State Prediction-Based Data Collection Algorithm in Underwater Acoustic Sensor Networks

In recent years, developments in data collection schemes based on multiple autonomous underwater vehicles (AUVs) are facilitating the realization of the so-called underwater acoustic sensor networks (UASNs). As yet, the lack of suitable collaboration mechanisms among multiple AUVs, which are based on functional or resource distributions, prevents effective information sharing and yields increased data collection delays, thus reducing the capacity of the networks. In this article, to address these shortcomings, we propose a state prediction-based data collection (SPDC) algorithm for UASNs. The principle of operation is as follows. First, some cluster pairs named observation clusters obtain and exchange the state information about AUVs between the adjacent subregions. Based on the shared information, the AUVs predict each other's status and adjust their data collection areas. Then, the AUVs use a heuristic strategy to complete the path planning based on the updated access area. Finally, a scheduling data forwarding mechanism reduces the diving number of the AUVs, by reasonably allocating the overlapped data unloading intervals between the AUVs and a mobile sink. Experimental results prove that the proposed algorithm shows satisfactory performance in reducing data collection delays and in improving the total network lifetime.