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.

翻译：我们提出了一种新颖的居民识别框架，用于多居住者智能环境中的个体身份识别。该框架采用基于位置编码概念的特征提取模型，将家庭中的位置区域视为图结构。我们设计了一种新型算法，通过智能环境的布局图构建此类图结构，并利用Node2Vec算法将图转化为高维节点嵌入。随后引入长短期记忆网络（Long Short-Term Memory, LSTM）模型，结合传感器事件的时间序列与节点嵌入来预测居民身份。大量实验表明，所提方案能有效识别多居住者环境中的用户身份。在两个真实数据集上的评估结果显示，该方法分别达到了94.5%和87.9%的准确率。