Smart home technology has profoundly changed modern living by interconnecting devices, services, dataflows, and user interactions into integrated, automated environments. Homeowners can easily program smart devices using conditional IF-THEN rules, where triggers prompt corresponding actions. However, as smart homes incorporate more multifunctional devices, conflicting trigger-action rules can simultaneously control devices in inconsistent ways, causing unexpected and potentially unsafe interference situations. This article introduces Sigfrid, a novel interference detection approach using scene interaction graphs constructed through Large Language Models (LLMs). To enhance LLM reasoning, we propose a new prompt engineering methodology that integrates automated and manual editing techniques to formulate queries for deriving causal insights in the smart home domain. Interferences are identified through efficient exploration of the graph constructed from the extracted relations. We evaluate Sigfrid on real-world If-This-Then-That (IFTTT) and SmartThings rule sets, demonstrating its superiority over state-of-the-art methods by more than 21% in F1-score.
SIGFRID: Unsupervised, Platform-Agnostic Interference Detection in IoT Automation Rules
Cimino G.;Deufemia V.
2025
Abstract
Smart home technology has profoundly changed modern living by interconnecting devices, services, dataflows, and user interactions into integrated, automated environments. Homeowners can easily program smart devices using conditional IF-THEN rules, where triggers prompt corresponding actions. However, as smart homes incorporate more multifunctional devices, conflicting trigger-action rules can simultaneously control devices in inconsistent ways, causing unexpected and potentially unsafe interference situations. This article introduces Sigfrid, a novel interference detection approach using scene interaction graphs constructed through Large Language Models (LLMs). To enhance LLM reasoning, we propose a new prompt engineering methodology that integrates automated and manual editing techniques to formulate queries for deriving causal insights in the smart home domain. Interferences are identified through efficient exploration of the graph constructed from the extracted relations. We evaluate Sigfrid on real-world If-This-Then-That (IFTTT) and SmartThings rule sets, demonstrating its superiority over state-of-the-art methods by more than 21% in F1-score.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
