MARLIN-Q: Multi-modal communications for reliable and low-latency underwater data delivery
MARLIN-Q: Multi-modal communications for reliable and low-latency underwater data delivery
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DOI：
10.1016/j.adhoc.2018.08.003
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发表时间：
2019-01-01
期刊：
影响因子：
4.8
通讯作者：
Petrili, Chiara
中科院分区：
文献类型：
--
作者：
Basagni, Stefano;Di Valerio, Valerio;Petrili, Chiara
本文探讨了智能开发的多模态通信能力的水下节点，使可靠和快速的水下网络。在基于模型的强化学习方法之后，我们定义了一个框架，允许网络管理员为其数据选择最佳转发中继，并与最佳通信设备一起到达该中继。随着时间的推移，与相邻节点的通信质量也会影响选择，从而使节点能够适应水下信道的高度不利和快速变化的条件。由此产生的转发方法允许应用程序在不同类别的软服务质量（QoS）之间进行选择，例如，支持到目的地的可靠路由，或寻求更快的数据包传输。我们命名为我们的转发方法MARLIN-Q多模态强化学习为基础的软QoS路由。我们评估的性能MARLIN-Q在不同的网络场景中，节点通过两个声学调制解调器具有广泛不同的特性进行通信。MARLIN-Q与最先进的转发协议进行了比较，包括通道感知解决方案和基于机器学习的解决方案。我们的研究结果表明，一个聪明的学习选择的中继和调制解调器是关键，以获得数据包的交付率是其他协议的两倍，同时保持低延迟和能耗。(C)2018爱思唯尔B. V.保留所有权利。
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.