Tsunamis have been recognized as one of the most terrible and powerful marine natural disasters that cause huge casualties to coastal residents and facilities when they occur. Therefore, it is of vital importance to establish a disaster and tsunami early warning system by exploring the propagation speed difference of the tsunami itself through sea surface and the associated seismic wave along the seabed. The main approach of current disaster and tsunami warning systems is to deploy sensor nodes on the bottom of the seabed to monitor changes in seabed pressure and transmit the data back to the shore. In view of the characteristics of warning systems, with the purpose to improve early warning efficiency of the bottom deployed deep sea sensor network, this study utilizes swarm intelligence algorithms and combines the features of deep sea Reliable Acoustic Paths (RAPs) to optimize the topology of monitoring nodes deployed on the bottom of the deep sea by formulating the problem as optimizing the coverage of monitoring nodes deployed on the deep-sea floor and enhancing their backhaul capability. Through simulation experiments, it provides a reference for selecting appropriate optimization methods in different deployment scenarios, in terms of the quantitative performance improvement of Deep Sea Acoustic Sensor Network (DSASN) enabled tsunami early-warning systems.
Lin et al. (Fri,) studied this question.