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

2026-01-09

arXiv.org

AI Summary

This work proposes an efficient anomaly detection framework based on Communities of Interest (CoI) to address the high computational overhead and limited generalization capability in large-scale heterogeneous IoT temperature sensor networks. By integrating temporal (Spearman correlation), spatial (Gaussian distance decay), and elevation-based similarity, the approach constructs sensor communities and trains a shared autoencoder model—employing BiLSTM, LSTM, or MLP architectures—for each community, leveraging reconstruction error for anomaly identification. Coupled with Bayesian hyperparameter optimization and an expanded window cross-validation strategy, the method significantly reduces training costs while achieving high detection accuracy within communities and effective generalization across them. The results demonstrate the advantage of multidimensional similarity-driven community modeling in balancing computational efficiency and detection performance.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• anomaly detection
• IoT sensor networks
• Community-Based Model Sharing
• model generalisation
• temperature sensors

Innovation

• Community-Based Model Sharing
• Anomaly Detection
• Autoencoder
• IoT Sensor Networks
• Bayesian Hyperparameter Optimization