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http://dx.doi.org/10.22156/CS4SMB.2020.10.06.041

Deep Learning based BER Prediction Model in Underwater IoT Networks  

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)
Publication Information
Journal of Convergence for Information Technology / v.10, no.6, 2020 , pp. 41-48 More about this Journal
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.
Keywords
Deep learning; Link adaptation; Adaptive Modulation and Coding(AMC); MLP Classification model; Machine learning; Underwater IoT network;
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Times Cited By KSCI : 6  (Citation Analysis)
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1 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.
2 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.
3 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   DOI
4 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   DOI
5 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   DOI
6 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   DOI
7 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
8 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
9 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   DOI
10 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   DOI
11 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.
12 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.
13 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).
14 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.   DOI
15 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.
16 A. Goldsmith. (2005). Wireless communications. Cambridge university press.
17 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   DOI
18 S. Verdu. (2002). Spectral efficiency in the wideband regime. IEEE Transactions on Information Theory, 48(6), 1319-1343. DOI : 10.1109/TIT.2002.1003824   DOI
19 X. Lurton. (2004). An introduction to underwater acoustics. The Journal of the Acoustical Society of America, 115(2), 443. DOI : 10.1121/1.1639324   DOI
20 A. Baggeroer. (1984). Acoustic telemetry-an overview. IEEE Journal of oceanic engineering, 9(4), 229-235. DOI : 10.1109/JOE.1984.1145629   DOI
21 S. Cui, A. J. Goldsmith & A. Bahai. (2005). Energy-constrained modulation optimization. IEEE transactions on wireless communications, 4(5), 2349-2360.   DOI
22 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.
23 T. Jayalakshmi & A. Santhakumaran. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3(1), 1793-8201.
24 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.
25 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.