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首页> 外文会议>International conference on computer design >Puppet: Energy Efficient Task Mapping For Storage-Less and Converter-Less Solar-Powered Non-Volatile Sensor Nodes
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Puppet: Energy Efficient Task Mapping For Storage-Less and Converter-Less Solar-Powered Non-Volatile Sensor Nodes

机译：木偶：用于存储少和转换器少的太阳能非易失性传感器节点的节能任务映射

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摘要

Solar powered sensor nodes have been adopted in many applications, but unstable energy source and high energy loss are hindrances to their wide spreading. Storage-less and converter-less solar powered non-volatile sensor nodes reduce the energy loss to a great extent. However, without energy buffers, sensor nodes become more sensitive to solar variations. Making full use of harvested energy to provide better quality of services (QoS) to guarantee stable operations under this circumstance is crucial. In this paper, we devise an energy efficient task mapping strategy for storage-less and converter-less solar powered non-volatile sensor nodes. The proposed strategy, Puppet uses a reinforcement learning to make nodes achieve higher energy utilization and finally enhance the QoS. Experimental results show that the proposed strategy reduces the deadline miss ratio (DMR) in Puppet by 22% while increases energy utilization and effective energy utilization by 11% and 25%, on the average, respectively.
机译：太阳能传感器节点已在许多应用中采用，但是不稳定的能源和高能量损耗是其广泛传播的障碍。无存储和无转换器的太阳能非易失性传感器节点在很大程度上减少了能量损失。但是，如果没有能量缓冲器，则传感器节点对太阳的变化变得更加敏感。充分利用所收集的能量来提供更好的服务质量（QoS），以确保在这种情况下的稳定运行至关重要。在本文中，我们为无存储和无转换器的太阳能非易失性传感器节点设计了一种节能任务映射策略。所提出的策略Puppet使用强化学习来使节点实现更高的能源利用率，并最终提高QoS。实验结果表明，所提出的策略将Puppet的最后期限未命中率（DMR）降低了22％，而能源利用率和有效能源利用率平均分别提高了11％和25％。

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