DCADNT: Dynamic Topology Adaptive Data Collection Scheme Based on AUV for Mobile Underwater Wireless Sensor Networking

Abstract Underwater wireless sensor network (UWSN) has become one of the pivotal technologies in the advancement of smart oceans monitoring systems, with autonomous underwater vehicles (AUVs) offering a highly promising solution for mobile sensor data collection. In dynamic UWSN, the mobility of nodes leads to continuous network topology variations and unstable cluster structures, accompanied by dynamic cluster-head (CH) updates and intermittent acoustic communication links, which increase the difficulty and latency of AUV-based data collection. To overcome these above difficulties, this paper proposes a data collection algorithm orientated for dynamic network topology (DCADNT), consisting of three core phases: dynamic clustering with multi-criteria CH selection, propagation delay-aware adaptive dynamic transmission, and mobility prediction-based reliable AUV data collection. Different from conventional mobile UWSN methods, DCADNT innovatively breaks the continuous communication assumption, integrates time-varying topology dynamics, acoustic propagation delay characteristics, and link reliability constraints into a unified optimization framework. By calculating the packet delivery probability based on propagation delay, the reliability of data transmission can be effectively guaranteed. Extensive simulation results demonstrate that the proposed DCADNT algorithm effectively reduces collection latency and improves packet collection reliability in dynamic underwater networks.