基于Q学习的蚁群优化水声网络协议

针对水声通信中数据传输延时高且动态适应性弱的问题, 提出了一种基于Q学习优化的蚁群智能水声网络路由协议(Q-learning ant colony optimization, QACO). 协议包括路由行为和智能决策部分, 在路发现和维护阶段, 依靠网络智能蚂蚁进行网络拓扑环境的构建和节点之间的信息交换以及网络的维护. 在Q学习阶段, 通过定量化节点能量和深度以及网络传输延时学习特征作为折扣因子和学习率, 以延长网络的生命周期, 降低系统能耗和延时. 最后通过水声网络环境进行仿真, 实验结果表明QACO在能耗、延迟和网络生命周期方面都优于基于Q学习辅助的蚁群算法(Q-learning aided ant colony routing protocol, QLACO)和基于 Q-learning 的节能和生命周期感知路由协议(Q-learning-based energy-efficient and lifetime-aware routing protocol, QELAR)和基于深度路由协议 (depth-based routing, DBR)算法.

Ant Colony Optimization Based on Q-learning for Underwater Acoustic Network Protocol

To solve the problems such as high data transmission delay and weak dynamic adaptability of underwater acoustic communication, this study proposes an intelligent underwater acoustic network routing protocol based on Q-learning ant colony optimization (QACO). The protocol includes routing behavior and intelligent decision. In the route discovery and maintenance phase, the construction of the network topology environment and information exchange among nodes as well as the network maintenance are carried out by intelligent NetAnts. In the Q-learning phase, the node energy and depth and network transmission delay learning characteristics are quantified as discount factors and learning rates to extend the network lifecycle and reduce system energy consumption and delay. Finally, simulations are carried out through the underwater acoustic network environment, and the experimental results show that QACO outperforms the Q-learning aided ant colony routing protocol (QLACO), Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR), and depth-based routing (DBR) algorithm in terms of energy consumption, delay, and network lifecycle.