Underwater Searching and Multiround Data Collection via AUV Swarms: An Energy-Efficient AoI-Aware MAPPO Approach

Autonomous underwater vehicles (AUVs) play a crucial role in data collection for underwater acoustic sensor networks (UWASNs). The limited capacity of individual AUV and the need for low-latency data collection necessitate the deployment of AUV swarms to achieve efficient and secure cooperative data collection. However, most existing works assume prior knowledge of sensor node locations, which is impractical in real-world AUV networks. Additionally, continuous data collection needs to be considered due to the sustained operation of sensors and cluster head replacement. To address these challenges, we propose a target uncertainty map assisted data collection scheme for AUV swarms based on the multiagent proximal policy optimization (MAPPO) algorithm. Specifically, the target uncertainty map is established by leveraging current and past search and collection results, guiding the AUV swarm to prioritize areas with higher probabilities of containing sensor nodes. Moreover, a digital pheromone mechanism incorporating repulsive and attractive pheromones is designed to establish an artificial potential field for adjusting the target uncertainty map. To further enable a comprehensive exploration of unknown environments, we introduce the Age of Information (AoI) as an indicator. Additionally, we consider the energy consumption associated with data collection to strike a balance between collection and energy efficiency, and derive a lower bound on the policy improvement achieved by the MAPPO algorithm. Simulation results have validated that the proposed scheme has a superior performance compared to the baselines, achieving an approximately 15% increase in the collection rate while reducing the energy consumption of data collection and AoI as well.