Skip to main content
SpringerLink
Log in

An Underwater Acoustic Channel Modeling for Internet of Things Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Interest in wideband and medium distance underwater acoustic communication systems is increasing due to the practical application of underwater Internet of Things (IoT) networks. In this paper, we propose a simplified empirical channel model for the medium distance underwater acoustic channels based on real measurement results. The simple and tractable channel model is critical for the development of advanced underwater communications technology. The underwater channel measurements were performed at 20 m sea water depth, the transmitters (TXs) were located at 5 m and 15 m from the bottom, the receivers (RXs) were located at 4 m, 8 m, 12 m, and 16 m from the bottom, and the TX–RX distances were 21 m, 71 m, 127 m, and 273 m. We derived the path loss from the measured dataset, and modified the log-distance model to create a model suitable for an underwater IoT channel. Numerical results show that the proposed model is accurate and reliable enough to use in the development of advanced underwater communication technologies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Goyal, N., Dave, M., & Verma, A. K. (2019). Protocol stack of underwater wireless sensor network: Classical approaches and new trends. Wireless Personal Communications, 104, 995–1022.

    Google Scholar 

  2. Stojanovic, M., & Preisig, J. (2009). Underwater acoustic communication channels: Propagation models and statistical characterization. IEEE Communications Magazine, 47(1), 84–89.

    Google Scholar 

  3. Stefanov, A., & Stojanovic, M. (2011). Design and performance analysis of underwater acoustic networks. IEEE Journal on Selected Areas in Communications, 29(10), 2012–2021.

    Google Scholar 

  4. Gomathi, R. M., & Manickam, J. M. L. (2019). Energy efficient static node selection in underwater acoustic wireless sensor network. Wireless Personal Communications, 107, 709–727.

    Google Scholar 

  5. Akyildiz, I. F., Pompili, D., & Melodia, T. (2005). Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks, 3(3), 257–279.

    Google Scholar 

  6. Cai, H., Xu, B., Jiang, L., & Vasilakos, A. V. (2017). IoT-based big data storage systems in cloud computing: Perspectives and challenges. IEEE Internet of Things Journal, 4(1), 75–87.

    Google Scholar 

  7. Zhang, Q., Yang, L. T., Chen, Z., Li, P., & Bu, F. (2019). An adaptive dropout deep computation model for industrial iot big data learning with crowdsourcing to cloud computing. IEEE Transactions on Industrial Informatics, 15(4), 2330–2337.

    Google Scholar 

  8. Sun, Y., Song, H., Jara, A. J., & Bie, R. (2016). Internet of things and big data analytics for smart and connected communities. IEEE Access, 4, 766–773.

    Google Scholar 

  9. Bera, S., Misra, S., Roy, S. K., & Obaidat, M. S. (2018). Soft-WSN: Software-defined WSN management system for IoT applications. IEEE Systems Journal, 12(3), 2074–2081.

    Google Scholar 

  10. Wazid, M., Das, A. K., Odelu, V., Kumar, N., Conti, M., & Jo, M. (2018). Design of secure user authenticated key management protocol for generic IoT networks. IEEE Internet of Things Journal, 5(1), 269–282.

    Google Scholar 

  11. Morabito, R., Cozzolino, V., Ding, A. Y., Beijar, N., & Ott, J. (2018). Consolidate IoT edge computing with lightweight virtualization. IEEE Network, 32(1), 102–111.

    Google Scholar 

  12. Sun, X., & Ansari, N. (2016). EdgeIoT: Mobile edge computing for the internet of things. IEEE Communications Magazine, 24(16), 22–29.

    Google Scholar 

  13. Bradbeer, R., Law, E., & Yeung, L. F. (2003). Using multi-frequency modulation in a modem for the transmission of near-realtime video in an underwater environment. In Proceedings of IEEE international conference on consumer electronics (pp. 360–361).

  14. Chitre, M., Ong, S. H., & Potter, J. (2005). Performance of coded OFDM in very shallow water channels and snapping shrimp noise. Proceedings of MTS/IEEE OCEANS Conference, 2, 996–1001.

    Google Scholar 

  15. Bocus, M. J., Agrafiotis, D., & Doufexi, A. (2018). Underwater acoustic video transmission using MIMO-FBMC. In 2018 OCEANS—MTS/IEEE Kobe Techno-Oceans (OTO).

  16. Bocus, M. J., Agrafiotis, D., & Doufexi, A. (2018). Real-time video transmission using massive MIMO in an underwater acoustic channel. In 2018 IEEE wireless communications and networking conference (WCNC).

  17. Kan, W., Wang, J., Zhou, H., & Xu, C. (2018). Design of underwater acoustic communication system based on OFDM. In 2018 2nd IEEE advanced information management, communicates,electronic and automation control conference (IMCEC).

  18. Qarabaqi, P., & Stojanovic, M. (2013). Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels. IEEE Journal of Oceanic Engineering, 38(4), 701–717.

    Google Scholar 

  19. Truhachev, D., Schlegel, C., Bashir, M., & Bousquet, J.-F. (2018). Modeling of underwater acoustic channels for communication system testing. In OCEANS 2018 MTS/IEEE Charleston.

  20. Qiao, G., Babar, Z., Ma, L., Wan, L., Qing, X., Li, X., & Bilal, M. (2018). Shallow water acoustic channel modeling and MIMO-OFDM simulations. In 2018 15th international bhurban conference on applied sciences and technology (IBCAST)

  21. Naderi, M., Zajic, A. G., & Patzold, M. (2018). A nonisovelocity geometry-based underwater acoustic channel model. IEEE Transactions on Vehicular Technology, 67(4), 2864–2879.

    Google Scholar 

  22. Lee, B. M., & Yang, H. (2019). Massive MIMO with massive connectivity for industrial internet of things. IEEE Transactions on Industrial Electronics,. https://doi.org/10.1109/TIE.2019.2924855.

    Article  Google Scholar 

  23. Lee, B. M., & Yang, H. (2018). Massive MIMO for industrial internet of things in cyber-physical systems. IEEE Transactions on Industrial Informatics, 14(6), 2641–2652.

    Google Scholar 

  24. Lee, B. M. (2018). Calibration for channel reciprocity in industrial massive MIMO antenna systems. IEEE Transactions on Industrial Informatics, 14(1), 221–230.

    Google Scholar 

  25. Lee, B. M. (2018). Improved energy efficiency of massive MIMO-OFDM in battery-limited IoT networks. IEEE Access, 6, 38147–38160.

    Google Scholar 

  26. Lee, B. M., & Kim, Y. (2013). An adaptive clipping and filtering technique for PAPR reduction of OFDM signals. Circuits, Systems, and Signal Processing, 32(3), 1335–1349.

    MathSciNet  Google Scholar 

  27. Okumura, Y., Ohmori, E., Kawano, T., & Fukuda, K. (1968). Field strength and its variability in VHF and UHF land-mobile radio service. Review of the Electrical Communication Laboratory, 16(9), 825–873.

    Google Scholar 

  28. Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, 29(3), 317–325.

    Google Scholar 

  29. Friss, H. T. (1946). A note on a simple transmission formula. Proceedings of the IRE, 34(5), 254–256.

    Google Scholar 

  30. Rappaport, T. S. (2002). Wireless communications: Principles and practice (2nd ed.). Upper Saddle River: Prentice Hall.

    MATH  Google Scholar 

  31. Cho, Y. S., Kim, J., Yang, W. Y., & Kang, C. G. (2010). MIMO-OFDM wireless communications with MATLAB. Hoboken: Wiley-IEEE Press.

    Google Scholar 

  32. Robertson, I., Somjit, N., & Chongcheawchamnan, M. (2016). Microwave and millimetre-wave design for wireless communications. Hoboken: Wiley.

    Google Scholar 

  33. Stojanovic, M. (2006) On the relationship between capacity and distance in an underwater acoustic communication channel. In Proceedings of the 1st ACM international workshop on underwater networks, Los Angeles, CA, USA, 25 Sept. 2006 ACM: New York, NY, USA, pp. 41–47.

  34. Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55.

    MathSciNet  Google Scholar 

  35. Bouvet, P.-J., & Loussert, A. (2010). Capacity analysis of underwater acoustic MIMO communications. In IEEE OCEANS’10, Sydney, May 24–27.

  36. Gao, M., Foh, C. H., & Cai, J. (2012). On the selection of transmission range in underwater acoustic sensor networks. Sensors, 12, 4715–4729.

    Google Scholar 

  37. Urick, R. (1983). Principles of underwater sound. New York: McGraw-Hill.

    Google Scholar 

  38. Duro, R. J., & Pena, F. L. (2012). Digital image and signal processing for measurement systems. Denmark: River Publishers.

    Google Scholar 

  39. Haykin, S. (1995). Adaptive filter theory (3rd ed.). Upper Saddle River: Prentice Hall.

    MATH  Google Scholar 

  40. Goldsmith, A. (2005). Wireless communications. Cambridge: Cambridge University Press.

    Google Scholar 

  41. Molisch, A. F. (2011). Wireless communications (2nd ed.). Hoboken: Wiley.

    Google Scholar 

  42. Butler, L. (1987). Underwater radio communication. Amateur Radio.

  43. Sharif, B. S., Neasham, J., Hinton, O. R., & Adams, A. E. (2000). A computationally efficient Doppler compensation system for underwater acoustic communications. IEEE Journal of Oceanic Engineering, 25(1), 52–67.

    Google Scholar 

  44. Abdelkareem, A. E., Sharif, B. S., Tsimenidis, C. C., & Neasham, J. A. (2011). Time varying Doppler-shift compensation for OFDM-based shallow Underwater Acoustic Communication systems. In 2011 Eighth IEEE international conference on mobile ad-hoc and sensor systems Oct. 17–22.

  45. Li, B., Zhou, S., Stojanovic, M., Freitag, L., & Willett, P. (2008). Multicarrier communication over underwater acoustic channels with nonuniform doppler shifts. IEEE Journal of Oceanic Engineering, 33(2), 198–209.

    Google Scholar 

  46. Abdelkareema, A. E., Sharifbc, B. S., & Tsimenidisb, C. C. (2016). Adaptive time varying doppler shift compensation algorithm for OFDM-based underwater acoustic communication systems. Ad Hoc Networks, 45, 104–119.

    Google Scholar 

  47. Domingo, M. C. (2012). An overview of the internet of underwater things. Journal of Network and Computer Applications, 32(6), 1879–1890.

    Google Scholar 

  48. Zhou, Z., Yao, B., Xing, R., Shu, L., & Bu, S. (2016). E-CARP: An energy efficient routing protocol for UWSNs in the internet of underwater things. IEEE Sensors Journal, 16(11), 4072–4082.

    Google Scholar 

  49. Kao, C.-C., Lin, Y.-S., Wu, G.-D., & Huang, C.-J. (2017). A comprehensive study on the internet of underwater things: Applications, challenges, and channel models. Sensors, 17, 1477.

    Google Scholar 

  50. Sharif-Yazd, M., Khosravi, M. R., & Moghimi, M. K. (2017). A survey on underwater acoustic sensor networks: perspectives on protocol design for signaling, MAC and routing. Journal of Computer and Communications, 5, 12–23.

    Google Scholar 

  51. Che, X., Wells, I., Dickers, G., Kear, P., & Gong, X. (2010). Swansea Metropolitan University, Re-Evaluation of RF electromagnetic communication in underwater sensor networks. IEEE Communications Magazine, 48, 143–151.

    Google Scholar 

  52. Bereketli, A. (2013). Remotely powered underwater acoustic sensor networks. Ph.D. dissertation, Middle East Technical University.

  53. Lee, H. K., Cheon, J. H., & Lee, B. M. (2020). Improved underwater horizontal ranging algorithm using reflected acoustic wave. Wireless Personal Communications, 111, 1775–1786.

    Google Scholar 

  54. Lee, H. K., Heo, S. W., & Lee, B. M. (2017). Trellis code design of block interleaved CIOD-STBCs for time varying channels. IEEE Transactions on Vehicular Technology, 66(11), 10542–10545.

    Google Scholar 

Download references

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT)(Grant Numbers: NRF-2019R1A4A1023746) and Korea Electric Power Corporation (Grants R18XA02).

Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Hongik University, Seoul, 04066, South Korea

    Ho Kyoung Lee

  2. School of of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea

    Byung Moo Lee

Authors
  1. Ho Kyoung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Byung Moo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Byung Moo Lee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, H.K., Lee, B.M. An Underwater Acoustic Channel Modeling for Internet of Things Networks. Wireless Pers Commun 116, 2697–2722 (2021). https://doi.org/10.1007/s11277-020-07817-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07817-x

Keywords

Search

Navigation