Impact Conflict Detection of IoT Services in Multi-resident Smart Homes

TL;DR

This paper introduces a new framework for detecting conflicts among IoT services in multi-resident smart homes, utilizing impact assessment, preference estimation, and proximity techniques to improve conflict detection accuracy.

Contribution

It presents a novel impact conflict detection framework that combines impact assessment, preference modeling, and proximity analysis for IoT services in smart homes.

Findings

Effective conflict detection demonstrated on real-world datasets

Improved accuracy over existing methods

Robust preference estimation model

Abstract

We propose a novel impact conflict detection framework for IoT services in multi-resident smart homes. The proposed impact assessment model is developed based on the integral of a signal deviation strategy. We mine the residents' previous service usage records to design a robust preference estimation model. We design an impact conflict detection approach using temporal proximity and preferential proximity techniques. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach.