Probabilistic Neighborhood Location-Point Covering Set-Based Data Collection Algorithm With Obstacle Avoidance for Three-Dimensional Underwater Acoustic Sensor Networks

Data collection is the core function of underwater acoustic sensor networks (UASNs). Lately, ambulatory data gathering methods are being popularized in real applications. However, due to present mobile underwater data collection investigations that are on the basis of 2-D scenarios, the associated approaches are not suitable for 3-D UASNs. Additionally, mobile-element-assisted data collection usually brings special issues on obstacle avoidance. Accordingly, we propose a probabilistic neighborhood location-point covering set-based data collection algorithm with obstacle avoidance for 3-D UASNs. The proposed algorithm initially generates a space lattice set to establish the probabilistic neighborhood location-point covering set for data collection, so as to optimize the data collection latency. Then, an autonomous underwater vehicle traverses only location points in the constructed covering set with a hierarchical grid-based obstacle avoidance strategy. The simulation experiments are performed to verify the proposed algorithm compared with other existing underwater data collection algorithms. Simulations show that our proposed algorithm achieves better performance in terms of data collection latency, data collection efficiency, and obstacle avoidance.