# Community-Based Model Sharing and Generalisation: Anomaly Detection in IoT Temperature Sensor Networks

arXiv: https://arxiv.org/abs/2601.05984 · HTML: https://mlsanity.com/paper/2601.05984/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art](https://mlsanity.com/paper/2004.00433/) (2020)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)
- [Towards a Machine Learning-driven Trust Evaluation Model for Social Internet of Things: A Time-aware Approach](https://mlsanity.com/paper/2102.10998/) (2021)
- [Graph Neural Network-Based Anomaly Detection for River Network Systems](https://mlsanity.com/paper/2304.09367/) (2023)
- [Detecting Anomalies within Smart Buildings using Do-It-Yourself Internet of Things](https://mlsanity.com/paper/2210.01840/) (2022)
- [Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection](https://mlsanity.com/paper/2401.03322/) (2024)

## Abstract (arXiv)

The rapid deployment of Internet of Things (IoT) devices has led to large-scale sensor networks that monitor environmental and urban phenomena in real time. Communities of Interest (CoIs) provide a promising paradigm for organising heterogeneous IoT sensor networks by grouping devices with similar operational and environmental characteristics. This work presents an anomaly detection framework based on the CoI paradigm by grouping sensors into communities using a fused similarity matrix that incorporates temporal correlations via Spearman coefficients, spatial proximity using Gaussian distance decay, and elevation similarities. For each community, representative stations based on the best silhouette are selected and three autoencoder architectures (BiLSTM, LSTM, and MLP) are trained using Bayesian hyperparameter optimization with expanding window cross-validation and tested on stations from the same cluster and the best representative stations of other clusters. The models are trained on normal temperature patterns of the data and anomalies are detected through reconstruction error analysis. Experimental results show a robust within-community performance across the evaluated configurations, while variations across communities are observed. Overall, the results support the applicability of community-based model sharing in reducing computational overhead and to analyse model generalisability across IoT sensor networks.