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

arXiv: https://arxiv.org/abs/2310.17836 · HTML: https://mlsanity.com/paper/2310.17836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [Are Transformers universal approximators of sequence-to-sequence functions?](https://mlsanity.com/paper/1912.10077/) (2019)
- [Rethinking Positional Encoding in Language Pre-training](https://mlsanity.com/paper/2006.15595/) (2020)
- [Positional Encoding as Spatial Inductive Bias in GANs](https://mlsanity.com/paper/2012.05217/) (2020)
- [GraLSP: Graph Neural Networks with Local Structural Patterns](https://mlsanity.com/paper/1911.07675/) (2019)
- [Identification of Wearable Devices with Bluetooth](https://mlsanity.com/paper/1809.10387/) (2018)
- [A Survey on Conflict Detection in IoT-based Smart Homes](https://mlsanity.com/paper/2310.04447/) (2023)
- [A Conflict Detection Framework for IoT Services in Multi-resident Smart Homes](https://mlsanity.com/paper/2004.12702/) (2020)
- [Fine-grained Conflict Detection of IoT Services](https://mlsanity.com/paper/2007.12487/) (2020)
- [An Internet of Things Service Roadmap](https://mlsanity.com/paper/2103.03043/) (2021)
- [Dynamic Conflict Resolution of IoT Services in Smart Homes](https://mlsanity.com/paper/2110.07083/) (2021)
- [Adaptive Priority-based Conflict Resolution of IoT Services](https://mlsanity.com/paper/2107.08348/) (2021)
- [Occupancy Estimation from Thermal Images](https://mlsanity.com/paper/2110.07796/) (2021)
- [You Are What You Use: Usage-based Profiling in IoT Environments](https://mlsanity.com/paper/2209.02298/) (2022)

## Abstract (arXiv)

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.