  Recent advances in Internet-of-Things (IoT) technologies have sparked
significant interest towards developing learning-based sensing applications on
embedded edge devices. These efforts, however, are being challenged by the
complexities of adapting to unforeseen conditions in an open-world environment,
mainly due to the intensive computational and energy demands exceeding the
capabilities of edge devices. In this paper, we propose OpenSense, an
open-world time-series sensing framework for making inferences from time-series
sensor data and achieving incremental learning on an embedded edge device with
limited resources. The proposed framework is able to achieve two essential
tasks, inference and incremental learning, eliminating the necessity for
powerful cloud servers. In addition, to secure enough time for incremental
learning and reduce energy consumption, we need to schedule sensing activities
without missing any events in the environment. Therefore, we propose two
dynamic sensor scheduling techniques: (i) a class-level period assignment
scheduler that finds an appropriate sensing period for each inferred class, and
(ii) a Q-learning-based scheduler that dynamically determines the sensing
interval for each classification moment by learning the patterns of event
classes. With this framework, we discuss the design choices made to ensure
satisfactory learning performance and efficient resource usage. Experimental
results demonstrate the ability of the system to incrementally adapt to
unforeseen conditions and to efficiently schedule to run on a
resource-constrained device.
