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Correspondence to Gongliang Liu.

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Kang, W., Du, R. & Liu, G. Dual-Domain Compressed Sensing Method for Oceanic Environmental Elements Collection with Underwater Sensor Networks. Mobile Netw Appl 23, 272–284 (2018). https://doi.org/10.1007/s11036-017-0947-1

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