期刊
AD HOC NETWORKS
卷 82, 期 -, 页码 134-145出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.adhoc.2018.08.003
关键词
Underwater wireless sensor networks; Multi-modal communications; Reinforcement learning-based routing; Soft QoS
资金
- EC EASME ArcheoSub project Autonomous underwater Robotic and sensing systems for Cultural Heritage discovery Conservation and in situ valorization [EASME/EMFF/2016/1.2.1.4/01/SI2.749264]
- Sapienza Visiting Professor programme (2016)
- [NSF CNS 1428567]
- [NSF CNS 1726512]
This paper explores the smart exploitation of multi-modal communication capabilities of underwater nodes to enable reliable and swift underwater networking. Following a model-based reinforcement learning approach, we define a framework allowing senders to select the best forwarding relay for its data jointly with the best communication device to reach that relay. The choice is also driven by the quality of the communication to neighboring nodes over time, thus allowing nodes to adapt to the highly adverse and swiftly varying conditians of the underwater channel. The resulting forwarding method allows applications to choose among different classes of soft Quality of Service (QoS), favoring, for instance, reliable routes to the destination, or seeking faster packet delivery. We name our forwarding method MARLIN-Q for Multi-modAl Reinforcement Learning-based RoutINg with soft QoS. We evaluate the performance of MARLIN-Q in varying networking scenarios where nodes communicate through two acoustic modems with widely different characteristics. MARLIN-Q is compared to state-of-the-art forwarding protocols, including a channel-aware solution, and a machine learning-based solution. Our results show that a smartly learned selection of relay and modem is key to obtain a packet delivery ratio that is twice as much that of other protocols, while maintaining low latency and energy consumption. (C) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据