Weather-Aware Anomaly Detection in Cloud-Assisted THz-Enabled IoT Networks: A Comparative Study of Isolation Forest and Deep Autoencoders
Ngobum Chinwengozi Ibe
Progress in Computing Applications, Vol. 15, No. 2, 2026.
DOI: https://doi.org/10.6025/pca/2026/15/2/66-81
Link: https://www.dline.info/pca/fulltext/v15n2/pcav15n2_2.pdf
Abstract:
The deployment of Terahertz (THz) communications in dense Internet of Things (IoT) networks promises
unprecedented data rates for next generation wireless systems, yet this technological advancement is critically
undermined by the exceptional susceptibility of THz frequencies to dynamic weather induced impairments.
This study presents a comprehensive empirical evaluation of weather aware anomaly detection frameworks
for cloud-assisted THz-enabled IoT networks, comparing the performance of Isolation Forest against a Deep
Autoencoder in identifying multivariate degradation patterns caused by adverse atmospheric conditions.
Using a synthetically generated dataset comprising environmental, network communication, and blockchainbased
trust metrics, unsupervised models were trained exclusively on Clear weather operational data and
evaluated against progressively severe weather conditions including Rain, Storm, and Extreme scenarios.
The experimental results demonstrate that the Deep Autoencoder significantly outperforms the Isolation
Forest, achieving superior ROC-AUC (0.934 vs. 0.883), Average Precision (0.904 vs. 0.842), and F1-score
(0.88 vs. 0.81). Notably, reconstruction error exhibited a near fivefold increase under Extreme weather
conditions, confirming progressive network degradation as atmospheric severity intensifies. Furthermore,
the analysis reveals cascading effects whereby physical layer weather disruptions propagate upward to
degrade blockchain consensus latency and trust scores, threatening decentralized security mechanisms.
These findings establish that nonlinear representation learning offers superior sensitivity to subtle, compound
degradations inherent in weather affected THz-IoT environments, positioning the Deep Autoencoder as the
recommended framework for proactive fault diagnosis and predictive network management in future cloudassisted
IoT deployments.