  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 $8.9\times$.
