Conflict Detection in IoT-based Smart Homes

Conflict Detection in IoT-based Smart Homes

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The idea of smart homes is gaining lots of interest nowadays. They improve the convenience of residents by developing ECA (Event-Condition-Action) rules, which connect dynamic events with expected actions. However, when numerous rules are employed, conflicting situations may occur.

Internet of things - artistic concept. Image credit: geralt | Free image via Pixabay

Internet of things – artistic concept. Image credit: geralt | Free image via Pixabay

Direct conflicts happen when different rules try to invoke the same service at the same time. Indirect conflicts, which are harder to detect, describe interference between several services, for instance, turning on cooling while windows are opened on a hot day.

A recent study proposes a method to detect both direct and indirect interventions. A graph model is proposed to capture the relations between services and environment properties. The researchers suggest a new conflict detection algorithm and validate the approach on real and synthesized datasets. The results demonstrate a sufficient performance and efficiency of the proposed framework.

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.

Research paper: Huang, B., Dong, H., and Bouguettaya, A., “Conflict Detection in IoT-based Smart Homes”, 2021. Link: https://arxiv.org/abs/2107.13179




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