CORE: Connectivity Optimization via REinforcement Learning in WANETs
CORE: Connectivity Optimization via REinforcement Learning in WANETs
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DOI：
10.1109/secon52354.2021.9491597
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
A. Gorovits;Karyn Doke;Lin Zhang;M. Zheleva;Petko Bogdanov
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作者：
A. Gorovits;Karyn Doke;Lin Zhang;M. Zheleva;Petko Bogdanov
虽然移动设备无处不在，但它们的配套通信基础设施只有在人口密集的城市地区才具有成本效益，而在农村地区往往缺乏。这种连通性的缺乏导致在农村应急准备和反应等应用方面失去机会。使用可预测的人员移动性的点对点交换可以在农村环境中实现可容忍延迟的信息访问。我们提出了一种自适应分布式解决方案，用于无线自组织网络中通过强化学习（CORE）进行设备到设备连接优化。我们的解决方案专为具有间歇性连接和有限电池电量的协作分布式代理而设计，但在短期内具有可预测的移动性。我们寻求最大化连接尝试的效用，同时保持电力支出在预定义的电池预算内。智能体学习自适应地自动决定何时尝试连接和交换信息，这是基于他们的移动性的本地RL模型，以及他们从交换中了解到的其他智能体的模型。通过使用合成和现实世界的移动轨迹，我们证明了智能体能够使用20%的电池实现95%的可能连接，并在几天的学习中成功地适应潜在移动模式的变化。
While mobile devices are ubiquitous, their supporting communication infrastructure is cost-effective only in densely populated urban areas and is often lacking in rural settings. This lack of connectivity leads to lost opportunities in applications such as rural emergency preparedness and response. Peer-to-peer exchange that uses predictable human mobility can enable delay-tolerant information access in rural settings. We propose, an adaptive distributed solution for device-to-device Connectivity Optimization via REinforcement Learning (CORE) in wireless adhoc networks. Our solution is designed for collaborative distributed agents with intermittent connectivity and limited battery power, but predictable mobility within short temporal horizons. We seek to maximize the utility of connection attempts while keeping the power expenditure within a predefined battery budget. Agents learn to adaptively make automated decisions for when to attempt connections and exchange information, based on a local RL model of their mobility and that of other agents they learn about from exchanges. Using both synthetic and real-world mobility traces, we demonstrate that agents are able to materialize 95% of the possible connections using 20% of their battery and successfully adapting to changes in the underlying mobility patterns within several days of learning.