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Reinforcement Learning Based Energy Management in Wireless Body Area Network: A Survey

机译:基于强化学习的无线体积电路能源管理:调查

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In modern life, personal health-care awareness is a fast-growing revolution. In which, Wireless Body Area Network (WBAN) allows inexpensive health-care services with the evaluation of modern devices. In particular, WBAN devices such as in-body sensors and coordinator become more decentralized and autonomous. Moreover, Reinforcement Learning (RL) type of machine learning is formulated to lead the WBAN devices to make an autonomous decision such as sensor access control, transmit power control, security against attack to improve the network performance, quality of service (QoS) and increase the overall utility of the network in an optimized way. In this paper, we provide a literature survey about WBAN and its application, challenges and issues. Finally, we present the application of RL has appeared with the sophisticated solution in the WBAN.
机译:在现代生活中,个人保健意识是一种快速增长的革命。其中,无线体积网络(WBAN)允许具有现代设备的评估廉价的保健服务。特别地,诸如体内传感器和协调器的WBAN设备变得更加分散和自主。此外,配制加强学习(RL)类型的机器学习,以引导WBAN设备,使传感器访问控制,传输功率控制,安全防止攻击的安全性决策,以提高网络性能,服务质量(QoS)和增加以优化的方式对网络的整体实用性。在本文中,我们为WAN和其应用,挑战和问题提供了关于WAN的文献调查。最后,我们提出了RL的应用出现了WBAN中复杂的解决方案。

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