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

TL;DR

rIoT is a framework that enables seamless, personalized, and adaptive automation in IoT environments by leveraging context similarities and decision trees, improving user interaction efficiency.

Contribution

The paper introduces rIoT, a novel framework that enhances IoT automation personalization and adaptability using context similarity and decision-tree techniques.

Findings

rIoT achieves higher accuracy than existing algorithms.

It demonstrates faster learning speed in real-world tests.

It reduces latency in IoT device interactions.

Abstract

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.