Positional Encoding-based Resident Identification in Multi-resident Smart Homes
This addresses the challenge of resident identification in smart homes for applications like personalized automation, though it appears incremental as it builds on existing methods like Node2Vec and LSTM.
The paper tackles the problem of identifying residents in multi-occupant smart homes by proposing a framework that uses positional encoding and graph-based feature extraction, achieving accuracies of 94.5% and 87.9% on two real-world datasets.
We propose a novel resident identification framework to identify residents in a multi-occupant smart environment. The proposed framework employs a feature extraction model based on the concepts of positional encoding. The feature extraction model considers the locations of homes as a graph. We design a novel algorithm to build such graphs from layout maps of smart environments. The Node2Vec algorithm is used to transform the graph into high-dimensional node embeddings. A Long Short-Term Memory (LSTM) model is introduced to predict the identities of residents using temporal sequences of sensor events with the node embeddings. Extensive experiments show that our proposed scheme effectively identifies residents in a multi-occupant environment. Evaluation results on two real-world datasets demonstrate that our proposed approach achieves 94.5% and 87.9% accuracy, respectively.