Routing Void Prediction and Repairing in AUV-Assisted Underwater Acoustic Sensor Networks

Abstract

Underwater Acoustic Sensor Networks have attracted much attention due to various applications. However, routing voids lead performance degradation of UASNs in terms of network connectivity and packet delivery ratio. In this paper, we propose a Routing Void Prediction and Repairing (RVPR) algorithm in AUV-assisted UASNs, which utilizes AUVs to carry sensor nodes to repair the routing voids when foreseeing the occurrence of voids. First, the repair position is calculated based on Particle Swarm Optimization algorithm by maximizing the connectivity of the void area and minimizing the AUV moving distance. Then, the routing void prediction based on Markov chain model is proposed to ensure that the AUVs come to the repair task before the voids have already formed. Next, we design a task selecting rule to let the AUVs choose the most important and urgent repair task. Lastly, RVPR applies an energy-efficient interaction mechanism among nodes and AUVs, which guarantees reliable operation of the algorithm. In the simulation, the RVPR algorithm is applied in several different types of routing protocols (HHVBF, QELAR, EAVARP). The simulation results show that RVPR algorithm improves the protocol performances in terms of the packet delivery ratio and the link connection. More specifically, when there are 100 nodes deployed in the network, the packet delivery ratio of HHVBF, EAVAPR and QELAR employing RVPR are increased by 29.4%,79% and 65% respectively.