Joint optimization of AoI and energy for AUV-assisted data collection in underwater acoustic sensor networks

Underwater data collection is a key component of ocean observation systems, providing reliable data provenance through sensor networks deployed across diverse marine environments. Timely and energy-efficient data collection mechanisms are critical for ensuring the continuity and integrity of the collected data. For the sake of jointly optimizing the age of information (AoI) and energy consumption, we propose a multi-autonomous underwater vehicle (AUV)-assisted underwater data collection scheme in this paper. The optimization problem is formulated as a mixed integer linear programming (MILP) problem, which is non-deterministic polynomial (NP)-hard. In order to address the problem efficiently, we propose a two-stage method. First, considering the balance of energy consumption and the limitation of sensor communication ranges, we design a cluster-based AUV scheduling data transmission protocol to find a series of hovering points at which the AUVs hover to receive data and schedule data transmissions between the AUVs and sensors. Based on that, a combined heuristic algorithm is proposed to plan AUVs’ trajectories so that the data collection task can be completed properly with a low cost. Simulation results show that the proposed algorithm outperforms three benchmark algorithms in terms of average AoI and collection utility. Specifically, the freshness of information and the collection utility improved by 10.23% and 33.36% on average, respectively. Additionally, the energy consumption of sensor nodes is more balanced, and the network lifetime is extended.