Cognitive ocean of things: a comprehensive review and future trends
- 36 Downloads
The scientific and technological revolution in Internet of Things is set off in oceanography. Humans have always observed the ocean outside the ocean to study the ocean. In recent years, it changes have been made into the interior of the ocean and the laboratories have been built on the sea floor. Approximately 71% of the Earth’s surface is covered by water. Ocean of things is expected to be important for disaster prevention, ocean resource exploration, and underwater environmental monitoring. Different from traditional wireless sensor networks, ocean of things has its own unique features, such as low reliability and narrow bandwidth. These features may be great challenges for ocean of things. Furthermore, the integration of artificial intelligence and ocean of things has become a topic of increasing interests for oceanology research fields. Cognitive ocean of things (COT) will become the mainstream of future ocean science and engineering development. In this paper, we provide the definition of COT, and the main contributions of this paper are (1) we review the ocean observing networks all the world; (2) we propose the COT architecture and describe the details of it; (3) important and useful applications are discussed; (4) we point out the future trends of COT researches.
KeywordsOcean networks Internet of Things Multimedia communication systems
- 1.Industry 4.0: The fourth industrial revolution. https://www.i-scoop.eu/industry-4-0/.
- 2.Arrott, M., Chave, A., & Farcas, C. (2011). Building transparent data access for ocean observatories: Coordination of US IOOS DMAC with NSFs OOI cyberinfrastructure. In OCEANS (pp. 1–9).Google Scholar
- 6.Moloney, J., Hillis, C., Mouy, X., Urazghildiiev, I., & Dakin, T. (2014). Autonomous multichannel acoustic recorders on the VENUS ocean observatory. In Proceedings of the of IEEE oceans (pp. 1–6).Google Scholar
- 7.Kaneda, Y. (2010). The advanced ocean floor real time monitoring system for mega thrust earthquakes and tsunamis-application of DONET and DONET2 data to seismological research and disaster mitigation. In Proceedings of IEEE oceans (pp. 1–6).Google Scholar
- 9.Lewis, P. Internet of Things. http://www.duluthenergydesign.com/Content/Documents/GeneralInfo/PresentationMaterials/2017/Day2/Internet-of-Things-LaForge.pdf. Accessed 2017.
- 14.Xie, S., Chen, J., Luo, J., Xie, P., & Tang, W. (2012). Detection and tracking of underwater object based on forward-scan sonar. In Proceedings of the international conference on intelligent robotics and applications (pp. 341–347).Google Scholar
- 15.Li, M., Ji, H., Wang, X., Weng, L., & Gong, Z. (2013). Underwater object detection and tracking based on multi-beam sonar image processing. In Proceedings of IEEE international conference on robotics and biomimetics (pp. 1–5).Google Scholar
- 16.Snyder, J., Silverman, Y., Bai, Y., & Maclver, M. (2013). Underwater object tracking using electrical impedance tomography. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 1–6).Google Scholar
- 17.Walther, D., Edgington, D., & Koch, C. (2004). Detection and tracking of objects in underwater video. In Proceedings of the 2004 computer society conference on computer vision and pattern recognition (pp. 1–5).Google Scholar
- 18.Chuang, M., Hwang, J., Ye, J., Huang, S., & Williams, K. (2016). Underwater fish tracking for moving cameras based on deformable multiple kernels. arXiv:1603.01695.
- 20.IHI. (2014). Power generation using the Kuroshio current. IHI Engineering Review, 46(2), 1–5.Google Scholar
- 25.Chandrasekhar, V., Seah, W. K., Choo, Y. S., & Ee, H. V. (2006). Localization in underwater sensor networks: Survey and challenges. In Proceedings of the 1st ACM international workshop on underwater networks, Los Angeles, CA, USA, 25 September 2006 (pp. 33–40).Google Scholar
- 26.Li, Y. (2017). Deep reinforcement learning: An overview. arXiv:1701.07274.
- 29.McGillivary, P., & Zykov, V. (2016). Ship-based cloud computing for advancing oceanographic research capabilities. Proceedings of IEEE Oceans, 2016, 1–6.Google Scholar
- 30.Weng, T., Chen, Y., & Lu, H. (in press). On parallelization of image dehazing with OpenMP. International Journal of High Performance Computing and Networking, 1–12.Google Scholar
- 31.Lu, H., Wang, D., Li, Y., Li, J., Li, X., Kim, H., Serikawa, S., & Humar, I. (2019). CONet: A cognitive ocean network. IEEE Wireless Communications, 1–8.Google Scholar
- 34.Sternlicht, D. D., Dikeman, R. D., Lemonds, D. W., Korporaal, M. T., & Teranishi, A. M. (2003). Target confirmation architecture for a buried object scanning sonar. Proceedings of IEEE OCEANS, 1, 1–9.Google Scholar
- 35.Nevis, A., Bryan, J., Taylor, J. S., & Cordes, B. (2002). Object detection using a background anomaly approach for electro-optic identification sensors. http://www.dtic.mil, ADA749176. Accessed 12 June 2018.
- 36.Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In Proceedings of the CVPR (pp. 7263–7271).Google Scholar
- 38.Manjula, R., & Manvi, S. (2013). Coverage optimization based sensor deployment by using PSO for anti-submarine detection in UWASNs. In Proceedings of the international symposium on ocean eletronics, Athani, India (pp. 1–6).Google Scholar
- 40.Kanazawa, T. (2013). Japan Trench earthquake and tsunami monitoring network of cable-linked 150 ocean bottom observatories and its impact to earth disaster science. In: Proceedings of the 2013 IEEE international underwater technology symposium (pp. 1–6).Google Scholar