Multi-AUV Collaboration-Assisted Location Privacy Protection Scheme in Unknown Marine Environments

With the increase in the status of the ocean, the development of ocean resources is a priority, as is ocean security. Ocean security contains several aspects, such as equipment security, data security, military security, and so on. Location privacy protection of ocean equipment during data collection missions in unknown environments is investigated and a multiple autonomous underwater vehicles (AUVs) collaboration-assisted location privacy-preserving scheme is proposed in this thesis. In an unknown environment, several AUVs gather together to form a swarm to perform regional detection and defense missions. The swarm may face potential active and passive attacks, and the leader AUV is in the priority of protection. In this case, the follower AUVs deploy nodes in an unknown environment to make a rough sense. Then, the nodes build a Voronoi diagram based on their own perceptions to form a graph. Based on this graph, the AUV divides the nodes into regions by density-based spatial clustering of applications with noise (DBSCAN) and uses rapidly-exploring random trees (RRT) to plan the path in an unknown environment. Finally, the fake data along with the randomness of the moving follower AUV contribute to the location privacy-preserving during the regional detection and defense mission. Compared with the previous two schemes, the simulation results of the proposed method show some advantages in certain metrics.