  Machine Learning (ML) has been applied to enable many life-assisting
appli-cations, such as abnormality detection and emdergency request for the
soli-tary elderly. However, in most cases machine learning algorithms depend on
the layout of the target Internet of Things (IoT) sensor network. Hence, to
deploy an application across Heterogeneous Sensor Networks (HSNs), i.e. sensor
networks with different sensors type or layouts, it is required to repeat the
process of data collection and ML algorithm training. In this paper, we
introduce a novel framework leveraging deep learning for graphs to enable using
the same activity recognition system across HSNs deployed in differ-ent smart
homes. Using our framework, we were able to transfer activity classifiers
trained with activity labels on a source HSN to a target HSN, reaching about
75% of the baseline accuracy on the target HSN without us-ing target activity
labels. Moreover, our model can quickly adapt to unseen sensor layouts, which
makes it highly suitable for the gradual deployment of real-world ML-based
applications. In addition, we show that our framework is resilient to
suboptimal graph representations of HSNs.
