Dynamic Trajectory-Based Adaptive Scheduling Scheme for AUV-Assisted Data Collection in UASNs

Autonomous Underwater Vehicle (AUV)-assisted data collection has been increasingly employed in various underwater applications, such as ocean exploration and marine rescue. Most of the existing schemes consider scenarios where the global information of sensor nodes is known, so that the path of AUV can be planned before it performs the task. Moreover, the interaction processes between the AUV and the sensor nodes are often overlooked. In this paper, we design a Dynamic Trajectory-based Adaptive Scheduling (DTAS) data collection scheme, in which the AUV dynamically adjusts its trajectory according to the real-time status information of the sensor nodes and the interaction rules between them are specified. While cruising in the sea, the AUV wakes up sensor nodes with data transmission demand and acquires their real-time status information through message exchange. With the real-time status information, a data collection efficiency-based strategy is proposed to dynamically determine the hovering point for AUV to receive data from the sensor nodes. To reduce packet collision and improve channel utilization, we organize data transmissions in a scheduling manner. In packet scheduling, transmission priorities are assigned to sensor nodes with the objective of maximizing the urgency-weighted Value of Information (VoI). Furthermore, in the Finite State Machine (FSM), we implement specific configurations to conserve sensor node energy and prevent the AUV from missing nodes due to Request to Send (RTS) message collisions. Simulation results demonstrate the superior performance of the proposed scheme in terms of data collection efficiency, network throughput, and total VoI.