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

• URL: http://arxiv.org/abs/2310.17836v1
• Date: Fri, 27 Oct 2023 01:29:41 GMT
• Title: Positional Encoding-based Resident Identification in Multi-resident Smart Homes
• Authors: Zhiyi Song, Dipankar Chaki, Abdallah Lakhdari, Athman Bouguettaya
• Abstract summary: 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. We design a novel algorithm to build such graphs from layout maps of smart environments.
• Score: 1.2084539012992412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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