Research Article
Unmanned Aerial Underwater Vehicle (UAUV) in the Ocean Sensor Network
@INPROCEEDINGS{10.1007/978-3-031-04245-4_8, author={Qihang Cao and Gongliang Liu}, title={Unmanned Aerial Underwater Vehicle (UAUV) in the Ocean Sensor Network}, proceedings={6GN for Future Wireless Networks. 4th EAI International Conference, 6GN 2021, Huizhou, China, October 30--31, 2021, Proceedings}, proceedings_a={6GN}, year={2022}, month={5}, keywords={Ocean sensor network AUV Unmanned aerial underwater vehicle Ergodic search algorithm}, doi={10.1007/978-3-031-04245-4_8} }- Qihang Cao
Gongliang Liu
Year: 2022
Unmanned Aerial Underwater Vehicle (UAUV) in the Ocean Sensor Network
6GN
Springer
DOI: 10.1007/978-3-031-04245-4_8
Abstract
Human exploration of the ocean has never stopped. A large number of sensors are placed in the ocean to establish ocean sensor networks to obtain more information about the marine environment, crustal dynamic changes and so on. With the development of science and technology, autonomous underwater vehicle (AUV) emerges as the times require. As an underwater sensor acquisition system, it has been widely used in ocean sensor networks. Due to the complex and changeable marine environment, the AUV can not travel accurately, resulting in a lot of resources waste of time and energy, even the loss of AUV, so the information can not be timely and effective collected. In this paper, the Unmanned Aerial Underwater Vehicle (UAUV) is introduced into the ocean sensor network, and the advantages and disadvantages of the UAUV in the ocean sensor network are compared objectively from multiple dimensions. Through the ergodic search algorithm, the optimal water entry point for the UAUV to complete the task in the shortest time and the minimum power consumption is found, and the performance of the underwater vehicle’s cross domain mode and underwater mode ocean sensor data acquisition task is compared and analyzed. The results show that compared with the traditional underwater mode, the cross domain mode saves 74.7% of the time and 24.34% of the energy consumption, which proves the feasibility, stability and High efficiency of introducing the air submersible into the ocean sensor network.
