  Many real-world applications of the Internet of Things (IoT) employ machine
learning (ML) algorithms to analyze time series information collected by
interconnected sensors. However, distribution shift, a fundamental challenge in
data-driven ML, arises when a model is deployed on a data distribution
different from the training data and can substantially degrade model
performance. Additionally, increasingly sophisticated deep neural networks
(DNNs) are required to capture intricate spatial and temporal dependencies in
multi-sensor time series data, often exceeding the capabilities of today's edge
devices. In this paper, we propose SMORE, a novel resource-efficient domain
adaptation (DA) algorithm for multi-sensor time series classification,
leveraging the efficient and parallel operations of hyperdimensional computing.
SMORE dynamically customizes test-time models with explicit consideration of
the domain context of each sample to mitigate the negative impacts of domain
shifts. Our evaluation on a variety of multi-sensor time series classification
tasks shows that SMORE achieves on average 1.98% higher accuracy than
state-of-the-art (SOTA) DNN-based DA algorithms with 18.81x faster training and
4.63x faster inference.
