Joint Trajectory Design and Resource Allocation Strategy for Underwater AUV-Aided MEC System

Sensor nodes (SNs) in underwater wireless sensor network (UWSN) may generate massive amounts of data and have computing task requirements. In this paper, mobile edge computing (MEC) is considered in UWSN. A resource allocation strategy in autonomous underwater vehicle (AUV)-aided MEC system is investigated to minimize the total system energy consumption. Specifically, the MEC servers are deployed at both AUV and buoy floating on the sea. By adopting magnetic induction (MI) communication technology, the AUV collects computing tasks from different SNs. Partial tasks are computed at the AUV, while the rest of tasks are offloaded to the buoy. The trajectory of the AUV, the transmitting power of SNs and the AUV, and the offloading ratio coefficient are jointly optimized. The suboptimal solution to the formulated optimization problem is obtained by using a social learning particle swarm optimization (SL-PSO) algorithm. Simulation results show that the proposed strategy has a faster convergence speed and better global search capability compared with other baseline schemes.