Skip to main content

Advertisement

SpringerLink
Log in

Deep learning-based hybrid dynamic biased track (DL-HDBT) routing for under water acoustic sensor networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In underwater acoustic sensor networks (UASN), the main challenging issues are bandwidth, higher propagation delay, and heavy packet loss during data transmission. The issues can be solved through efficient routing algorithms. Due to the complexity and variability of the underwater acoustic environment, the underwater acoustic sensor network has the characteristics of fluidity, sparse deployment, and energy limitation, which brings certain challenges to underwater positioning technology. Aiming at the scenario that the node redundancy in the underwater acoustic sensor network leads to low positioning efficiency, this paper has proposed the deep learning-high dynamic biased track (DL-HDBT) algorithm. The DL-HDBT combines the deep learning and hybrid dynamic biased tracking algorithm. Deep learning (DL) helps in the identification of the best relay nodes in the network and traffic-congested nodes are tracked using a high dynamic bias track. The routing protocol has been implemented in the ns2-AqaSim simulator and testbed for measurement of the performance metrics of the UASN. The simulation results showed that the novel routing method throughput has increased by 17%, 35%, and 57% when compared with SUN, VBF and DF method. It can effectively improve the throughput of nodes, balance positioning performance as well as energy use efficiency, and optimize the positioning result of UWASN.

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

  • Akyildiz F, Pompili D, Melodia T (2005) Underwater acoustic sensor networks: research challenges. Ad Hoc Netw 3:257–279

    Article  Google Scholar 

  • Anuradha D, Srivatsa SK (2019) Energy effectual reconfigurable routing protocol (E2R2P) for cluster based underwater wireless sensor networks. J Ambient Intell Humaniz Comput 6:453–461

    Google Scholar 

  • Arifur Rahman MD, Lee YD et al (2017) EECOR: an energy-efficient cooperative opportunistic routing protocol for underwater acoustic sensor networks. IEEE Access 5(2):14119–14132

    Article  Google Scholar 

  • Ayyadurai M, Selvakumar Raja S (2020) Optimisation of data reliability in UASN using adaptive Buffalo algorithm. J Ambient Intell Humaniz Comput

  • Chen Y, Jin X et al (2018) Selective dynamic coded cooperative communications for multi-hop underwater acoustic sensor networks. IEEE Access 7:70552–70563

    Article  Google Scholar 

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

    Google Scholar 

  • Diamant R, Casari P et al (2018) Fair and throughput-optimal routing in multimodal underwater networks. IEEE Trans Wirel Commun 17(3):1738–1754

    Article  Google Scholar 

  • Gong Z, Li C et al (2018) AUV-aided joint localization and time synchronization for underwater acoustic sensor networks. IEEE Signal Process Lett 25(4):477–481

    Article  Google Scholar 

  • Goyal N, Dave M et al (2015) Fuzzy based clustering and aggregation technique for under water wireless sensor networks. ICECS, pp 1–4

  • Hu T, Fei Y (2010) QELAR: a machine-learning-based adaptive routing protocol for energy-efficient and lifetime-extended underwater sensor networks. IEEE Trans Mob Comput 9(6):796–809

    Article  Google Scholar 

  • Jouhari M, Ibrahimi K et al (2019) Underwater wireless sensor networks: a survey on enabling technologies, localization protocols, and internet of underwater things. IEEE Access 7:96879–96899

    Article  Google Scholar 

  • Khan A, Ali I et al (2018) Co EEORS: cooperative energy efficient optimal relay selection protocol for underwater wireless sensor networks. IEEE Access 99(1):1–15

    Google Scholar 

  • Lee YM (2017) Classification of node degree based on deep learning and routing method applied for virtual route assignment. Ad Hoc Netw 58(4):70–85

    Article  Google Scholar 

  • Li X, Sun Y, Guo Y, Fu X, Pan M (2017) Dolphins first: Dolphin-aware communications in multi-hop underwater cognitive acoustic networks. IEEE Trans Wirel Commun 16(4):2043–2056

    Article  Google Scholar 

  • Qiuli C, Wei X, Fei D, Ming H (2018) A reliable routing protocol against hotspots and burst for UASN-based fog systems. J Ambient Intell Humaniz Comput 10:3109–3121

    Article  Google Scholar 

  • Sendra S, Lloret J, Jimenez JM, Parra L (2016) Underwater acoustic modems. IEEE Sens J 16(11):4063–4071

    Article  Google Scholar 

  • Sharma DK, Dhurandher SK, Agarwal D, Arora K (2018) kROp: k-means clustering based routing protcol for opportunistic networks. J Ambient Intell Humaniz Comput 10:1289–1306

    Article  Google Scholar 

  • Song A, Badiey M, Newhall A, Lynch JF, DeFerrari HA, Katsnelson BG (2010) Passive time-reversal acoustic communications through shallow water internal waves. IEEE J OE 35(4):756–764

    Google Scholar 

  • Su Y, Fan R, Fu X, Jin Z (2019) DQELR: an adaptive deep Q-network-based energy- and latency-aware routing protocol design for underwater acoustic sensor networks. IEEE Trans. https://doi.org/10.1109/access.2019.2891590

    Article  Google Scholar 

  • 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 

  • Tran-Dang H, Kim D-S (2019) Efficient bandwidth-aware routing for underwater cognitive acoustic sensor networks. IET Wirel Sens Syst 9(2):77–84

    Article  Google Scholar 

  • Ullah I, Chen J et al (2019) Localization and detection of targets in underwater wireless sensor using distance and angle based algorithms. IEEE Access 7:45693–45704

    Article  Google Scholar 

  • Wang S, Shin Y et al (2019) 3D-deployment of magnetic induction relays in underwater sensor networks. ICOIN, pp 222–226

  • Xing G, Chen Y et al (2019) Energy consumption in relay underwater acoustic sensor networks for NDN. IEEE Access 7(5):42694–42702

    Article  Google Scholar 

  • Yan H, Shi J, Cui JH (2008) DBR: depth-based routing for underwater sensor networks. In: Proceedings of Networking, pp 72–86

  • Yan J, Zhang X et al (2018) Asynchronous localization with mobility prediction for underwater acoustic sensor networks. IEEE Trans Veh Technol 67(3):2543–2556

    Article  Google Scholar 

  • Zeng Z, Fu S, Zhang H, Dong Y, Cheng J (2017) A survey of underwater optical wireless communications. IEEE Commun Surv Tutor 19(1):204–238

    Article  Google Scholar 

  • Zhang C, Wang X et al (2017) Deep learning-based network application classification for SDN. IEEE Trans Emerg Telecommun Technol 29(4):1–18

    Google Scholar 

  • Zhang X-Y, Huang J-L et al (2019) Hypersonic target tracking with high dynamic biases. IEEE Trans Aerosp Electron Syst 55(1):506–510

    Article  MathSciNet  Google Scholar 

  • Zhou ZB, Yao B et al (2016) E-CARP: an energy efficient routing protocol for UWSNs on the internet of underwater things. IEEE Sens J 16(11):4072–4082

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE, Vellammal Engineering College, Chennai, India

    N. Hemavathy

  2. School of ECE, Madras Institute of Technology Campus, Anna University, Chennai, India

    P. Indumathi

Authors
  1. N. Hemavathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Indumathi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Hemavathy.

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

Hemavathy, N., Indumathi, P. Deep learning-based hybrid dynamic biased track (DL-HDBT) routing for under water acoustic sensor networks. J Ambient Intell Human Comput 12, 1211–1225 (2021). https://doi.org/10.1007/s12652-020-02165-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02165-x

Keywords

Search

Navigation