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There is an increasing demand for intelligent processing on emergingultra-low-power internet of things (IoT) devices, and recent works have shownsubstantial efficiency boosts by executing inference tasks directly on the IoTdevice (node) rather than merely transmitting sensor data. However, thecomputation and power demands of Deep Neural Network (DNN)-based inference posesignificant challenges for nodes in an energy-harvesting wireless sensornetwork (EH-WSN). Moreover, these tasks often require responses from multiplephysically distributed EH sensor nodes, which imposes crucial systemoptimization challenges in addition to per-node constraints.  To address these challenges, we propose \emph{Seeker}, a novel approach toefficiently execute DNN inferences for Human Activity Recognition (HAR) tasks,using both an EH-WSN and a host mobile device. Seeker minimizes communicationoverheads and maximizes computation at each sensor without violating thequality of service. \emph{Seeker} uses a \emph{store-and-execute} approach tocomplete a subset of inferences on the EH sensor node, reducing communicationwith the mobile host. Further, for those inferences unfinished because ofharvested energy constraints, it leverages an \emph{activity aware coreset}(AAC) construction to efficiently communicate compact features to the hostdevice 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 thecommunication data volume by $8.9\times$.

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