rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things

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.

I. Introduction

With recent technology advances made in the Internet-of-Things (IoT), there is a growing number of smart devices helping to build the many smart-* scenarios that people have long envisioned [40] . In scenarios like smart-homes and smart-offices, the plethora of these new devices has created many possibilities for automating daily tasks. At the same time, new challenges arise; a particular challenge to note is that applications demand responsive and intelligent approaches to leverage context [10] in IoT environments. In this work, we address a fundamental piece of this challenge: automating human-device interaction, by asking a simple yet unsolved question: how can contextual information be leveraged to make IoT device interaction more seamless and personalized ?