论文标题
通过水下传感器网络Q学习的任何路径路由协议设计
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks
论文作者
论文摘要
作为水下事物互联网上有前途的技术，水下传感器网络引起了学术界和工业的广泛关注。但是，由于水下环境中的高能耗和较大的延迟，为水下传感器网络设计一个路由协议是一个巨大的挑战。本文提出了一个基于Q学习的本地化任何路由（QLFR）协议，以延长寿命，并减少水下传感器网络的端到端延迟。针对最佳路由策略，通过在整个路由过程中共同考虑传感器节点的残留能量和深度信息来计算Q值。更具体地说，我们为Q学习定义了两个奖励函数（即与深度相关和能源相关的奖励），以减少延迟并延长网络寿命。此外，根据转发候选节点的优先级，设计了一种新的保留时间机制。此外，还提供了数学分析来分析提出的路由协议的性能。广泛的仿真结果表明，根据端到端延迟和网络寿命，提出的路由协议的优越性性能。
As a promising technology in the Internet of Underwater Things, underwater sensor networks have drawn a widespread attention from both academia and industry. However, designing a routing protocol for underwater sensor networks is a great challenge due to high energy consumption and large latency in the underwater environment. This paper proposes a Q-learning-based localization-free anypath routing (QLFR) protocol to prolong the lifetime as well as reduce the end-to-end delay for underwater sensor networks. Aiming at optimal routing policies, the Q-value is calculated by jointly considering the residual energy and depth information of sensor nodes throughout the routing process. More specifically, we define two reward functions (i.e., depth-related and energy-related rewards) for Q-learning with the objective of reducing latency and extending network lifetime. In addition, a new holding time mechanism for packet forwarding is designed according to the priority of forwarding candidate nodes. Furthermore, a mathematical analysis is presented to analyze the performance of the proposed routing protocol. Extensive simulation results demonstrate the superiority performance of the proposed routing protocol in terms of the end-to-end delay and the network lifetime.
