Multi-AUV Cooperative Data Collection for Underwater Acoustic Sensor Networks Using Stackelberg Game

Due to the challenges of low bandwidth, high latency, and high communication energy costs in acoustic channel communication, data collection in underwater acoustic sensor networks (UASNs) remains severely restricted. The assistance of multiple autonomous underwater vehicles (AUVs) in data collection brings the advantages of low latency and energy-efficient balance. However, existing AUV-assisted data collection schemes preplan paths and assume data transmission from all nodes, which is inefficient in information delay-sensitive monitoring scenarios. Furthermore, most schemes do not consider the influence of ocean currents when studying path planning. To solve these problems, this article proposes a joint optimization scheme for multi-AUV cooperative data collection based on the Stackelberg game, where the task allocation and path planning problems are formulated as the leader and follower of the Stackelberg game, respectively. At the leader level, a sequential game-based algorithm is proposed, dynamically allocating cluster heads that require data transmission to different AUVs. At the follower level, the influence of ocean currents is considered, and an improved backtracking method is employed to plan the optimal path for each AUV. Experimental results show that the proposed method significantly improves network performance when compared with other representative underwater data collection methods.