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We propose a novel resident identification framework to identify residents ina multi-occupant smart environment. The proposed framework employs a featureextraction model based on the concepts of positional encoding. The featureextraction model considers the locations of homes as a graph. We design a novelalgorithm to build such graphs from layout maps of smart environments. TheNode2Vec algorithm is used to transform the graph into high-dimensional nodeembeddings. A Long Short-Term Memory (LSTM) model is introduced to predict theidentities of residents using temporal sequences of sensor events with the nodeembeddings. Extensive experiments show that our proposed scheme effectivelyidentifies residents in a multi-occupant environment. Evaluation results on tworeal-world datasets demonstrate that our proposed approach achieves 94.5% and87.9% accuracy, respectively.

Positional Encoding-based Resident Identification in Multi-resident Smart Homes