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

TL;DR

This paper proposes a community-based anomaly detection framework for IoT temperature sensors, leveraging sensor similarities to improve detection accuracy and reduce computational costs in large-scale sensor networks.

Contribution

It introduces a novel community grouping method using a fused similarity matrix and evaluates autoencoder models for anomaly detection within and across communities.

Findings

Robust within-community anomaly detection performance.

Variations observed in cross-community model generalization.

Community-based sharing reduces computational overhead.

Abstract

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.