Vol and Energy-Aware AUV-Assisted Data Collection for Internet of Underwater Things

In this study, an autonomous underwater vehicle (AUV) is considered to collect data in Internet of Underwater Things (loUT) networks. The AUV is tasked with timely visits to sensor nodes (SNs) to collect data using a navigation-hover-communication protocol. Considering the AUV's limited energy, the dynamic data upload demand of SNs and the diminishing Value of Information (Vol) during data transmission, efficient path planning for AUV is required. Therefore, we formulate a multi-objective optimization problem and employ the deep deterministic policy gradient (DDPG) algorithm to address it. Our objectives encompass the maximization of the sum data rate, the maximization of the sum Vol, and the minimization of the AUV's energy consumption within specified task time constraints. To mitigate the challenges posed by sparse rewards, we enhance the DDPG algorithm with hindsight experience replay (HER). The simulation results show that the data collection policy trained by our proposed algorithm can converge quickly and has excellent generalization. When the communication range changes, it can still effectively reduce the AUV's energy consumption while ensuring the quality and timeliness of the data collection task.