Positional Encoding-based Resident Identification in Multi-resident
  Smart Homes
Zhiyi Song Dipankar Chaki Abdallah Lakhdari Athman Bouguettaya
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.