Underwater Cognitive Acoustic Networks Architecture, Development and Deployment

##plugins.themes.academic_pro.article.sidebar##

Published Jul 26, 2022
https://doi.org/10.47164/ijngc.v13i2.339
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 13, Issue 2, July 2022

##plugins.themes.academic_pro.article.main##

BALAJI K
MAILAM ENGINEERING COLLEGE
Sakthivel Murugan Santhanam
Associate Professor, Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering,Tamilnadu, India
Logeshwaran R
Assistant Professor,Department of Electronics and Communication Engineering, Anand Institute of Higher Technology, Tamilnadu, India

Abstract

To develop eco-friendly Underwater Cognitive Acoustic Networks (UCANs) with high spectrum utilization. A huge number of sensor networks are installed Underwater to enable applications for oceanographic data collection, pollution, and corrosion monitoring, offshore exploration, disaster prevention, assisted navigation, and tactical surveillance applications. For feasible applications, there is a need to enable underwater communications among the underwater devices. The latest developments in wireless communications cause spectrum shortage problems in the oceans. Cognitive Acoustic (CA) is the enabling technology for supporting dynamic spectrum access techniques in wireless communication. The CA provides the solution for the spectrum scarcity problem that is encountered in many countries. In oceans, both natural acoustic systems and artificial acoustic systems use acoustic signals for communication. To efficiently utilize the spectrum without interference with other acoustic systems, a smart UAN should be designed with awareness of the surrounding environment and the ability to reconfigure their operation parameters such as frequency band, modulation schemes, and transmission power.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
K, B., Santhanam, S. M. ., & Logeshwaran R. (2022). Underwater Cognitive Acoustic Networks Architecture, Development and Deployment. International Journal of Next-Generation Computing, 13(2). https://doi.org/10.47164/ijngc.v13i2.339
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Al-Kinani, A., Wang, C., Zhou, L., and Zhang, W. 2018. Optical wireless communication
  2. channel measurements and models. IEEE Communications Surveys Tutorials 20, 1939–
  3. Balaji, K. and Murugan, S. 2019. Implementing iot in underwater communication using li-fi.
  4. International Journal Of Recent Technology And Engineering, 958–956.
  5. Casari, P. and Zorzi, M. Protocol design issues in underwater acoustic networks. Computer
  6. Communications 34, 2013–2025.
  7. Dheepa, B., Nithya, R., Nishavithri, N., Vinoth, K., and Balaji, K. 2020. Directional
  8. lifting wavelet transform based SAR Image Compression.
  9. Ghasemi, A. and Sousa, E. 2008. Spectrum sensing in cognitive radio networks: requirements,
  10. challenges and design trade-offs. IEEE Communications Magazine 46, 32–39.
  11. Heidemann, J., Ye, W., Wills, J., Syed, A., and Li, Y. 2006. Research challenges and applications
  12. for underwater sensor networking. IEEE Wireless Communications And Networking
  13. Conference 2006, 228–235.
  14. Hossain, E., Niyato, D., and Han, Z. Dynamic spectrum access and management in cognitive
  15. radio networks. (cambridge university press. 2009.
  16. Ikuma, T. and Naraghi-Pour, M. A. 2008. Comparison of three classes of spectrum sensing DOI: https://doi.org/10.1109/GLOCOM.2008.ECP.843
  17. techniques. IEEE GLOBECOM 2008, 1–5.
  18. Ileri, O., Samardzija, D., Sizer, T., and Mandayam, N. 2005. Demand responsive pricing
  19. and competitive spectrum allocation via a spectrum server. First IEEE International
  20. Symposium On New Frontiers In Dynamic Spectrum Access Networks 2005, 194–202.
  21. Jun-Cui, K. and J., G. 2006. M. zhou, s. The challenges of building mobile underwater wireless
  22. networks for aquatic applications 20, 12–18. DOI: https://doi.org/10.1109/MNET.2006.1637927
  23. Kalpana, G., Rajendran, V., and SakthivelMurugan, S. Study of de-noising techniques
  24. for SNR improvement for underwater acoustic communication.
  25. Kao, C., Lin, Y., Wu, G., and Huang, C. A. 2017. Comprehensive study on the internet of
  26. underwater things: Applications, challenges, and channel models. Sensors. 17
  27. Menon, R., Buehrer, R., and Reed, J. 2005. Outage probability based comparison of
  28. underlay and overlay spectrum sharing techniques. First IEEE International Symposium
  29. On New Frontiers In Dynamic Spectrum Access Networks 2005, 101–109.
  30. Murugan, S., Natarajan, V., and Kumar, R. 2012. Estimation of noise model and denoising
  31. of wind driven ambient noise in shallow water using the lms algorithm. Acoustics
  32. Australia 40, 111.
  33. Raj, K., Murugan, S., Natarajan, V., and Radha, S. 2011. Denoising algorithm using
  34. wavelet for underwater signal affected by wind driven ambient noise. 2011 international
  35. conference on recent trends in information technology (icrtit). p, 943–946.
  36. Rawat, D. and Yan, G. Signal processing techniques for spectrum sensing in cognitive radio
  37. systems: Challenges and perspectives. (2009. 12.
  38. Santoso, T. 2013. Wirawan hendrantoro, g. Development of underwater acoustic communication
  39. model: Op- portunities and challenges 2013, 358–362.
  40. Syed, A., Ye, W., and Heidemann, J. T.-L. A. 2008. New class of mac protocols for
  41. underwater acoustic sensor networks. IEEE INFOCOM 2008, 231–235.
  42. Tandra, R. and Sahai, A. 2005. Fundamental limits on detection in low snr under noise uncertainty.
  43. interna- tional conference on wireless networks, communications and mobile
  44. computing. 1. p 1, 464–469.
  45. Veni, S., Murugan, S., and Natarajan, V. 2011. Modified lms adaptive algorithm for
  46. detection of underwater acoustic signals against ambient noise in shallow water of Indian
  47. sea. 2011 international conference on recent trends in information technology (icrtit). p,
  48. –905.
  49. Wang, B. and Liu, K. 2011. Advances in cognitive radio networks: A survey. IEEE Journal
  50. Of Selected Topics In Signal Processing 5, 5–23.
  51. Yucek, T. and Arslan, H. A. 2009. survey of spectrum sensing algorithms for cognitive radio DOI: https://doi.org/10.1109/SURV.2009.090109
  52. applications. IEEE Communications Surveys Tutorials 11, 116–130.
  53. Zeng, Y., Liang, Y., Hoang, A., and Zhang, R. A. 2010. review on spectrum sensing DOI: https://doi.org/10.1155/2010/381465
  54. for cognitive radio: challenges and solutions. EURASIP Journal On Advances In Signal
  55. Processing 2010, 1–15.
  56. Zhao, Q., Tong, L., Swami, A., and Chen, Y. 2007. Decentralized cognitive mac for opportunistic
  57. spectrum access in ad hoc networks: A pomdp framework. IEEE Journal On
  58. Selected Areas In Communications 25, 589–600.
  59. Zheng, H. and Cao, L. D.-c. s. m. 2005. First ieee international symposium on new frontiers
  60. in dynamic spectrum access networks. 2005, 56–65.
  61. ˇCabri´c, D., M.-S. W. D. B. R. . W. A. 2005,1. A cognitive radio approach for usage of
  62. virtual unlicensed spectrum.