Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Deep Learning based BER Prediction Model in Underwater IoT Networks

딥러닝 기반의 수중 IoT 네트워크 BER 예측 모델

  • Byun, JungHun (Department of Computer Science, Chungbuk National University) ;
  • Park, Jin Hoon (Department of Computer Science, Chungbuk National University) ;
  • Jo, Ohyun (Department of Computer Science, Chungbuk National University)
  • 변정훈 (충북대학교 소프트웨어학과) ;
  • 박진훈 (충북대학교 소프트웨어학과) ;
  • 조오현 (충북대학교 소프트웨어학과)
  • Received : 2020.04.29
  • Accepted : 2020.06.20
  • Published : 2020.06.28
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Abstract

The sensor nodes in underwater IoT networks have practical limitations in power supply. Thus, the reduction of power consumption is one of the most important issues in underwater environments. In this regard, AMC(Adaptive Modulation and Coding) techniques are used by using the relation between SNR and BER. However, according to our hands-on experience, we observed that the relation between SNR and BER is not that tight in underwater environments. Therefore, we propose a deep learning based MLP classification model to reflect multiple underwater channel parameters at the same time. It correctly predicts BER with a high accuracy of 85.2%. The proposed model can choose the best parameters to have the highest throughput. Simulation results show that the throughput can be enhanced by 4.4 times higher than the conventionally measured results.

수중 IoT 네트워크에서 센서 노드는 지속적인 전력 공급이 어렵기 때문에 제한된 상황에서 소비 전력과 네트워크 처리량의 효율성이 매우 중요하다. 이를 위해 기존의 무선 네트워크에서는 SNR(Signal Noise Rate)과 BER(Bit Error Rate)의 높은 연관성을 기반으로 적응적으로 통신 파라미터를 선택하는 AMC(Adaptive Modulation and Coding) 기술을 적용한다. 하지만 본 논문의 실험 결과, 수중에서 SNR과 BER 사이의 상관 관계가 상대적으로 감소함을 확인하였다. 따라서 본 논문에서는 SNR과 함께 다중 파라미터를 동시에 사용하는 딥러닝 기반 BER 예측 모델(MLP, Multi-Layer Perceptron)을 적용한다. 제안하는 BER 예측 모델은 처리량이 가장 높은 통신 방법을 찾아낼 수 있고, 시뮬레이션 결과 85.2%의 높은 정확도와 네트워크 처리량은 기존 처리량보다 4.4배 높은 성능을 보여주는 우수한 성능을 확인하였다.

Keywords

References

  1. X. Lurton. (2004). An introduction to underwater acoustics. The Journal of the Acoustical Society of America, 115(2), 443. DOI : 10.1121/1.1639324
  2. A. Baggeroer. (1984). Acoustic telemetry-an overview. IEEE Journal of oceanic engineering, 9(4), 229-235. DOI : 10.1109/JOE.1984.1145629
  3. H. H. Ng, W. S. Soh & M. Motani. (2008). MACA-U: A media access protocol for underwater acoustic networks. In IEEE GLOBECOM 2008-2008 IEEE Global Telecommunications Conference (pp. 1-5). IEEE.
  4. M. Yang, M. Gao, C. H. Foh, J. Cai & P. Chatzimisios. (2011). DC-MAC: A data-centric multi-hop MAC protocol for underwater acoustic sensor networks. In 2011 IEEE Symposium on Computers and Communications (ISCC) (pp. 491-496). IEEE.
  5. N. Chirdchoo, W. S. Soh & K. C. Chua. (2008). MACA-MN: A MACA-based MAC protocol for underwater acoustic networks with packet train for multiple neighbors. In VTC Spring 2008-IEEE Vehicular Technology Conference (pp. 46-50). IEEE.
  6. H. H. Ng, W. S. Soh & M. Motani. (2010, September). BiC-MAC: Bidirectional-concurrent MAC protocol with packet bursting for underwater acoustic networks. In OCEANS 2010 MTS/IEEE SEATTLE (pp. 1-7). IEEE.
  7. A. Sher, A. Khan, N. Javaid, S. H. Ahmed, M. Y. Aalsalem & W. Z. Khan. (2018). Void hole avoidance for reliable data delivery in IoT enabled underwater wireless sensor networks. Sensors, 18(10), 3271. DOI : 10.3390/s18103271
  8. J. W. Lee & H. S. Cho. (2014). Cascading multi-hop reservation and transmission in underwater acoustic sensor networks. Sensors, 14(10), 18390-18409. DOI : 10.3390/s141018390
  9. W. H. Liao & C. C. Huang. (2011). SF-MAC: A spatially fair MAC protocol for underwater acoustic sensor networks. IEEE Sensors Journal, 12(6), 1686-1694. DOI : 10.1109/JSEN.2011.2177083
  10. M. S. Kwon, U. J. Gim, J. J. Lee & O. Jo. (2018). IoT-based Water Tank Management System for Real-time Monitoring and Controling. Journal of Convergence for Information Technology, 8(6), 217-223. DOI : 10.22156/CS4SMB.2018.8.6.217
  11. Y. S. Jeong. (2017). A Study on improving manufacturing environment using IoT technology in small business environment. Journal of Convergence for Information Technology, 7(2), 83-90. DOI : 10.22156/CS4SMB.2017.7.2.083
  12. K. O. Park & J. K. Lee. (2017). A Countermeasure Technique for Attack of Reflection SSDP in Home IoT. Journal of Convergence for Information Technology, 7(2), 1-9. DOI : 10.22156/CS4SMB.2017.7.2.001
  13. J. H. Ku. (2017). A Study on the Machine Learning Model for Product Faulty Prediction in Internet of Things Environment. Journal of Convergence for Information Technology, 7(1), 55-60. DOI : 10.22156/CS4SMB.2017.7.1.055
  14. L. Wan, H. Zhou, X. Xu, Y. Huang, S. Zhou, Z. Shi & J. H. Cui. (2014). Adaptive modulation and coding for underwater acoustic OFDM. IEEE Journal of Oceanic Engineering, 40(2), 327-336. DOI : 10.1109/JOE.2014.2323365
  15. H. S. Lee, J. W. Jung, C. U. Baek, A. H. Lee & W. J. Kim. (2019). Adaptive Modulation and Coding for Underwater Acoustic Communication. In 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 54-56). IEEE.
  16. K. Pelekanakis, L. Cazzanti, G. Zappa & J. Alves. (2016). Decision tree-based adaptive modulation for underwater acoustic communications. In 2016 IEEE Third Underwater Communications and Networking Conference (UComms) (pp. 1-5). IEEE.
  17. J. Lin, W. Su, L. Xiao & X. Jiang. (2018). Adaptive modulation switching strategy based on Q-learning for underwater acoustic communication channel. In Proceedings of the Thirteenth ACM International Conference on Underwater Networks & Systems (pp. 1-5).
  18. W. Su, J. Lin, K. Chen, L. Xiao & C. En. (2019). Reinforcement Learning-Based Adaptive Modulation and Coding for Efficient Underwater Communications. IEEE Access, 7, 67539-67550. https://doi.org/10.1109/access.2019.2918506
  19. K. Pelekanakis & L. Cazzanti. (2018, October). On adaptive modulation for low SNR underwater acoustic communications. In OCEANS 2018 MTS/IEEE Charleston (pp. 1-6). IEEE.
  20. A. Goldsmith. (2005). Wireless communications. Cambridge university press.
  21. S. Verdu. (2002). Spectral efficiency in the wideband regime. IEEE Transactions on Information Theory, 48(6), 1319-1343. DOI : 10.1109/TIT.2002.1003824
  22. F. Meshkati, H. V. Poor, S. C. Schwartz & N. B. Mandayam. (2005). An energy-efficient approach to power control and receiver design in wireless data networks. IEEE transactions on communications, 53(11), 1885-1894. DOI : 10.1109/TCOMM.2005.858695
  23. S. Cui, A. J. Goldsmith & A. Bahai. (2005). Energy-constrained modulation optimization. IEEE transactions on wireless communications, 4(5), 2349-2360. https://doi.org/10.1109/TWC.2005.853882
  24. J. Byun, Y. H. Cho, T. H. Im, H. L. Ko, K. S. Shin & O. Jo. (2020). Iterative Learning for Reliable Underwater Link Adaptation. Thirty-Fourth AAAI Conference on Artificial Intelligence, 34(10), 13761-13762.
  25. T. Jayalakshmi & A. Santhakumaran. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3(1), 1793-8201.