# Conflict Detection in IoT-based Smart Homes

arXiv: https://arxiv.org/abs/2107.13179 · HTML: https://mlsanity.com/paper/2107.13179/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [A Conflict Detection Framework for IoT Services in Multi-resident Smart Homes](https://mlsanity.com/paper/2004.12702/) (2020)
- [Fine-grained Conflict Detection of IoT Services](https://mlsanity.com/paper/2007.12487/) (2020)

## Built on by (most cited first, 3 of 3)

- [Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach](https://mlsanity.com/paper/2406.03836/) (2024)
- [Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication](https://mlsanity.com/paper/2508.09665/) (2025)
- [Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Services](https://mlsanity.com/paper/2508.12560/) (2025)

## Abstract (arXiv)

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.