  Advances in mobile computing capabilities and an increasing number of
Internet of Things (IoT) devices have enriched the possibilities of the IoT but
have also increased the cognitive load required of IoT users. Existing
context-aware systems provide various levels of automation in the IoT. Many of
these systems adaptively take decisions on how to provide services based on
assumptions made a priori. The approaches are difficult to personalize to an
individual's dynamic environment, and thus today's smart IoT spaces often
demand complex and specialized interactions with the user in order to provide
tailored services. We propose rIoT, a framework for seamless and personalized
automation of human-device interaction in the IoT. rIoT leverages existing
technologies to operate across heterogeneous devices and networks to provide a
one-stop solution for device interaction in the IoT. We show how rIoT exploits
similarities between contexts and employs a decision-tree like method to
adaptively capture a user's preferences from a small number of interactions
with the IoT space. We measure the performance of rIoT on two real-world data
sets and a real mobile device in terms of accuracy, learning speed, and latency
in comparison to two state-of-the-art machine learning algorithms.
