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.

我们提出了一种新颖的居民识别框架，用于在多居住者智能环境中识别居民。该框架利用了基于位置编码概念的特征提取模型，该模型将住宅位置视为一个图形。我们设计了一种新颖的算法，从智能环境的布局图构建这样的图形。Node2Vec 算法用于将图形转换为高维节点嵌入。引入了长短期记忆（LSTM）模型，使用节点嵌入与传感器事件的时间序列来预测居民的身份。大量的实验证明我们提出的方案有效地在多居住者环境中识别居民。两个实际数据集的评估结果分别达到 94.5％和 87.9％的准确率。