论文标题

部分可观测时空混沌系统的无模型预测

Seeker: Synergizing Mobile and Energy Harvesting Wearable Sensors for Human Activity Recognition

论文作者

Mishra, Cyan Subhra, Sampson, Jack, Kandemir, Mahmut Taylan, Narayanan, Vijaykrishnan

论文摘要

对新兴的超低功能互联网(IoT)设备的智能处理的需求越来越不断增加,并且最近的作品通过直接在IoT设备(NODE)上执行推理任务,而不仅仅是传输传感器数据,从而显示出很大的效率提高。但是,基于深神经网络(DNN)的推理对能量收获无线传感器网络(EH-WSN)中节点的挑战构成了重大挑战。此外,这些任务通常需要来自多个物理分布的EH传感器节点的响应,这些节点除了每个节点约束外,还会引起至关重要的系统优化挑战。 为了应对这些挑战,我们建议使用EH-WSN和主机移动设备有效地执行人类活动识别(HAR)任务的DNN推断,以有效地执行DNN推断。寻求者可以最大程度地减少通信开销,并最大程度地提高每个传感器的计算,而无需违反服务质量。 \ emph {seeker}使用\ emph {store and-octecute}方法完成对EH传感器节点上的推断子集,从而减少了与移动主机的通信。此外,对于由于收获的能量限制而未完成的推论,它利用\ emph {活动意识到的核心}(AAC)结构来有效地将紧凑的特征传达给主机设备,在该设备中,使用集合技术有效地完成了推论。 \ emph {seeker}以$ 86.8 \%$的精度执行HAR,超过了$ 81.2 \%$ $的精度。此外,通过使用AAC,它将通信数据量降低$ 8.9 \ times $。

There is an increasing demand for intelligent processing on emerging ultra-low-power internet of things (IoT) devices, and recent works have shown substantial efficiency boosts by executing inference tasks directly on the IoT device (node) rather than merely transmitting sensor data. However, the computation and power demands of Deep Neural Network (DNN)-based inference pose significant challenges for nodes in an energy-harvesting wireless sensor network (EH-WSN). Moreover, these tasks often require responses from multiple physically distributed EH sensor nodes, which imposes crucial system optimization challenges in addition to per-node constraints. To address these challenges, we propose \emph{Seeker}, a novel approach to efficiently execute DNN inferences for Human Activity Recognition (HAR) tasks, using both an EH-WSN and a host mobile device. Seeker minimizes communication overheads and maximizes computation at each sensor without violating the quality of service. \emph{Seeker} uses a \emph{store-and-execute} approach to complete a subset of inferences on the EH sensor node, reducing communication with the mobile host. Further, for those inferences unfinished because of harvested energy constraints, it leverages an \emph{activity aware coreset} (AAC) construction to efficiently communicate compact features to the host device where ensemble techniques are used to efficiently finish the inferences. \emph{Seeker} performs HAR with $86.8\%$ accuracy, surpassing the $81.2\%$ accuracy of a state of the art approach. Moreover, by using AAC, it lowers the communication data volume by $8.9\times$.

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