Applied Fuzzy Knowledge Graph Model for Monitoring Smart Home Under Uncertain Environments

Smart home provision faces many challenges, resulting from the growing demand for monitoring smart home services in uncertain environments, such as rural areas, and regions with high humidity, pollution, and unstable electricity supply. Effective data processing in decision-support cyber–physical systems for smart environments is challenging given that the raw data [collected from context-aware networked sensor-based systems under dynamic uncertainty] is typically imprecise, inaccurate, and potentially incomplete. Addressing this challenge requires effective data processing. To address this challenge, in this paper we present our proposed novel Fuzzy Knowledge Graph Model to enable the processing of raw data derived from a smart home environment. The experimental testing of the proposed model includes data preparation, reasoning, and action control. To evaluate the performance of the proposed model we utilise four evaluation metrics: accuracy, precision, recall, and F1-score. The evaluation shows that the proposed model achieves good statistically significant results in terms of accuracy (recall: 100% with low false positives; precision: 94.74%), leading to an F1-score of 97.3%. The results indicate that the proposed model demonstrates high levels of performance in terms of accuracy, with good precision, recall, and F1-score. Moreover, the reported results confirm the reliability, stability, efficacy, and utility of our proposed model, which we posit has the capability to be generalised to smart home domains.