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Data Collection Scheme for Underwater Sensor Cloud System Based on Fog Computing

  • Haitao YuEmail author
  • Jiansheng Yao
  • Xianhao Shen
  • Yanling Huang
  • Meijuan Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)

Abstract

The scheme design of data collection for Underwater Acoustic Sensor Networks (UASNs) poses many challenges due to long propagation, high mobility, limited bandwidth, multi-path and Doppler Effect. In this paper, unlike the traditional underwater sensor network architecture (single sink or multi-sink), we proposed a novel underwater sensor cloud system based on fog computing in view of time-critical underwater applications. In such an architecture, fog nodes with great computation and storage capacity are responsible for computing, dimension reduction and redundant removal for data collected from physical sensor nodes, and then transfer the processed and compressed data to surface center sink node. After that, the center sink sends the received data from fog nodes to cloud computing center. In addition, in this paper we present distance difference and waiting area-based routing protocol, called DDWA. Finally, in comparison with RDBF, naive flooding and HH-VBF, we conduct extensive simulations using NS-3 simulator to verify the effectiveness and validity of the proposed data collection scheme in the context of the proposed architecture.

Keywords

Underwater sensor network Sensor cloud Fog computing Routing protocol NS-3 simulator 

Notes

Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant No. 41661031, Guangxi Natural Science Foundation under Grant No. 2018GXNSFAA138209 and 2018GXNSFAA294061; Foundation of Guilin University of Technology under Grant No. GUTQDJJ2017; Daqing Normal University Natural Science Fund Project under Grant No. 17zr04.

References

  1. 1.
    Wang, T., Zhang, G., Bhuiyan, M.D.Z.A., et al.: A novel trust mechanism based on Fog Computing in Sensor–Cloud System. Future Gener. Comput. Syst. (2018)Google Scholar
  2. 2.
    Wang, T., Zeng, J., Lai, Y., et al.: Data collection from WSNs to the cloud based on mobile Fog elements. Future Gener. Comput. Syst. (2017)Google Scholar
  3. 3.
    Wang, T., Zhou, J., Liu, A., et al.: Fog-based computing and storage offloading for data synchronization in IoT. IEEE Internet Things 6(3), 4272–4282 (2018)CrossRefGoogle Scholar
  4. 4.
    Srimathi, C., Park, S.H., Rajesh, N.: Proposed framework for underwater sensor cloud for environmental monitoring. In: 2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 104–109. IEEE (2013)Google Scholar
  5. 5.
    Hollinger, G.A., Choudhary, S., Qarabaqi, P., et al.: Underwater data collection using robotic sensor networks. IEEE J. Sel. Areas Commun. 30(5), 899–911 (2012)CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Chen, Y., Zhou, S., et al.: Dynamic node cooperation in an underwater data collection network. IEEE Sens. J. 16(11), 4127–4136 (2016)CrossRefGoogle Scholar
  7. 7.
    Wang, J., Li, D., Zhou, M., et al.: Data collection with multiple mobile actors in underwater sensor networks. In: 2008 the 28th International Conference on Distributed Computing Systems Workshops, pp. 216–221. IEEE (2008)Google Scholar
  8. 8.
    Williams, D.P.: AUV-enabled adaptive underwater surveying for optimal data collection. Intel. Serv. Robot. 5(1), 33–54 (2012)CrossRefGoogle Scholar
  9. 9.
    Vasilescu, I., Kotay, K., Rus, D., et al.: Data collection, storage, and retrieval with an underwater sensor network. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 154–165. ACM (2005)Google Scholar
  10. 10.
    Ilyas, N., Alghamdi, T.A., Farooq, M.N., et al.: AEDG: AUV-aided efficient data gathering routing protocol for underwater wireless sensor networks. Procedia Comput. Sci. 52, 568–575 (2015)CrossRefGoogle Scholar
  11. 11.
    Ghoreyshi, S.M., Shahrabi, A., Boutaleb, T.: An efficient AUV-aided data collection in underwater sensor networks. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pp. 281–288. IEEE (2018)Google Scholar
  12. 12.
    Nicolaou, N., et al.: Improving the robustness of location-based routing for underwater sensor networks. In: OCEANS 2007-Europe. IEEE (2007)Google Scholar
  13. 13.
    Li, Z., Yao, N., Gao, Q.: Relative distance based forwarding protocol for underwater wireless networks. Int. J. Distrib. Sensor Netw. 10(2), 173089 (2014)CrossRefGoogle Scholar
  14. 14.
    Noh, Y., Lee, U., et al.: VAPR: void-aware pressure routing for underwater sensor networks. IEEE Trans. Mob. Comput. 12(5), 895–908 (2013)CrossRefGoogle Scholar
  15. 15.
    Haitao, Yu., Yao, Nianmin, et al.: WDFAD-DBR: weighting depth and forwarding area division DBR routing protocol for UASNs. Ad Hoc Netw. 37(2), 256–282 (2016)Google Scholar
  16. 16.
    Coutinho, R.W.L., Boukerche, A., et al.: Geographic and opportunistic routing for underwater sensor networks. IEEE Trans. Comput. 65(2), 548–561 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haitao Yu
    • 1
    Email author
  • Jiansheng Yao
    • 1
  • Xianhao Shen
    • 1
  • Yanling Huang
    • 1
  • Meijuan Jia
    • 2
  1. 1.College of TourismGuilin University of TechnologyGuilinChina
  2. 2.College of Computer Science and Information TechnologyDaqing Normal UniversityDaqingChina

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