CONet: A Cognitive Ocean Network
By Dong Wang, Yujie Li, Jianru Li, Xin Li, Hyoungseop Kim, Seiichi Serikawa, I. Humar. 2019
162 citations recorded by the source corpus.
The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the sea floor. Approximately 70.8 percent of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the ocean network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The cognitive ocean network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the article are as follows: a CONet architecture is proposed and described in detail; important and useful demonstration applications of the CONet are proposed; and future trends in CONet research are presented.
Subfields: Computer Networks and Communications, Ocean Engineering
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In Computer Networks and Communications
In Ocean Engineering
Citations in the corpus
- NAS-Unet: Neural Architecture Search for Medical Image Segmentation (2019, background): “Many works have designed network structures for specific tasks [15], [16].”
- Recent progress in blue energy harvesting for powering distributed sensors in ocean (2021, background): “As an extension of the Internet of Things (IoT), sensing the ocean is playing an increasingly vital role in developing smart oceans [2, 3, 15].”
- Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks (2019, methodology): “The detection speed of SSD is much faster than that of Faster R-CNN [28], [29], while the detection accuracies of the two methods are approximately the same.”