SDN-QLTR: Q-Learning-Assisted Trust Routing Scheme for SDN-Based Underwater Acoustic Sensor Networks

In underwater acoustic sensor networks (UASNs), the underwater sensors perform underwater data collection tasks, such as data collection and transmission at different locations in the monitoring area. To support cooperative underwater missions among the underwater sensor nodes, such as cooperative data delivery, one of the challenges is how to design smart underwater routing protocols that can guarantee safe, reliable, and energy-efficient data transfer among the underwater sensors. In this article, we introduce the paradigm of software-defined networking (SDN) and propose an SDN-based network framework for UASNs. Based on the proposed network framework, a $Q$ -learning-assisted trust routing scheme for SDN-based UASNs (SDN-QLTR) is proposed. The proposed SDN-QLTR aims to seek for a secure routing path for executing underwater data transmission. Note that, in SDN-QLTR, effective trust evaluation methods are designed to resist malicious attacks initiated by nodes in UASNs. And SDN-QLTR integrates the advantages of SDN and reinforcement learning algorithm, can be flexibly applied in UASNs with dynamic features. Simulation results show that SDN-QLTR performs better in network lifetime, latency, and reliability.