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首页> 外文会议>2017 IEEE SmartWorld, Ubiquitous Intelligence amp; Computing, Advanced amp; Trusted Computed, Scalable Computing amp; Communications, Cloud amp; Big Data Computing, Internet of People and Smart City Innovation >DiNAMAC: A disruption tolerant, reinforcement learning-based Mac protocol for implantable body sensor networks
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DiNAMAC: A disruption tolerant, reinforcement learning-based Mac protocol for implantable body sensor networks

机译：DiNAMAC：一种用于植入式人体传感器网络的基于容错的，基于强化学习的Mac协议

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摘要

Ongoing advancements in Body Sensor Networks (BSN) have enabled continuous health monitoring of chronically ill patients, with the use of implantable and body worn sensor nodes. Inevitable day-to-day activities such as walking, running, and sleeping cause severe disruptions in the wireless link among these sensor nodes, resulting in temporary shadowing of wireless signals. These disruptions in the wireless link not only reduce the reliability of the network but also increase the power consumption. Both signal disruption and power consumption must be reduced in order to achieve long term monitoring of physiological signals in chronic patients. In this paper we propose a MAC protocol called DiNAMAC (Disruption tolerant reiNforcement leArning-based MAC), which is not only aware of the wireless link quality but also is aware of network resource availability and application requirements. DiNAMAC uses reinforcement learning to adapt the scheduling based on channel conditions and to prioritize data transmission and availability according to the application requirements. In addition, we design DiNAMAC based on a model-free learning technique to make it more practical in real-world applications. Our simulation results show that DiNAMAC performs better than conventional MAC protocols in terms of latency and throughput even with when the wireless link quality is challenged by large temporal variations.
机译：身体传感器网络（BSN）的不断发展，通过使用可植入和身体佩戴的传感器节点，可以对慢性病患者进行持续的健康监测。诸如步行，跑步和睡觉之类的不可避免的日常活动会导致这些传感器节点之间的无线链接严重中断，从而导致无线信号的暂时阴影。无线链路中的这些中断不仅降低了网络的可靠性，而且增加了功耗。为了长期监测慢性病人的生理信号，必须减少信号干扰和功耗。在本文中，我们提出了一种称为DiNAMAC（基于容错增强强化学习的MAC）的MAC协议，该协议不仅了解无线链路质量，而且还了解网络资源的可用性和应用需求。 DiNAMAC使用强化学习来根据信道条件调整调度，并根据应用要求对数据传输和可用性进行优先级排序。此外，我们基于无模型学习技术设计DiNAMAC，使其在实际应用中更加实用。我们的仿真结果表明，即使在无线链路质量受到较大的时间变化挑战的情况下，DiNAMAC在延迟和吞吐量方面也比常规MAC协议更好。

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