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A Reliable GSHH-DL Routing Protocol for Monitoring the Thermohaline Environment Condition

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Abstract

In underwater acoustic sensor networks (UASN), the main challenging issues are bandwidth, higher propagation delay, and heavy packet loss during data transmission. The existing UASN routing algorithms have larger latency in the network link and a high rate of packet loss because of the salinity and temperature in the water at different depths. In this nominal, an innovative method called Gravitational Search Hybrid Hexagon-Deep Learning algorithm is proposed. By combining Deep Learning and Gravitation Search, the optimized weighting factor is determined after that which is given to ANN to classify the relay nodes. The weighting value of the classifier is calculated by the GS algorithm to get an accurate reliable relay node status. The classified data has the details about the relay node status in the network which attains whether the relay node is in good status or worst status. These relay node statuses has given to the Hybrid Hexagon scheme to identify the best relay path, by comparing the primary relay node value with one standard value. If the present value is greater than the primary value, then it chooses that value among all neighbour relay node data. Hence the way, the relay node routing is accomplished, if there is any worst stage relay present in the relay path, it can suddenly change the relay path without wasting the time to lose the packet. The routing protocol has been implemented in the ns2-AqaSim simulator and testbed for measurement of the performance metrics of the UASN. The simulation result showed that there is a 45% improvement in throughput, 37% in delay, 47.45% in network lifetime and 48% in packet delivery ratio using the proposed method. It is concluded that the proposed method effectively routing the data packet among the Underwater Acoustic network (UWAN) and successfully improve the network capability with worthy overhead.

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References

  1. Chen, Y., Jin, X., Wan, L., Zhang, X., & Xu, X. (2021). Selective dynamic coded cooperative communications for multi-hop underwater acoustic sensor networks. IEEE Access, 7, 70552–70563.

    Article  Google Scholar 

  2. Ullah, I., Chen, J., Su, X., Esposito, C., & Choi, C. (2019). Localization and detection of targets in underwater wireless sensor using distance and angle based algorithms. IEEE Access, 7, 45693–45704.

    Article  Google Scholar 

  3. Zeng, R., & Wang, Y. (2018). Orthogonal angle domain subspace projection-based receiver algorithm for underwater acoustic communication. IEEE Communications Letters, 22(5), 1102–1105.

    Article  Google Scholar 

  4. Toso, G., Masiero, R., Casari, P., Komar, M., Kebkal, O., & Zorzi, M. (2017). Revisiting source routing for underwater networking: The sun protocol. IEEE Access, 6(1), 1525–1541.

    Google Scholar 

  5. Han, X., Yin, J. W., Liu, B., & Guo, L. X. (2020). MIMO underwater acoustic communication in shallow water with ice cover. China Ocean Engineering, 33(2), 237–244.

    Article  Google Scholar 

  6. Yin, J., Ge, W., Han, X., Liu, B., & Guo, L. (2019). Partial FFT demodulation with IRC in MIMO-SC-FDE communication over doppler distorted underwater acoustic channels. IEEE Communications Letters, 23(11), 2086–2090.

    Article  Google Scholar 

  7. Ma, L., Zhou, S., Qiao, G., Liu, S., & Zhou, F. (2016). Superposition coding for downlink underwater acoustic OFDM. IEEE Journal of Oceanic Engineering, 42(1), 175–187.

    Google Scholar 

  8. Chen, Y., Jin, X., Wan, L., Zhang, X., & Xu, X. (2019). Selective dynamic coded cooperative communications for multi-hop underwater acoustic sensor networks. IEEE Access, 7, 70552–70563.

    Article  Google Scholar 

  9. Ahmad, A. M., Barbeau, M., Garcia-Alfaro, J., Kassem, J., & Kranakis, E. (2018). Tuning the demodulation frequency based on a normalized trajectory model for mobile underwater acoustic communications. Emerging Telecommunication Technologies., 30(12), 1–15.

    Google Scholar 

  10. Tran-Dang, H., & Kim, D.-S. (2019). Efficient bandwidth-aware routing for underwater cognitive acoustic sensor networks. IET Wireless Sensor Systems, 9(2), 77–84.

    Article  Google Scholar 

  11. Lee, Y. M. (2017). Classification of node degree based on deep learning and routing method applied for virtual route assignment. Ad Hoc Networks, 58(4), 70–85.

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Diamant, R., Casari, P., Campagnaro, F., Kebkal, O., Kebkal, V., & Zorzi, M. (2018). Fair and throughput-optimal routing in multimodal underwater networks. IEEE Transactions on Wireless Communications, 17(3), 1738–1754.

    Article  Google Scholar 

  14. Rahman, M. A., Lee, Y., & Koo, I. (2017). EECOR: an energy-efficient cooperative opportunistic routing protocol for underwater acoustic sensor networks. IEEE Access, 5(2), 14119–14132.

    Article  Google Scholar 

  15. Zhang, C., Wang, X., Li, F., He, Q., & Huang, M. (2017). Deep learning-based network application classification for SDN. Transactions on Emerging Telecommunications Technologies, 29(4), 1–18.

    Google Scholar 

  16. Zeng, Z., Fu, S., Zhang, H., Dong, Y., & Cheng, J. (2017). A survey of underwater optical wireless communications. IEEE Communications Surveys and Tutorials, 19(1), 204–238.

    Article  Google Scholar 

  17. Diamant, R., Lampe, L., & Gamroth, E. (2017). Bounds for low probability of detection for underwater acoustic communication. IEEE Journal of OE, 42(1), 143–155.

    Google Scholar 

  18. Xing, G., Chen, Y., He, L., Su, W., Hou, R., Li, W., Zhang, C., & Chen, X. (2019). Energy consumption in relay underwater acoustic sensor networks for NDN. IEEE Access, 7(5), 42694–42702.

    Article  Google Scholar 

  19. Zhang, X., Huang, J., Wang, G., & Li, L. (2019). Hypersonic target tracking with high dynamic biases. IEEE Transactions on Aerospace and Electronic Systems, 55(1), 506–510.

    Article  Google Scholar 

  20. Khan, A., Ali, I., Rahman, A. U., Imran, M., & Mahmood, H. (2018). Co-EEORS: Cooperative energy efficient optimal relay selection protocol for underwater wireless sensor networks. IEEE Access, 99(1), 1–15.

    Google Scholar 

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Correspondence to N. Hemavathy.

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Hemavathy, N., Ramesh, K. & Indumathi, P. A Reliable GSHH-DL Routing Protocol for Monitoring the Thermohaline Environment Condition. Wireless Pers Commun 124, 2553–2577 (2022). https://doi.org/10.1007/s11277-022-09478-4

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