Conflict Detection in IoT-based Smart Homes
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 20 references that are in the map. First posted on arXiv 28 Jul 2021.
Builds on
4 of 20 references.
References · 4
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
- 2020A Conflict Detection Framework for IoT Services in Multi-resident Smart Homescited by 3
- 2020Fine-grained Conflict Detection of IoT Servicescited by 3
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approachcited by 0
- 2025Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authenticationcited by 0
- 2025Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Servicescited by 0
- 2025Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Services18 Aug 2025
- 2025Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication13 Aug 2025
- 2024Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach6 Jun 2024