Emerging applications for Internet of Things (IoT) devices demand smaller mass, size, and cost whilst increasing capability and reliability. Energy harvesting can provide power to these ultra-constrained devices, but introduces unreliability, unpredictability, and intermittency. Schemes for wireless sensors without batteries or supercapacitors overcome intermittency through saving system state into nonvolatile memory before the supply drops below the minimum operating voltage, termed transient, or intermittent computing. However, this introduces significant time and energy overheads. This article presents two schemes that significantly reduce these overheads: entering a sleep mode to avoid saving state and utilizing direct memory access (DMA) when state saves are required. Time and energy previously wasted on state saves can instead be used to perform useful computation, termed “forward progress.” We practically validate the proposed approaches across a range of energy sources and IoT benchmarks and demonstrate up to 46.8% and 40.3% increase in forward progress and up to 91.1% and 85.6% reduction in overheads for each scheme, respectively.

中文翻译：

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从全景图像中提取大型建筑资产


物联网 (IoT) 设备的新兴应用需要更小的质量、尺寸和成本，同时提高功能和可靠性。能量收集可以为这些超受限设备提供电力，但会带来不可靠性、不可预测性和间歇性。没有电池或超级电容器的无线传感器方案通过在电源降至最低工作电压以下之前将系统状态保存到非易失性存储器中来克服间歇性，称为瞬态或间歇性计算。然而，这会带来大量的时间和精力开销。本文提出了两种显着减少这些开销的方案：进入睡眠模式以避免保存状态，以及在需要状态保存时利用直接内存访问 (DMA)。以前浪费在状态保存上的时间和精力可以用来执行有用的计算，称为“前进进度”。我们在一系列能源和物联网基准上实际验证了所提出的方法，并证明每个方案的前进进度分别提高了 46.8% 和 40.3%，管理费用减少了 91.1% 和 85.6%。