Conflict Detection in IoT-based Smart Homes
Bing Huang, Hai Dong, Athman Bouguettaya

TL;DR
This paper introduces a framework for detecting conflicts in IoT-based smart homes using a knowledge graph approach, aiming to improve safety and reliability in smart home environments.
Contribution
It presents a novel conflict detection framework with a knowledge graph model and a conflict taxonomy tailored for IoT smart homes, validated through experiments.
Findings
Effective conflict detection demonstrated on real datasets
High efficiency in identifying potential conflicts
Versatile framework adaptable to various smart home settings
Abstract
We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home setting based on the context information. We propose a conflict taxonomy to capture different types of conflicts in a single resident smart home setting. A conflict detection algorithm is proposed to identify potential conflicts using the profiled knowledge graph. We conduct a set of experiments on real datasets and synthesized datasets to validate the effectiveness and efficiency of our proposed approach.
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Taxonomy
TopicsIoT and Edge/Fog Computing · Context-Aware Activity Recognition Systems · Mobile Crowdsensing and Crowdsourcing
