CARMA: Channel-Aware Reinforcement Learning-Based Multi-Path Adaptive Routing for Underwater Wireless Sensor Networks
CARMA: Channel-Aware Reinforcement Learning-Based Multi-Path Adaptive Routing for Underwater Wireless Sensor Networks
复制标题
DOI：
10.1109/jsac.2019.2933968
复制
发表时间：
2019-11-01
影响因子：
16.4
通讯作者：
Basagni, Stefano
中科院分区：
文献类型：
--
作者：
Di Valerio, Valerio;Lo Presti, Francesco;Basagni, Stefano
多跳水下无线传感器网络的路由解决方案由于无法适应水下环境的巨大动态而遭受显着的性能下降。为了应对这一挑战，我们提出了一种新的数据转发方案，其中中继选择可以快速适应水下信道的变化条件。我们的协议被称为基于通道感知强化学习的多路径自适应路由的 CARMA，在分布式强化学习框架的指导下自适应地在单路径和多路径路由之间切换，共同优化路由长能耗和数据包传递率。我们通过基于 SUNSET 的海上模拟和实验，将 CARMA 与其他三种路由解决方案（即 CARP、QELAR 和 EFlood）的性能进行了比较。我们的结果表明，CARMA 的数据包传送率比所有其他协议高出 40%。 CARMA 传输数据包的速度也明显快于 CARP、QELAR 和 EFlood，同时保持网络能耗。
Routing solutions for multi-hop underwater wireless sensor networks suffer significant performance degradation as they fail to adapt to the overwhelming dynamics of underwater environments. To respond to this challenge, we propose a new data forwarding scheme where relay selection swiftly adapts to the varying conditions of the underwater channel. Our protocol, termed CARMA for Channel-aware Reinforcement learning-based Multi-path Adaptive routing, adaptively switches between single-path and multi-path routing guided by a distributed reinforcement learning framework that jointly optimizes route-long energy consumption and packet delivery ratio. We compare the performance of CARMA with that of three other routing solutions, namely, CARP, QELAR and EFlood, through SUNSET-based simulations and experiments at sea. Our results show that CARMA obtains a packet delivery ratio that is up to 40% higher than that of all other protocols. CARMA also delivers packets significantly faster than CARP, QELAR and EFlood, while keeping network energy consumption at bay.